The Telos Method

Box 19.1. Definitions of relevant terms.

Term

Definition

Capital

The three essential aggregated subsystems of

the total societal system: ecological, socio-

cultural, and economic.

Stocks

The essential elements that together de-

termine the quality and quantity of the capital.

Requirements

The requirements formulated with respect to

the development of a stock.

Indicators

The degree to which the requirements are met

is measured by means of indicators.

Standards

Normative criteria, developed by the stake

holders, to assess the score of indicators.

Context variables

Variables that influence sustainability but can

not or will not be influenced.

Box 19.2. Stocks.

社会文化

Ecological

Economic

Health and care facilities

Nature

Labor

Solidarity

Soil

Capital goods

Safety

Deep groundwater

Knowledge

Cultural diversity

Air

Infrastructure

Citizenship

Surface water

Economic structure

Living environment

Natural resources

Education and

training

Box 19.3. Labor stock.

Indicator

Weight

Ratio of employment to labor force

17%

Use of potential labor stock

20%

Labor market tension

9%

Educational match

17%

Initiation and aging

13%

Number of information and communication technology jobs

8%

Number of highly educated people

Ecological capital

Figure 19.3. Visual representation of the actual situation and the direction of development, by indicator.

社会文化capital Economic capital

Figure 19.3. Visual representation of the actual situation and the direction of development, by indicator.

Weighting and Determining the Value of the Capitals. The score of one capital is the weighted average of the stock scores. Not all stocks are equally important, so the stocks were weighted, like the indicators. The process of determining the sustainability triangle is summarized in Figure 19.4.

By mapping out the scores per stock within a capital and by comparing them in time, we can ascertain whether there is progress in the desired direction. In each capital, progress is represented by the shaded part of the line expanding outward toward the corner of the triangle. Progress in capital as a whole can coincide with negative changes in one of its stocks if this is compensated by progress in one of the other stocks of the same capital.

Figure 19.4. Visual representation of the structure of the sustainability triangle.

Application of the Telos Method

The Telos method was applied in four Dutch provinces: first in the province of Brabant, in 2000 and 2001, and then in 2003 in the provinces of Zeeland, Limburg, and Flevoland. When we started, we expected to find some dissimilarities between the provinces but not too many. However, the reality was different, and we found major differences. We discuss and analyze them in this section, first by looking at the process. For additional information, please see our Web site (www.telos.nl).

The Process

The Telos method had already been applied in Brabant before it was used in the other three provinces. The idea was to use the method developed for Brabant as much as possible in the other regions. The stocks, requirements, and indicators defined in Brabant were used as inputs, and the intention was to use the same procedure for the other regions (i.e., to adapt, reject, or adjust them according to expert judgment and stakeholder views).

In practice, the modus operandi in the other provinces was different. It was shaped far more by the provincial government and stakeholders' points of view than in Brabant (Figure 19.5).

Region of Brabant

Assignment (political)

Analyze policy papers

利益相关者评估库存、需求和indicators by stakeholders

Data collection

New data in 2002

Publication and debate

Three other regions

Assignment (administrative)

Administrative assessment of the model of Brabant

Figure 19.5. Differences and similarities between Brabant and the three other regions.

In Brabant, from the outset there was political support for the idea of assessing regional sustainable development. In the other provinces, the political will was less pronounced. In all three provinces, the main supporters of the process were provincial civil servants. One explanation for this difference was the fact that this project was partly initiated and largely funded by the Dutch Ministry for Housing, Regional Development, and the Environment (VROM). The ministry wanted a general model for assessing sustainable development at the provincial level in the Netherlands (with a 75 percent overlap concerning requirements and indicators).

Stocks, Requirements, and Indicators

如前所述,目的的第一步model is to determine the stocks, requirements, and indicators for each of the three forms of capital. In Brabant this was done in three steps. The first step was desk research, a thorough analysis of existing scientific publications and policy documents. The second step was to discuss the results of this research with experts and adapt them if necessary. The third step was to discuss these outcomes with a selected group of thirty stakeholders, consisting of key informants from all relevant sectors (government, civil society, business) in Brabant. They had to decide what the final stocks, requirements, and indicators should be. The decision was based on consensus.

In the other three provinces, the procedure was somewhat different. The stocks, requirements, and indicators already defined in Brabant served as a starting point. They were presented in each of the provinces to a selected group of stakeholders, mostly provincial civil servants, as well as specialists and generalists. The procedure for doing this was as follows.

After an introduction, where the method was presented along with the stocks, requirements, and indicators already defined in Brabant, subgroups were formed for each type of capital. Those subgroups were made up of officials from the province who were specialized in the policy fields relevant to the capital concerned. Each subgroup was given the assignment to examine the stocks, requirements, and indicators defined for Brabant and, if necessary, adjust them to local circumstances. Once this was done, we verified whether the outcomes formulated by those provincial civil servants were also accepted by the other categories of stakeholders. This was done in interactive sessions with selected representatives. When necessary, adjustments were made. The results of those sessions were presented and discussed again in a workgroup made up of some of the members mentioned earlier. This group made the final decision about what the stocks, requirements, and indicators should be.

Operationalization and Data

The next step is the most labor-intensive one. The indicators are put into use and data are collected, processed, and weighted on the basis of these indicators. The first real obstacles appeared at this time. In Brabant and in the other three provinces, little time was available for this part of the process. We soon found out that much of the data needed were available only at the national level. Sometimes it was difficult to break these national data down into subsets usable at the regional level. Sometimes regional data available in theory were in fact not accessible. Even worse, sometimes the available data did not match the indicators, for quantitative or qualitative reasons.

In fact, the process of collecting, interpreting, weighting, and combining regional data proved to be the main obstacle in the whole method and was very time consuming.

Evaluation of the Process

In all four provinces, the learning process was considered as important as, if not more important than, the factual results of the assessments. The main purpose was to develop and test a method for assessing regional sustainable development.

In 1999, when we started developing our method, well-developed models to assess regional sustainable development did not exist, although there already was a growing body of useful literature in principle. For instance, we used the studies of Hardi and Zdan (1997) and Bossel (1999). One of the few existing region-oriented methods was developed in British Columbia, but that approach differed from the method we had in mind. It opted for a strongly structured, statically based model (Pierce and Dale 1999; Robinson 1996).

Our desire to emphasize the learning process was a good reason to call evaluation meetings with experts and stakeholders in all the provinces. A lot of useful comments came out of these sessions. The most relevant are as follow:

Positive.

•The reason why the elements that make up a capital are chosen and brought together is worked out well in the model. It also increases our insight into the ways they are connected.

•The method reinforces and conceptualizes the interaction and communication between scientific disciplines, science and nonscience, and different arenas in society (policy, civil society, and business).

•The method lends itself well to propagating the idea of sustainable development and the assessment of it on a regional scale to a wide audience. This is partly because of the simple and attractive way in which the concept and the results can be presented and visualized.

Negative.

•The connection and interaction between the three forms of capital are not worked out well enough.

•Partly as a result of this, the debate often got bogged down in a discussion about the effectiveness of concrete measures, instruments, or funds. (However, this was also caused by giving stakeholders a strong voice.)

•The method of choosing requirements, indicators, and sometimes also stocks was too subjective and therefore was too sensitive to change. Moreover, the way the norms were selected was not always transparent.

•Although participants in the end tended to appreciate the method, they had some problems getting acquainted with it at first. The degree of abstraction is considered high. Disciplines and topics are linked in new ways.

Differences in Place and Time

As explained earlier, the idea of developing a model to assess regional sustainable development was broadly supported in the province of Brabant, more widely so than in the other three provinces. But there are more differences. In 1999, when the project started in Brabant, the economy in the Netherlands—and even more in Brabant—was booming, and it was a time of political optimism. The assessment in the other provinces was done after the ICT bubble burst and the 9/11 attacks took place. The promotion of sustainable development became a lower priority in the Netherlands, and the perception of it changed. Economic growth became a top priority again, along with another and rather new concern: security. These concerns clearly affected the perceptions and wishes of the stakeholders and influenced the selection in all three provinces. This is one of the possible drawbacks of relying heavily on stakeholders.

However, physical, economic, and sociocultural disparities were more important in explaining the differences between the provinces. Some of these differences are as follow:

•Brabant and Limburg have sandy soils; Zeeland and Flevoland have clay soils.

•In Limburg minerals are extracted; this is not the case in Zeeland and Flevoland.

•Brabant has a lot of manufacturing industries. These are nearly absent from Flevoland, where agriculture and the service industries are important.

•Aging of the population is not an issue in Flevoland but is an important one in the other provinces (e.g., in the countryside in Brabant).

The Telos Method Itself as an Indicator

The Telos method is, first of all, itself an indicator, intended to show what the situation is at a certain moment in a certain political and geographic area with regard to sustainable development. It is a kind of alarm system designed to show what is going well and what needs attention. Indicating trends is considered more important than providing static measurements.

However, the better one can measure, the better this purpose will be served. Measuring presupposes the availability of sufficient, reliable, comparable data. Often these requirements have not been fulfilled, as explained earlier. This does not mean that the method cannot be used. Certainly in the initial phase the process is already on track, and thus is a success, if it frames the opinions of people who make decisions, helps to develop a common language, stimulates discussions, and draws attention to the need to develop adequate norms and data and if it provides some concrete indications on the basis of already existing data.

The Telos method is still in its infancy. Its main goal is to offer politicians, civil servants, nongovernment organizations, firms, and citizens a tool to discuss sustainable development in general and its state in their own region.

The Results Compared

It is worthwhile to discuss some of the concrete outcomes of the assessments in the different provinces. However, we will limit ourselves to comparing the selection procedures of the stocks, requirements, and indicators in the different provinces.

Stocks

At first, the idea was to keep very strictly to the set of stocks already formulated by Telos on the basis of desk research. This proved not to be completely possible. Debates with local experts and stakeholders about the structure of capital led to adaptations, especially in the sociocultural capital (Telos 2003). Second, stocks already proved to be defined to some extent by temporal and spatial conditions. This became very clear when we applied the method in the different provinces:

•In Limburg, the stocks coincided to a large degree with those formulated for Brabant, although their interpretation differed greatly. The mineral resources stock was added to economic capital. It is very likely that this stock will henceforth be included in every new regional account. But before we can do this, we have to redefine the natural resource stock in ecological capital.

•The discussion in Zeeland made it clear that the consumption stock, before inclusion in sociocultural capital, should be deleted. The fact that the experts and stakeholders in Zeeland saw no use for the natural resource stock or for a new stock dealing with mineral resources was interesting.

Requirements

When the Telos method was applied in other provinces, the hope was to identify a large correspondence in the requirements defined and an overlap of at least 75 percent. The underlying reasoning was that this would allow the development of a generic, usableregional model.

The Venn diagram in Figure 19.6 shows the overlap for all provinces. The diagram consists of four rectangles, each representing a province. The area where all provinces overlap is black. Overlap between three (dark gray) or two (light gray) provinces is also represented. The white fields represent indicators without any overlap.

The results are remarkable. Only twenty-three requirements overlap everywhere; fourteen are shared by three provinces, fourteen by two provinces, and twenty-eight requirements proved to be completely province-specific.

Limburg has defined the most requirements (fifty-two) and also has the most province-specific ones (fourteen). This is partially because of the extra stock added in Limburg. This resulted in four extra requirements. Zeeland used the smallest number of requirements (thirty-nine). But in Zeeland two stocks were left out. If we look at the overlap with Brabant, we see that this overlap is the largest for Flevoland and the smallest for Limburg. As mentioned earlier, Limburg has the largest number of province-specific requirements.

Figure 19.6. Venn diagram of requirements.

Indicators

We also assessed the overlap of indicators, as shown in Figure 19.7.

林堡有最大数量的指标(123), 24 of which are province-specific. Sixty-three indicators are shared by all four provinces, represented in the area where all rectangles overlap. If we look at the overlap of Brabant with the other provinces, we see that Brabant and Limburg share the greatest number of overlaps (83) but also the greatest number of differences (69). But Flevoland and Zeeland have approximately as many overlaps with Brabant (80 and 81 indicators, respectively).

Overlap

To calculate the total overlap between the provinces, we formulated the following model. If a requirement or indicator occurs in all four provinces (f4, or a frequency of 4), there is 100 percent overlap; if a requirement occurs in three provinces, the overlap is 75 percent, and so on.

Figure 19.7. Venn diagram of indicators.

Overlap = f4 x 100% + f3 x 75% + f2 x 50% + f1 x 25%

Application of this formula led to the following results3:

Requirements: 60% overlap Indicators: 64% overlap