Research, part of a special feature on Beyond the Assessment on the Diverse Values of Nature: Hidden gems, Biases, Frontiers, Challenges, and Insights

INTRODUCTION

Scenarios describe plausible futures, with the aim to expand our collective imagination on what types of future development could occur (exploratory scenarios) and to collectively deliberate desirable futures and pathways to reach them (normative or target-seeking scenarios; IPBES 2016). As such, scenarios are widely used in research, policy-making, and practice to assess potential future development of social-ecological systems at various scales.

Scenarios can be developed in expert-based settings, participatory settings, or a combination of both (Harrison et al. 2019a, Pedde et al. 2021, Jahel et al. 2023, Lazurko et al. 2023). Whereas expert-based scenarios tend to be designed to allow for further modeling or to be further communicated to decision-makers and wider society (e.g., O’Neill et al. 2020), participatory scenarios tend to be co-designed to enable collective thinking, facilitating social learning and developing collective understanding of social-ecological complexities (Oteros-Rozas et al. 2015, Collste et al. 2023).

Science-policy interfaces have relied heavily on scenarios to assess implications of climate change and biodiversity loss for global ecosystems and human well-being in the context of future uncertainties (O’Neill et al. 2017, Rogelj et al. 2018). The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), a global science-policy interface assessing the state of biodiversity and ecosystem services (in response to requests for evidence-based knowledge by decision-makers), is no exception and has invested substantial capacities in synthesizing available scenarios (Harrison et al. 2019b, IPBES 2019, Sitas et al. 2019, Díaz et al. 2020) and promoting advances in scenario co-design (Rosa et al. 2020).

Hitherto, scenarios typically focused on the role of indirect and direct driving forces (such as economy, demography, social-cultural change, governance, technologies, land use change, or natural resource use) in potential future development (IPBES 2018). Importantly, much less attention has been paid to the motivations and normative positions embedded in the scenarios (scenario values, for short), which (explicitly or implicitly) shape how the scenario storylines or narratives evolve. Scenario values underpin, for instance, indirect and direct driving forces, patterns, and motivators of people’s future behavior, framings, and ways in which decisions, actions, or policies are legitimized in a scenario (e.g., people in the scenario preferring a mosaic landscape to an intensively farmed one, or people in the scenarios valuing individual benefits to collective ones; Martin et al. 2022, Pascual et al. 2023). An increasing number of scenarios have embedded normative positions, such as values for nature, nature’s contributions to people (NCPs; including ecosystem services), and good quality of life in their storylines (Martin et al. 2022). Whereas the scenario literature had mostly treated scenario values implicitly, recent literature has seen an increase in their explicit inclusion in scenario storylines and logic (Rawluk et al. 2018, Harmáčková et al. 2022a, Kuiper et al. 2022, Durán et al. 2023).

IPBES has conducted multiple structured reviews of scenarios capturing potential future development of nature, nature’s contributions to people, and people’s quality of life across its assessment reports (e.g., Biggs et al. 2018, Gundimeda et al. 2018, Harrison et al. 2018, Klatt et al. 2018, Sitas et al. 2019). In particular, the IPBES Values Assessment (IPBES 2022) has synthesized scenarios with a specific emphasis on scenario values and their potential future impacts on nature, nature’s contributions to people, and good quality of life (Martin et al. 2022, Harmáčková et al. 2023, Pascual et al. 2023).

There has been general agreement in the futures literature that scenario values, as well as their causal link to specific future trajectories, are shaped by the value considerations of scenario co-designers (Slaughter 1999, Bogert et al. 2022). However, the relationship between scenario co-designer values and the scenario values is by no means direct or straightforward. A common understanding has been that whose voices, worldviews, and interpretations are included in scenario co-design (e.g., the selection of considered narratives or variables) matters, as it influences what scenario content is created. However, the values held by scenario co-designers (e.g., whether a scenario co-designer likes particular types of landscapes, or whether they prefer technological fixes to behavioral change in the face of environmental challenges) may or may not align with the values embedded in the scenarios they create. Indeed, a single group of scenario co-designers (with their individual values) usually creates multiple scenarios during a scenario process. Those scenarios express a range of scenario values, while being created by an identical group of scenario co-designers (e.g., Lehtonen et al. 2021, Pedde et al. 2021, Harmáčková et al. 2022b). Thus, it is difficult to identify a direct relationship between scenario co-designers’ values and the scenario values. This analytical distinction between scenario values and the values held by scenario co-designers is essential for this paper.

Yet, while this issue of the relationship between scenario values and values held by scenario co-designers was raised decades ago (Fowles 1977), empirical testing has been largely missing and the extent to which scenario values are a function of the kind of actors involved in scenario co-design remains unresolved (Schmid et al. 2017).^[1] This represents a key caveat, as scenarios are socially performative, i.e., they express and reinforce peoples’ framing of real-world situations that shape and influence decision- and policy-making (Oomen et al. 2022). It is thus important to understand how the scenario co-designers, with their own particular values, link to the values embedded in scenarios.

This paper aims to address this knowledge gap with a novel analysis building on the comprehensive review of scenarios in the IPBES values assessment. To this end, we advance the current knowledge on scenario values by identifying re-appearing archetypal configurations of values through a formal archetype analysis of 257 recent scenarios, and link these emerging value archetypes to the categories of actors involved in the scenario co-design, as well as to the conventional IPBES scenario families, commonly referred to as “scenario archetypes” (Hunt et al. 2012, IPBES 2016, Sitas et al. 2019).

METHODS

Scenario database, coding, and sampling

This study builds on a scenario database developed within the IPBES values assessment that catalogued values incorporated in scenarios relating to nature, nature’s contributions to people, and good quality of life (Martin et al. 2022). Here, we briefly summarize the methodology, detailed in the Data Management Report to Chapter 5 of the IPBES values assessment (Harmáčková et al. 2022c).

Scenarios and other futures works were collected through a structured keyword-based search (see Box S1) of scientific databases, complemented by snowball sampling (Harmáčková et al. 2022c). While the database compiled 460 scenarios through iterative review processes that encompassed both academic and non-academic literature (Harmáčková et al. 2022c), the present study utilized a core sample of 257 scenarios from peer-reviewed literature. Selected descriptive characteristics of the scenarios in the core sample (e.g., regional coverage, scale, scenario type, and sector of origin) are included in Table S1.A and Figure S1. The majority of scenarios originated from local and national scales. Whereas the regions of the Americas, Asia-Pacific, Europe, and Central Asia were roughly equally represented, the structured keyword-based search identified comparatively fewer studies from Africa (Figure S1).

Scenario descriptions were thematically coded for over 60 scenario characteristics (e.g., geographic focus, scenario design methodology) and elements related to scenario content (e.g., driving forces, impacts on nature, nature’s contributions to people, and good quality of life), including value attributes (different aspects of scenario values; Harmáčková et al. 2022c, 2023). Out of these, the codes relevant for this study were (1) the scenario values and (2) the categories of actors involved as scenario co-designers. Importantly, the coding process was (a) guided by a set of detailed questions related to each attribute, facilitating its thematic coding; and (b) started by iterative benchmarking among the coding team, which piloted the coding with a set of training scenarios independently coded by each member of the team. The pilot coding was subsequently compared, discussed, and iteratively repeated and re-discussed to ensure a coherent coding approach (Harmáčková et al. 2022c, 2023).

The value attributes whose presence or absence was coded (Table 1) followed the typology of values developed within the IPBES preliminary guide on values (IPBES 2015) and the IPBES values assessment (Anderson et al. 2022). Specifically, we coded the focus of the values in a given scenario, i.e., the dimensions toward which scenario values are expressed. In relation to human-nature relations, people’s values can focus on (1) nature per se (e.g., biodiversity, ecosystems, non-human beings), (2) nature’s contributions to people (i.e., the benefits provided to people by nature), and (3) good quality of life as supported by nature (see Box S2 for additional details). In addition, we coded the justification of the scenario values, i.e., the reasons why something is being valued with respect to human-nature relationships. Value justifications tend to be defined as (1) intrinsic (nature is valued for itself without reference to humans, e.g., as a motivation for nature conservation), (2) instrumental (nature is valued as a substitutable means to a human end, e.g., as a source of profit), and (3) relational (non-substitutable relationship to nature is valued, e.g., in the form of relationship to a particular animal, tree, or natural space). Although these categories may be understood as partly overlapping, the distinction between value foci and justifications provides an additional level of nuance.^[2] Multiple value attributes could apply to any given scenario.

The category of actors involved in each scenario’s development (scenario co-designers) was coded, strictly on the basis of the information provided in each scenario study and/or its supplementary materials. The coding was based on the typology of actors used in the IPBES values assessment: the involvement of (1) governments and authorities, (2) communities and citizen groups, (3) businesses and private sector, (4) households, (5) individuals (e.g., people with land-based livelihoods such as farmers, other residents, recreationists), (6) others, and (7) experts and academics involved in co-designing the scenarios (e.g., various types of professionals or researchers with expertise in different aspects of the co-designed scenarios; Table S1.B). Multiple actor categories could apply to each scenario. The sets of scenarios emerging from a single scenario process commonly differed by their value attributes, while being co-designed by an identical set of actors. Although the categorization of scenario co-designers may seem rather coarse, as some categories may conceal a much more diverse set of actors (e.g., the category of experts and academics involved in the scenario co-design may involve a high diversity of people with their unique value systems), it is important to note that scenario studies rarely provide enough information on the actors involved in scenario processes to allow for a more fine-grained categorization. The rationale for selecting this categorization was inductive-deductive, motivated partly by the need to comply with a shared actor categorization across the IPBES values assessment, partly by the motivation to use a categorization as detailed as possible, given the descriptions available in the scenario studies.

Archetype analysis

The analysis aimed to identify “value archetypes” (i.e., configurations of value attributes that re-appear as scenario values), and how these systematically link to the scenario co-designers. To this end, we employed archetype analysis to investigate “recurrent patterns of the phenomenon of interest at an intermediate level of abstraction to identify multiple models that explain the phenomenon under particular conditions” (Oberlack et al. 2019). Archetype analysis inductively identifies archetypes (here, value archetypes) as a non-disjoined classification of cases (here, scenarios), where the resulting archetypes can be (re)combined in different ways, like building blocks, to scrutinize individual cases (Eisenack et al. 2021; see Technical Annex). Thus, archetype analysis is not meant to find a single pattern that fits every scenario but multiple patterns that endorse differences among scenarios. In other words, a single scenario can be covered by anything between zero, one, and all of the identified value archetypes, while the aim is to define a set of archetypes that covers as much of the overall scenario sample as possible.

In our case, we aimed to identify a suite of several value archetypes. Each of the value archetypes was characterized by three elements: (1) a configuration of several value attributes; (2) a set of scenarios covered by this archetype (i.e., all scenarios that are characterized by the respective configuration of value attributes); and (3) an explanation of why this configuration is reasonable, ideally supported by more details from the scenarios where it holds (cf. Eisenack et al. 2019). Since the coded data were binary (i.e., the presence or absence of value attributes and scenario co-designer categories), elements (1) and (2) can be described with set theory (see Technical Annex). For our data, each of the 257 scenarios was characterized by which value attributes were present (none, one, or multiple, from Table 1), and which actor categories were involved as scenario co-designers (none, one, or multiple). The archetype analysis itself was conducted by using only the value attributes, based on formal concept analysis (FCA; Ganter et al. 2005), which was previously applied in other archetype analyses (Oberlack et al. 2016, Oberlack and Eisenack 2018, Wang et al. 2019, Gotgelf et al. 2020). Importantly, FCA can cope well with the possibility that scenarios can be covered by multiple archetypes. Consequently, archetypes do not need to be completely different from each other in order to make them capable of representing different yet related nuances in the data.

An important step in the archetype analysis is to determine the optimal number of archetypes, i.e., the optimal size of the archetype suite. This task is not straightforward. For instance, if every scenario would count as an individual archetype, these archetypes would perfectly represent the diversity of the sample, but the analysis would not reduce any complexity (“idiographic trap”). On the contrary, admitting only a single archetype covering all the scenarios in the sample would not endorse differences among scenarios (“nomothetic trap”). Rigorous criteria for choosing the right suite size is thus acknowledged as a key desideratum in the archetype literature (Eisenack et al. 2019).

In this paper, we propose a novel way to deal with this challenge. In order to obtain an optimal suite of archetypes, we extend the proposal by Partelow et al. (2024) (see Technical Annex). The method consists of three steps, the first two are algorithmic and address elements (1) and (2) above, in order to maximize the number of scenarios covered by the suite of value archetypes. Technically speaking, the algorithm to compute optimal configurations solves a maximization problem with set operations in the maximand and the constraint. Each archetype in the optimal suite is described by a number of value attributes it includes. We call this number the richness ρ of an archetype. For instance, archetypes described by two attributes (ρ = 2) only allow for a simple description, whereas three or more attributes facilitate characterizing the archetype in a richer way. The first two steps of the method nevertheless compute several optimal suites (e.g., depending on the richness we require). This gives us a limited amount of possibilities to choose from in a qualitative way, in order to address element (3), the explanation of configurations. We only retained an optimal suite if it was backed up by robust qualitative rationale.

Comparing archetypes against scenario co-designers

Once the optimal suite of value archetypes was obtained, we tested whether they are systematically linked to the actor categories involved as scenario co-designers. We also tested whether the archetypes resemble or are different from established scenario families known as “scenario archetypes” in the IPBES community (IPBES 2016, Harrison et al. 2019b, Sitas et al. 2019). In order to do so, we used the lift metric, commonly applied in association rules mining (e.g., McNicholas et al. 2008). The observed frequency, e.g., of scenarios covered by a certain archetype and co-created with local citizens, is normalized to the frequency of local citizens being involved in the full data set, and normalized to the frequency that the certain archetype is present in the full data set (see Technical Annex for the formal definition). A lift of 1 means that features co-occur exactly as frequently as expected under the assumption of stochastic independence (McNicholas et al. 2008). A value above 1 means that they co-occur more frequently than expected; conversely, a lift value below 1 signifies that the co-occurrence is less frequent than expected under the assumption of stochastic independence.

We also calculated the lift for each pair of conventional IPBES scenario archetypes and the newly identified value archetypes.

RESULTS

The identified value archetypes represented configurations of value attributes from Table 1. Of the 257 coded scenarios, 254 were characterized by at least three value attributes (out of the ten possible value attributes). With respect to the value justification, instrumental values (INS) were covered in 97% of the scenarios, intrinsic values (INT) in 25%, and relational values (REL) in 26%. In terms of the value focus, 88% of the scenarios included a focus on material values of nature’s contributions to people (MAT), 61% regulating values (REG), and 44% non-material values (NMT). A share of 39% of the scenarios included a focus on the value of nature per se (NAT). Scenarios also focused on the values of nature as related to individual aspects of good quality of life (IND) in 81% of the scenarios, 53% on societal aspects (SOC), and 25% on cultural aspects (CUL).

The frequencies of actor categories as scenario co-designers were: governments (39% of scenarios), communities (37%), businesses and private sector (26%), households (4.3%), individuals (47%), others (7.0%), and experts and academics involved in the scenario co-design (32%) (Figure S1). The actor frequencies add up to more than 100% as multiple actors can participate in a single scenario co-design process.

Some of the value attributes (in particular INS, MAT, IND) were highly frequent in the scenario sample. Should they be considered individually, they would not prove very helpful for telling apart the values embedded in the scenarios. This provided a strong rationale for identifying the re-appearing (recurrent) configurations of multiple value attributes characterizing the scenario values in the sample through an archetype analysis.

Identifying the optimal suite of value archetypes

Before identifying the final suite of value archetypes covering the reviewed scenarios, a vital intermediate step was to identify the optimal number of archetypes to cover our data sample.

The vast majority of scenarios (99%) were characterized by at least three value attributes (see above). Thus, we required value archetypes comprising exactly three value attributes (richness ρ = 3; see Table 2 and Technical Annex for further justification of this richness). For richness ρ = 3, a very good coverage was gained by a suite of five archetypes. This specific suite was optimal, as given the richness ρ = 3, no other selection of five configurations of value attributes was able to reach a higher coverage of scenarios. As usual for such an analysis, no single archetype covered all scenarios, and the archetypes were not mutually exclusive. The only scenarios not covered by any of the identified archetypes were the 3 scenarios for which only two value attributes were coded (and thus could not be covered by archetypes with richness ρ = 3), and 11 scenarios with extremely untypical value attribute configurations. As the following sections show, the resulting suite of archetypes could be well rationalized by closely inspecting the scenarios covered by the archetypes.

Additional levels of richness and numbers of archetypes in a suite were tested, but they did not provide better insights (see Technical Annex). Furthermore, we undertook validity checks in line with the quality criteria suggested by Eisenack et al. (2019), in particular with respect to external validity (see Piemontese et al. 2022). For this purpose, we used a larger pool of 437 coded scenarios, including the IPBES review of gray literature and science-policy reports (Harmáčková et al. 2022c, 2023, Martin et al. 2022, Yoshida et al. 2024). The checks (see Technical Annex) basically confirmed our results. The checks also confirmed the relations between scenario co-designing actor categories and values archetypes (see below) qualitatively. Finally, we exposed the optimal suite, as determined by the algorithm, to additional scrutiny in a qualitative way (step 3), i.e., whether the archetypes could be rationalized by the qualitative descriptions of the coded scenarios (see below). These measures ensure that the resulting selection of value configurations can indeed be considered a novel suite of value archetypes.

The resulting five values archetypes represented recurrent value configurations characterizing the analyzed future scenarios (Table 2). They showed that most of the variation in the data (with ten values attributes) could be represented by only five archetypes. As such, the identified value archetypes sorted nearly all coded IPBES scenarios into five overlapping classes, and represented frequent configurations of values attributes that play out in the scenarios’ storylines. The full list of n = 275 scenarios included in the archetype analysis together with the archetype(s) covering each of them is available in Table S1.A.

Value archetype “Individualist”: valuing nature for material benefits to individual well-being

Value archetype “Individualist” values nature for material benefits to individual well-being and covers scenarios typically assuming that as long as supply, demand, prices, trade, and competition are left free of government regulation, the pursuit of material self-interest will maximize the wealth of a society through profit-driven production of goods and services (de Bruin et al. 2017). These scenarios generally tend to prioritize profit generation from more or less sustainable economic activities. Furthermore, these scenarios frequently assume that policies on multiple scales (from local to global) will not undergo a substantial revision or paradigm shift compared to the current state (e.g., Ward et al. 2018). The scenario values in this value archetype tend to be characterized by self-centered attitudes or even greed (Boschetti et al. 2015, Kohler et al. 2017, Ward et al. 2018). However, in the case of some scenarios, the sustainability of economic activities, harvests, and their environmental and social impacts gradually gain more attention, although nature conservation tends to be weak (Farrell and Silva-Macher 2017). In terms of the impact on nature and people, scenarios covered by this archetype tend to assume negative impacts on ecosystems, biodiversity, and natural resources (Eide 2008, Koo et al. 2019), increased migration including rural abandonment, and negative impacts on equality (Kostakis et al. 2016), although in some of the scenarios covered by the “Individualist” value archetype, economic activities are regulated and profits redistributed in the societies (Farrell and Silva-Macher 2017, Martínez-Sastre et al. 2017). Finally, this archetype also holds in some breakdown scenarios, in which weakening regulation leads to disorder, dissolution of governments, over-exploitation of resources, and breakdown of the social order (Brown et al. 2016).

Value archetype “Conservationist”: valuing nature for regulating benefits to individual well-being

Value archetype “Conservationist” values nature for regulating benefits to individual well-being. It is similar to “Individualist” in that people value nature for the benefits it provides them, and for enhancing their individual well-being. The difference is that in the scenarios covered by this value archetype, scenario values more strongly prioritize nature’s contributions, such as climate regulation, air quality regulation, or water quality/quantity regulation. For instance, scenarios depict people who appreciate the cooling effects of green and blue areas in cities, providing local climate regulation and ensuring the flow of clean air, resulting in air quality regulation (Larondelle et al. 2016). In turn, these scenarios often value human health, recreational activities, and aesthetic appreciation (Larondelle et al. 2016), as well as revenues from ecotourism (May et al. 2019). In some scenarios, the emphasis on the regulating benefits of nature’s contributions to people tended to be driven and regulated top-down, e.g., by governments (May et al. 2019).

Value archetype “Planned conservation”: valuing nature for using its material and regulatory benefits

Value archetype “Planned conservation” was based on values for material and regulating benefits and their use by people. Unlike the previous two archetypes, this use is not necessarily linked to benefiting individual well-being, but can also contribute to other aspects of well-being, including societal and cultural. Scenarios covered by this archetype frequently include integrated land use planning and management, leading to sustaining and restoring material and regulating nature’s contributions to people, such as food provision and water quality and quantity regulation (Arunyawat and Shrestha 2018). Scenarios covered by “Planned conservation” frequently resemble business-as-usual, differing in the extent to which market forces are regulated by policy and planning interventions, while trying to establish a balance between material and regulating nature’s contributions (Chatterton et al. 2015, Albert et al. 2016). The scenarios covered solely by this archetype were primarily developed in collaboration between experts/academics and individuals such as farmers or local residents.

Value archetype “Fixing for society”: valuing nature for regulating benefits to societal well-being

Most of these scenarios portrayed moderate conservation efforts, not very different from the status quo measures, led by local government agencies (Thorne et al. 2015, Santos et al. 2017). The means of conservation were primarily technological or engineering fixes (Thorne et al. 2015, Santos et al. 2017), followed by regulatory or pricing structures (Bartolai et al. 2015). Although few scenarios are covered solely by this archetype, it illustrates a key contrast to the value archetype “Conservationist,” which prioritized regulating benefits of nature’s contributions to people from a purely individual perspective, as opposed to a societal one in this value archetype. The scenarios covered solely by this archetype were primarily developed in collaboration between experts/academics, representatives of governments and authorities, and individuals such as farmers or local residents.

Value archetype “Pristine ideals”: valuing nature for itself and its benefits to societal well-being

These scenarios emphasize conservation and protection of nature with a focus on its intrinsic value. Many scenarios focus on the restoration of native species or ecosystems (Mitchell et al. 2016, Bremer et al. 2018, Burnett et al. 2019), such as the establishment of a protected area (Davies et al. 2015, Chu et al. 2018) or general approach of leaving nature alone (de Bruin et al. 2017). Some included human intervention to upkeep the ecosystem, which cannot persist on its own (Mitchell et al. 2016, Burnett et al. 2019). Scenarios covered solely by this value archetype were typically developed by a broad range of different types of societal actors, including governments, business, and private sector and communities.

Combinations of archetypes

Whereas some scenarios were covered by only a single value archetype, the archetype analysis also allows for a single scenario to be covered by multiple value archetypes (Table 3). In our sample, the combination of value archetypes “Individualist,” “Conservationist,” and “Planned conservation” was the most frequent.

The combination of value archetypes “Individualist,” “Conservationist,” and “Planned conservation” spanned a gradient ranging from business-as-usual scenarios, assuming that current policy and economic trends will continue without radical shifts (Gilbert et al. 2015, Harmáčková and Vačkář 2015, Zhang et al. 2017), to Economic Optimism or policy reform scenarios, assuming that society will continue focusing on economic growth and technological development, differing based on whether they assume more or less regulated markets (Carpenter et al. 2015, Hermanns et al. 2017, Qiu et al. 2018, Sandström et al. 2020). In addition, several scenarios combining these three archetypes fell into the category of breakdown scenarios (Tejada et al. 2016, Kubiszewski et al. 2017, Zhang et al. 2017).

Several scenarios were covered by all five value archetypes. Many of these focused on nature protection, conservation, and restoration (Bottalico et al. 2016, Farrell and Silva-Macher 2017, Wang et al. 2018, Sharma et al. 2019), sustainable agricultural intensification (Kindu et al. 2018, Sharma et al. 2019), and development beneficial to local communities (Carlsson et al. 2015, Mavrommati et al. 2016, May et al. 2019, Sandström et al. 2020). In addition, these scenarios were characterized as sustainable and often described as “win-win” or “desirable” by their authors (Beach and Clark 2015, Brown et al. 2016, Tejada et al. 2016, Wolff et al. 2018).

Although the frequency of value archetypes across scenarios from different geographic regions and scales did not substantially vary, there were several outstanding patterns (Table S2 and S3). For instance, the frequency of the archetypes “Fixing for society” and “Pristine ideals” was slightly higher in scenarios from the Americas and slightly lower in scenarios from Africa and the Asia-Pacific region; the value archetype “Planned conservation” was slightly more represented in scenarios from Europe and Central Asia (Table S2). In terms of spatial scale of the scenarios, scenarios focusing on the local scale had a stronger representation of the value archetype “Pristine ideals” and a weaker representation of the value archetypes “Individualist” and “Planned conservation” (Table S3).

Value archetypes and the scenario co-designers

The next key step of the analysis was to assess how the participation of certain categories of scenario co-designers was associated with the occurrence of the value archetypes. For this purpose, Figure 1 depicts the frequency count and lift of each possible combination of actor category and value archetype in the data.

Some scenario co-designer categories and value archetypes co-occurred more (or less) frequently simply because some types of actors generally appear more (less, respectively) often in the scenario co-designing processes, thus were more (or less) represented across the whole scenario sample. This is indicated by the lift metric equal to 1. In many cells the lift is different from 1, i.e., from the expectation under stochastic independence (Fig. 1). This indicates that the value archetypes and the involved scenario co-designers are not completely independent of each other, underlining the need to understand this relation in a more nuanced manner.

In terms of lift, expert/academia-based scenario co-designers pervasively appeared in all studies that produced scenarios covered by value archetypes “Individualist,” “Conservationist,” “Planned conservation,” and “Fixing for nature.” In the vast majority of cases, the scenario studies do not provide sufficient detail to disaggregate the description of the experts/academics involved. Scenario co-designers from business and private sector participated more pervasively in the “Individualist” scenarios, but also in the generation of scenarios covered by the value archetype “Pristine ideals.” The latter, however, more frequently in collaboration with governments and citizen groups, covered a wide range of businesses from agricultural (Dupont et al. 2016) to industry (Sandhu et al. 2018) and tourism, forestry and mining-oriented companies (Harmáčková and Vačkář 2015, Brown et al. 2016, de Bruin et al. 2017, Sandström et al. 2020). This value archetype involved a more diverse sets of stakeholders as scenario co-designers. Individuals do not seem to be particularly associated with specific value archetypes. The “Fixing for society” value archetype was more pervasively associated with experts and academics involved in the scenario co-design.

Interestingly, scenarios covered by all value archetypes at the same time were quite frequently developed solely by scenario co-designing experts and academics (e.g., Law et al. 2015, Bottalico et al. 2016, Kindu et al. 2018, Wolff et al. 2018, Sharma et al. 2019). In the cases that other actor categories were involved as scenario co-designers, these were various mixes of actors, ranging from governments and authorities to businesses, communities, and individuals.

DISCUSSION

What can be learned through the archetype analysis of scenario values?

In contrast to past analyses that examined value-based clusters of scenarios or frequencies of individual value attributes (e.g., Harrison et al. 2018, Harmáčková et al. 2023), this study examined scenarios through the lens of different and partially overlapping configurations of value attributes. The use of archetype analysis is suited to cover the diversity of scenario values, as the approach does not require that each scenario is characterized by only a single archetype. The approach thus facilitates a more fine-grained perspective of the scenarios and their underlying values. In particular, the inductive nature of archetype analysis admits to cover configurations of value attributes (e.g., those related to breakdown scenarios) that are less frequently chosen by scenario co-designers, while also providing more detailed insights into more common configurations of value attributes (such as those related to instrumental value justification), through highlighting how they appear in a variety of nuances (e.g., in different configurations with a material, regulating or individual value foci). The identified set of value archetypes covers scenarios originating from the majority of global regions (with a slightly lower coverage for Africa), and reflects well the variability among scenarios from different local contexts (Figure S1). Thus, the resulting suite of value archetypes covers a rich sample of scenarios, while admitting to “zoom into” common value patterns.

The identified value archetypes hence reflect and advance discussions in the current scenario literature, e.g., the commonly recognized types of values. For instance, the value archetype “Individualist,” prioritizing instrumental values of nature while focusing on nature’s material contributions, is a value configuration typically featured in business-as-usual scenarios (Bogert et al. 2022). However, the archetype analysis has newly revealed other value configurations characterizing business-as-usual scenarios, such as the value archetype “Conservationist,” which places emphasis on regulating contributions of nature (REG), individual aspects of well-being, and instrumental valuing of nature. Thus, the identified value archetypes reduced complexity of the data while simultaneously highlighting ranges of value attributes and configurations not previously perceived as relevant in future scenarios.

In light of these reflections, the identified value archetypes strive to supplement future scenario development processes by providing a motivation to explore more complex combinations and patterns of value attributes than in the status quo scenario practice (e.g., Durán et al. 2023), thus improving the credibility of the developed scenarios. Though not aiming to become a classification system for future scenarios (as in the case of the conventional scenario archetypes, e.g., Economic Optimism, Global Sustainable Development; IPBES 2016), the developed value archetypes aim to provide a potential entry point for including diverse value combinations in future scenarios.

Value archetypes and scenario co-designers: implications for future scenario processes

As expected, the analysis showed that there is no perfect one-to-one relationship between the categories of scenario co-designers and scenario values. This can be attributed to the common way of co-designing scenarios, and arguably to the values held by scenario co-designers. With this in mind, the analysis confirms several assumptions prevalent in the scenario literature, while qualifying others.

First, experts and academics tend to be more frequently (than expected under the assumption of stochastic independence) involved in the design of scenarios that value nature for material benefits to individual well-being (value archetype “Individualist”), while exactly such values are commonly reflected as less likely to lead to sustainable and just futures in the peer-reviewed literature (Yoshida et al. 2022, Pascual et al. 2023). On the other hand, governmental and community actors more frequently (than expected) contributed to the design of scenarios valuing nature for itself and its benefits to societal well-being (value archetypes “Conservationist” and “Pristine ideals”). This became even more apparent when expanding the analyzed dataset to also include non-academic future visions and pathways (see Technical Annex; cf. Vainio et al. 2019).

Scenarios covered by the value archetype “Pristine ideals” were the least represented, showing that considering nature in terms of its intrinsic value and its benefits to societal aspects of well-being is currently underrepresented in the scenario literature. This also contrasts with the current literature illustrating that these types of values are crucial for sustainable and just outcomes (Vervoort et al. 2015, Klain et al. 2017, Neuteleers et al. 2021). Scenarios covered by the “Pristine ideals” value archetype were primarily created in studies co-designed by a more diverse set of stakeholders (see also Technical Annex, section on validation). This may indicate that actor categories outside academia can be more open to exploring non-conventional value configurations and may bring in a stronger emphasis on appreciating nature’s role in societal aspects of well-being. Because such values seem to be strongly associated with sustainable and just outcomes (Harmáčková et al. 2023), these results provide an additional strong justification for involving diverse portfolios of actors and stakeholders in scenario design processes (Aguiar et al. 2020).

In summary, the results provide valuable insights for diversity and inclusivity in scenario co-design, resonating with previous calls (Saito 2017, McElwee et al. 2020, Harmáčková et al. 2022a, Maraud and Roturier 2023, Muiderman et al. 2023). They further illustrate that stakeholder diversity in scenario co-design processes may not utilize its full potential, e.g., if the process operates within the value framings and configurations predefined by academic actors. Instead, more emphasis needs to be put on co-designing scenario values from the bottom up, in order not to fall into conventional value configurations in an unreflected way (Le Heron et al. 2016). The value archetypes can be used to purposefully create scenarios that leave the beaten path of conventionally considered value patterns.

Value archetypes and the IPBES scenario archetypes: implications for future IPBES assessments

IPBES commonly relies on deductive heuristic to divide assessed scenarios into pre-existing categories, denoted as scenario archetypes (IPBES 2016). These “conventional” scenario archetypes have been used for scenario synthesis across multiple IPBES science-policy assessment reports (Sitas et al. 2019). In contrast, the value archetypes in this study were derived inductively from the source data, and thus can be used to generate a new perspective on the conventional scenario archetypes. In this respect, this study strives to highlight how these two approaches to archetype thinking match and what complementary strengths they have. For the purposes of this discussion, we compare the identified value archetypes with the conventional scenario archetypes of Economic Optimism, Business as Usual, Regional Competition, Inequality, Breakdown, Regional Sustainability, and Global Sustainable Development (Harrison et al. 2018, 2019b). To this end, Figure 2 determines the co-occurrence frequency count and lift of conventional and value archetypes in the coded data.

In particular, Figure 2 highlights that the Breakdown, Inequality, and Regional Competition scenarios are developed less often compared to other types of scenarios. The remaining conventional archetypes (Business as Usual, Economic Optimism, Global Sustainable Development, and Regional Sustainability) and the newly identified value archetypes do not show any clear one-to-one relation, which may hint that from the perspective of values, the conventionally used sets of scenario archetypes may not provide a sufficiently fine-grained heuristic to characterize currently available sets of future scenarios (Pedde et al. 2019, Sitas et al. 2019). In particular, the analysis illustrates that while sustainable outcomes are conventionally associated with only two sets of value-related assumptions (those of Regional Sustainability and Global Sustainable Development), the newly identified value archetypes show that the underlying values tend to be differentiated among four different value configurations (value archetypes “Conservationist,” “Planned conservation,” “Fixing for society,” and “Pristine ideals”), which emphasize different value foci and value justifications. This analysis thus illustrates that aggregating scenarios into conventional scenario archetypes may result in homogenization, which threatens to lose important value texture (Hunt et al. 2012), or might question the legitimacy of the resulting aggregated scenario groups for decision-making.

The study illustrates that future assessment processes within IPBES and other science-policy processes should strive for a more contextualized approach to synthesizing and sense-making around values in future scenarios. Although the conventional IPBES scenario archetypes have been well received by policy- and decision-makers as intuitive and operational tools for futures thinking (Sitas et al. 2019), this analysis shows that their interpretation may be misleading, and that they should ideally be combined with more bottom-up, inductive approaches to scenario synthesis.

CONCLUSIONS

This study contributes to the current sustainability literature by going beyond previous syntheses of values in future scenarios and providing fine-grained insights into the patterns of scenario values, building on a nuanced inductive approach. In addition, it contributes to bridging the knowledge gap related to the link between scenario values and the values held by scenario co-designers.

We find that the diversity of value foci and value justifications in most scenarios (which come in quite different configurations) can be well covered by five newly determined value archetypes. Based on a rigorous and inductive approach, they provide a novel view on how, for instance, instrumental value justifications are combined in different ways with a material, regulating, or individual value focus. The archetype analysis revealed that these nuances are quite common in currently published scenario studies, whereas scenarios with intrinsic value justification are not so frequent and not so diversified.

Furthermore, the analysis provides novel insights in the association between value archetypes, scenario co-designers, and commonly used scenario archetypes. In particular, we find a strong role of experts and academics in the scenarios with instrumental justification. Similarly, the analysis shows that conventional scenario archetypes may conceal more diverse configurations of value attributes. Specifically, it appears that the new value archetypes facilitate deeper examination of multiple facets within the conventional Global Sustainable Development and Regional Sustainability scenario archetypes.

From a methodological perspective, this study tested the use of a novel method for selecting the optimal suite of archetypes, successfully addressing a well-known gap in the general archetype literature (cf. Oberlack et al. 2019). This approach considerably simplified the justification of the suite of archetypes. Interestingly, the optimal configurations of value attributes, determined in this way, turned out to be systematically related to the actor categories, even without considering these in the primary analysis. This indicates that archetype selection methods might be further improved by directly considering their explanatory power. Obviously, the method would require considerable extension should it be applied to non-binary data; however, this is not necessary in studies where research questions can be examined through binary data, such as in this case.

Our study thus offers new perspectives to enrich future scenario co-design processes by embedding more diverse value configurations, whether within the IPBES context or beyond. It also offers new ways to research the relation between the values held by scenario co-designers, and the values embedded in scenarios. Though not aiming to become a standard classification system for future scenarios, the novel value archetypes offer insightful nuances to complement the conventional scenario archetypes. Most importantly, value archetypes contribute a further step to ultimately achieve one of the very promises of scenario co-design: the representation of multiple futures that are diverse both in the values embedded in scenarios and the people involved in their creation.

__________

^[1] Importantly, in typical participatory scenario-building processes, actors are involved in the co-design of an entire set of multiple different scenarios. Usually, the scenario co-designers do not state a preference for any of the scenarios and the values embedded in them. Thus, the association between scenario co-designers and the values embedded in scenarios should not be understood in terms of the co-designers imposing their preferences for particular values in scenarios, but rather in terms of the scenarios mirroring the way how scenario co-designers think about the link between values, specific decisions and actions, and future outcomes.
^[2] For instance, the value focus on “nature” and the “intrinsic” justification for valuing nature may appear as equivalent. However, this is not entirely the case: “nature’s” value can be justified by the existence of nature as such (“intrinsic” value), but also by unique relationships between specific natural elements (organisms, specific ecosystems, and natural places), such as pets, familiar animals, or sacred groves (“relational” values).

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