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Metzger, M. J., M. D. A. Rounsevell, H. Van den Heiligenberg, M. Pérez-Soba, and P. Soto Hardiman. 2010. How personal judgment influences scenario development: an example for future rural development in Europe. Ecology and Society 15(2): 5. [online] URL: http://www.ecologyandsociety.org/vol15/iss2/art5/
Insight, part of Special Feature on Landscape Scenarios and Multifunctionality – Making Land Use Assessment Operational How Personal Judgment Influences Scenario Development: an Example for Future Rural Development in Europe
1Centre for the Study of Environmental Change and Sustainability (CECS), University of Edinburgh, 2Alterra Wageningen University and Research Centre, 3Centre for the study of Environmental Change and Sustainability (CECS), University of Edinburgh, 4Netherlands Environmental Assessment Agency (PBL), 5Province of Utrecht, 6Grupo Alba
Scenarios of alternative plausible futures have been used extensively to explore the potential effects of socioeconomic and environmental change. The ultimate objective of any explorative scenario exercise is to assess the variation in possible futures to provide insights into the range of potential outcomes. These results provide stakeholders with guidance for policy development, planning, and management. We explore how personal judgment can influence scenario development. Scenarios for the future of European rural regions are used to explore alternative outcomes under a public interventionist future and a market liberalization oriented future. A transparent qualitative framework is used to identify differences in outcomes based on personal judgment. Results show that, for both scenarios, there are plausible mechanisms that can lead to similar positive or negative outcomes. Choosing a single process per scenario, based on personal judgment and interpretation, can therefore greatly influence scenario outcomes and limit the range of uncertainty that is covered by the scenarios. The exercise shows the importance of making these judgments explicit in scenario development, especially when exploring broad consequences of alternative policy directions that may be based in political worldviews.
Key words: Common Agricultural Policy reform; explorative scenarios; personal judgment; rural development; uncertainty
The main socioeconomic drivers of environmental change are global in scope and are inherently unpredictable (MA 2005, IPCC 2007). Scenarios that provide alternative images of how the future may unfold can act as an integration tool in assessing the effects of future environmental and social change (Zurek and Henrichs 2007, Rounsevell and Metzger 2010). Although we cannot attach a probability of occurrence to any given scenario, scenarios help to stimulate open discussion in the policy arena about potential futures (Van der Heijden 2005). Over the last decade, a large number of studies have developed scenarios to explore potential future changes at different geographical scales. Rashkin (2005) gives an overview of recent global exercises, including the climate change scenarios of the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (Nakićenović et al. 2000) and the Millennium Ecosystem Assessment (MA 2005). At the subglobal scale, many scenarios studies have been conducted for the European Union (EU). We compare the objectives and characteristics of some of these scenarios (Table 1).
Despite the increased use of scenarios in global change science, the associated terminology remains loosely defined. We have therefore included a list of definitions of the most important terminology used (Table 2).
Although scenario studies have been used for different objectives, there is considerable overlap in the conceptual frameworks adopted to structure these scenarios (i.e., scenario logic) and the models applied to quantify the scenario outcomes. For example, Busch (2006) grouped 24 scenarios from six global and European studies under just four broad labels: global markets, global society, continental barriers, and regional sustainability. In addition, several studies share a common pedigree. The PRELUDE and ALARM scenarios stem from the ATEAM methods (Ewert et al. 2005, Rounsevell et al. 2005, 2006), and the CLUE model (Verburg et al. 2008, 2010) has been used in both SENSOR and the EUruralis to provide the spatial disaggregation of land use change (Table 1).
There are also signs of common reasoning in storyline interpretation to assess the scenario outcomes among European scenario studies. For example, both ATEAM (Schröter et al. 2005, Rounsevell et al. 2006) and EUruralis (WUR/MNP 2008) project significant land abandonment in unfavorable production regions when trade barriers and agricultural subsidies are abolished. Although such a future is consistent with recent observations, e.g., in European mountain regions (MacDonald et al. 2000), reductions in subsidies need not necessarily lead to wide-scale abandonment. In New Zealand, for example, radical policy reform in 1984 led to the complete abolishment of agricultural subsidies. After an initial difficult period, the sector managed to redevelop through both diversification and the reduction of inputs in marginal regions, with the result that little land was abandoned (Smith and Montgomery 2004, MacLeod and Moller 2006).
The objective of any explorative scenario exercise is to assess the variation in possible futures to provide insights into the range of potential outcomes (Alcamo 2001, Van der Heijden 2005, Zurek and Henrichs 2007, Rounsevell and Metzger 2010). These results provide stakeholders with guidance for policy development, planning, and management. Generally speaking, the scenario logic is used to ensure sufficient coverage of variation in future developments. However, outcomes are also influenced by differences in storyline interpretation and quantitative modeling assumptions. Variation in outcomes is reduced by common paradigms, as illustrated for land abandonment, whereas alternative modeling methods can enhance variation. Busch (2006) has shown that, for similar narrative scenarios, global models have contrasting outcomes for Europe compared with European-scale studies.
The convergence of European scenarios is an undesirable development because it ultimately limits the range of possible outcomes that can be explored and may not reflect the full diversity of plausible futures. This is especially important when investigating alternative policy directions, each of which can have multiple outcomes depending on the uptake or success of the policy. For example, this is the case for rural regions, given likely changes in policies for rural development, including the Common Agricultural Policy (CAP). The consequences for people living and working in rural regions depend on complex and dynamic processes that are determined by a range of driving forces, including demographic, socioeconomic, technological, and environmental change. This implies that for rural scenario studies, it is important to interpret the future outcomes of alternative policy directions, which rely heavily on personal judgment. Set paradigms must be avoided and personal judgments of the outcomes of different policy directions should be made explicit to provide stakeholders with transparent information to evaluate suitable policy options (De Vries and Petersen 2009).
Here, we present a novel concept in scenario development to explicitly address and analyze judgment in scenario development. Using a set of scenarios for the future of European rural regions as an example, we explore how alternative worldviews influence the interpretation of scenario outcomes. Although our study is based on qualitative reasoning, we discuss the implications of this approach for quantitative modeling.
Developing scenarios for rural Europe
As part of the Foresight Analysis of Rural Areas Of Europe (FARO-EU) project for the European Commission, we developed contrasting scenarios for the future of European rural regions. The development involved two stakeholder workshops with approximately 15 EU policy makers from Directorate General (DG) Agriculture and DG Regio and a few national policy makers seconded to Brussels. During the first workshop we asked the policy makers to individually score the importance of a range of drivers and policy issues that could affect rural regions. The outcomes of this consultation were then discussed in plenary. In addition, we presented the policy makers with a number of scenarios and asked them to discuss each scenario's relevance for the FARO-EU project (Table 3). During a second workshop we presented the policy makers with initial results, and asked for specific feedback and comments, which were used to improve the scenarios.
Although business management textbooks discuss scenario methods (e.g., Van der Heijden 2005), explicit descriptions of the methods are surprisingly rare in the scientific literature and can lead to confusion, especially when concepts are also poorly defined. We summarize the scenario development as following five stages (Fig. 1) that are consistent with the terms defined by us (Table 2).
Stage 1 was to define the focal question and spatial boundaries. These were defined by the project aims: to explore the future of European rural regions, focusing specifically on working and living conditions. The time frame of the project was determined after the first policy workshop. Although the policy makers thought that climate change could have serious effects in rural regions, they felt that a time frame greater than 25 years would have limited policy relevance. It was therefore decided to focus on 2030. We then used the results from the policy workshop to construct a list of relevant drivers, which was Stage 2. Relevant drivers were identified for the society, technology, economy, environment and policy/governance (STEEP) categories, but we decided to exclude environment and society drivers. Although these categories could influence working and living conditions in rural regions, we see them as scenario outcomes, rather than drivers that can be described in the scenario assumptions.
The stakeholders identified the future of the CAP after 2013 as one of the most important and uncertain factors influencing future working and living conditions in European rural areas. However, the project did not envisage the ex ante evaluation of specific policy reforms (cf. Van Ittersum et al. 2008), but rather explored the consequences of broad policy directions. In Stage 3, we therefore structured our scenarios on two different worldviews. The first is a world based on a strong belief that the public sector must intervene to solve social, economic, and environmental problems to achieve social, economic, and territorial equity and environmental protection. This we label the Muskateers ‘all for one and one for all’ scenario. The second world vision is a world in which there is a strong belief that market liberalization will achieve solutions to social, economic, and environmental problems by strengthening competitiveness in the global economy. This we label Marketeer scenario.
In Stage 4, the narrative storylines for the Muskateer and Marketeer scenario assumptions were developed for the future trends in policy, governance, economy, and technology (Table 3). These storylines were then used to explore the trends of the important drivers affecting change (Table 4; cf. Nakićenović et al. 2000, Rounsevell et al. 2006).
Following traditional scenario methods (Rounsevell et al. 2006, Zurek and Henrichs 2007), the trends in the drivers (Table 4) are used in Stage 5 to make projections for specific indicators related to rural development, either qualitatively (cf. MA 2005) or using quantitative numerical models (cf. Rounsevell et al. 2006). However, it is difficult to define consistent rules to assess the consequences of alternative narratives objectively without reflecting political ideologies or personal beliefs. For example, as illustrated in the introduction, there are alternative plausible hypotheses about the consequences of abolishing agricultural subsidies. Similar contrasting outcomes can be devised for employment, social cohesion, and the environment, all based on valid but contrasting underlying assumptions imbedded in personal beliefs or worldviews.
These discussions led us to construct a framework for making explicit personal judgments of scenario implications, allowing us to explore its potential influence on future outcomes.
Assessing personal judgment in scenario outcomes
A consistent analysis of personal judgment in scenario outcomes (Stage 5 in Fig. 1) becomes possible when the scenario assumptions are interpreted from contrasting perspectives. A supporter of the worldview has high expectations of positive outcomes, which we refer to as a high-expectation world (HEW). Policies, societal trends, and markets work out as expected and the social values of its supporters are not compromised. A detractor of the worldview believes that the assumed positive outcomes will not be realized, resulting in a low-expectation world (LEW). The societal and economic developments, which are expected by the supporter, do not work out. Furthermore, there are “forgotten” trade-offs of a HEW, which compromise the detractor’s social values. For example, the pursuit of economic growth may lead to environmental degradation, which may not be of great concern to a Marketeer but be perceived as a negative trade-off by others.
Although a HEW and LEW interpretation of the Muskateer and Marketeer scenario principles could be achieved through stakeholder consultation, we made our interpretation based on the insights we obtained in the two policy workshops and our expert knowledge. We illustrate the extent to which personal judgment could influence scenario outcomes (Table 5). For example, in a Muskateer HEW, strong governments lead to desired levels of public services in rural regions, whereas in a LEW, bureaucracy overwhelms efficient government and regions exploit incoherent policies to their own benefit. In the Marketeer HEW a lean government allows a regional governance structure to emerge to support public service provision on a demand basis, whereas under a LEW, peripheral and poor regions are disadvantaged and have little political influence, resulting in inadequate service provision. The complete results of this qualitative assessment are illustrated for policy, governance, economic, technology, societal, and environmental outcomes (Table 5).
The difference between HEW and LEW outcomes defines the judgment-related variation for each scenario, which we term the belief range. When traditional scenarios have single outcomes that are easily compared, belief ranges can show partial or complete overlap (Fig. 2). Although this may seem complex compared to traditional explorative storylines with just one interpretation for each scenario, we argue that such results reflect political reality on discussions of policy direction outcomes. Furthermore, belief ranges provide valuable insights into personal perceptions and thus form a useful extension to existing scenario methods.
Despite the fact that our analysis is based on mental models of potential outcomes, applying a consistent logic helps to explore the extent to which personal beliefs or worldviews can influence these outcomes. Our results show that there is considerable overlap in the belief ranges for the policy interventionist Muskateer scenario and the market liberalization Marketeer scenario (cf. Fig. 2b,c,d). A further inspection of the HEW/LEW interpretations reveals that although both scenarios result in positive outcomes for the HEW, the risk of negative outcomes appears greater in the market-led scenario, because the interventionist measures provides more buffering from external pressures by investing public funds to avoid or limit negative consequences. However, more quantitative methods are required to explore the range of judgment-based variation within each scenario, as discussed below.
Over recent decades, conventional scenario methods have been used extensively to explore future effects on climate, ecosystems, and the services they provide (e.g., MA 2005, Schröter et al. 2005, IPCC 2007). Scenarios have proved to be successful in linking qualitative storylines to quantitative models, providing numeric outcomes for potential effects on ecology and society. When the processes that are influenced by the storylines are well understood and can be adequately quantified, as with many biophysical and macroeconomic processes, traditional methods for interpreting scenario outcomes (Stage 5) work well. However, when processes are less well understood, as is the case in many social processes, interpretation and judgment become more important.
The exercise presented here illustrates how personal judgment can influence scenario outcomes. There is a risk of developing fixed paradigms in scenario development that ignore other possible outcomes within the same storyline framework. This is an important observation that, as far as we are aware, has not yet received attention in the scenario literature. Although ex ante policy impact assessment tools (cf. Van Ittersum et al. 2008) are useful for exploring short-term effects of specific policy measures, explorative scenarios should span a wide range of possible future outcomes to explore medium to long-term effects on social-ecological systems. Paradigms based on one interpretation of a scenario create an artificial and undesirable limit on the range of future worlds explored.
At first, it may seem confusing that alternative scenario directions can result in similar outcomes (cf. Fig. 2b,c,d). Our example shows that both policy intervention and market liberalization can result in positive or negative implications for rural regions. However, the mechanisms leading to these outcomes are different among scenarios. The HEW/LEW concept forms a useful first step to tease out these different processes. For example, in the Muskateer HEW there is large public investment in rural infrastructure, allowing public services to be transformed and delivered through better transport networks and information and communication technologies, resulting in improved accessibility for businesses. Conversely, in the Marketeer HEW, technological development is high because of the success of private enterprise, and rural society benefits from rapid technology transfer through market mechanisms. Such an analysis can lead to further research to identify and quantify thresholds for the success of both mechanisms.
It is a considerable challenge for the environmental change modeling community to develop tools that allow judgment to be made explicit. Ideally, the HEW/LEW concept could be translated into transparent quantitative models incorporating the range of processes that could operate within the same scenario. Such models could be informed by open discussions with stakeholders and subsequently explore the belief range for each scenario (cf. Fig. 2). However, existing numerical models (e.g., MNP 2006, Rounsevell et al. 2006, Verboom et al. 2007, Verburg et al. 2010) are programmed to follow predefined scenario interpretations of key drivers and cannot be easily adapted for this purpose. Developing tools that can incorporate judgment will therefore form a considerable challenge for the modeling community, requiring considerable research investment.
Personal judgment and interpretation can greatly influence scenario outcomes and can even lead to the development of scenario paradigms that limit the range of uncertainty that is covered by the scenarios. It is of great importance to make these judgments explicit in scenario development, especially when exploring broad consequences of alternative policy directions that may be based in political worldviews. The HEW/LEW concept outlined here provides a transparent framework for identifying and understanding differences in outcomes based on personal judgment. In addition, it helps to tease out underlying processes that can lead to these alternative outcomes. The ultimate challenge will be to develop flexible quantitative modeling techniques that are able to quantify judgment-related variation for alternative scenarios by incorporating multiple processes within the same scenario.
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ACKNOWLEDGMENTSThe work presented here was carried out as part of the European Union funded Framework Programme 6 Specific Targeted Research Project FARO-EU (Foresight Analysis for Rural Regions of Europe, contract 044495).
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