Objetos e imágenes

Science-policy interfaces are defined by van den Hove (2007, p.807) as “… social processes which encompass relations between scientists and other actors in the policy process, and which allow for exchanges, co-evolution, and joint construction of knowledge with the aim of enriching decision-making. They are implemented to manage the intersection between science and policy.”

Three theoretical problems and related science-policy interfaces that are considered relevant to WDM implementation, identified from recent review article by van den Hove (2007) examining science-policy interfaces in environmental management, are listed in Table 1.2, below. It should be pointed out that van den Hove (2007) identifies ten science-policy interfaces in all relating to environmental management and the six listed in Table 1.2 are those that are considered particularly relevant to the research reported in the following chapters.

The first theoretical problem in Table 1.2 is associated with the meaning of research as input to policy-making and relates to the complexity, uncertainty and indeterminacy that arise when explaining and predicting human interaction with the environment (O’Connor, 1999). A consequence of complexity, uncertainty and indeterminacy that is relevant to cross-sectoral planning is that “we are unavoidably confronted with an irreducible plurality of valid standpoints and of (objectively and subjectively) valid descriptions of the world” (van den Hove, 2007, p811).

Table 1.2. Science-policy interfaces relevant to WDM planning

This raises a need for approaches to facilitate communication and debate about assumptions, choices and uncertainties, and about the limits of scientific knowledge (Farrell and Jager, 2005). Accepting the limitations of scientific knowledge is a possible barrier to adopting such approaches but as van den Hove (2007, p809) points out, “…contrary to some a priori fears of relativism that are often found in both scientific and policy communities, such transparency and explicit statement of boundaries does not weaken the power of science—or maybe only some undue power—but can correspond to a reinforcement of scientific quality”.

The second theoretical problem relevant to WDM implementation relates to the identification of research priorities and, what van den Hove (2007) refers to as “issue-and curiosity- based science”. A number of authors (van den Hove, 2007;

Lubchenco, 1998) have recognised that science in general, and particularly environmental sciences are being increasingly driven towards issue-driven approaches and away from curiosity-driven research. This is partly due to “the acute nature of the environmental crisis that gives a sense of urgency to the development of knowledge on which to ground action” (van den Hove, 2007, p818). Lubchenco (1998) further stresses that in a rapidly changing world where complex environmental issues are becoming ever more pressing, the role of science cannot be confined to its ‘‘traditional’’ roles as scientists are increasingly called upon to address the most

THEORETICAL PROBLEMS SCIENCE-POLICY INTERFACES RELEVANT TO WATERDEMANDMANAGEMENT Complexity, uncertainty,

indeterminacy 1. To bring about communication and debate about assumptions, choices and uncertainties, and about the limits of scientific knowledge

2. To allow for articulation of different types of knowledge: scientific, local, indigenous, political, moral and institutional knowledge.

3. To provide room for a transparent negotiation among standpoints (participatory processes).

Issue-driven vs. curiosity-driven science

Prioritising and organising research

4. To allow for balancing issue- and curiosity-driven science and their articulation in knowledge for decision-making processes

5. To include a reflection on research priorities and research organisation

Roles of scientific networks Inputs and roles of social sciences

6. To allow for genuine trans-disciplinary articulation between social and natural sciences

urgent needs of society. Following these observations, any method proposed to address science-policy interfaces in demand-side management planning would ideally support identification of key research priorities. For example, the degree of uncertainty between factors relevant to policy decisions can guide data-collection effort and addressing relevant issues.

The third theoretical problem arises from the need to address issues that cross the disciplinary boundaries of research. For IWRM and demand management impacts of water stress on social, economic and environmental and the interaction between humankind and nature represent problems that are not bound to traditional research disciplines. IWRM and WDM thus require communication across and between research disciplines, and the way knowledge is articulated between disciplines determines how is it is communicated to policy-makers, managers and the public.

Ramadier (2004) refers to the articulation of knowledge across disciplines as

‘transdisciplinarity’ and describes it as “…the simultaneous integration of two contradictory movements of disciplinary thinking: on the one hand, the compartmentalization of knowledge; on the other hand, the existence of relationships between the disciplines—the aim being to determine how the different forms of knowledge thus produced can be articulated together” (Ramadier, 2004, p424).

Direct parallels can be found between Ramdier’s definition and the requirement in WDM planning to combine social, economic and environmental disciplines. Oxley et al. (2003) suggest that the extent to which computer-based decision support tools provide an environment that supports inter-disciplinarity is a criteria in determining their suitability to addressing environmental issues.

1.5 Conclusions

Sections 1.1 and 1.2 above made an important distinction between the legislating and design stages of WDM implementation that is referred to throughout this thesis.

Modelling and support tool tasks for the two stages are summarised in Table 1.3, below.

As indicated in Table 1.3, developing the evidence-base for WDM is relevant to both stages, although as discussed in Section 1.1.2 above, the objectives of developing the evidence-base for WDM are different for each stage.

Table 1.3. Water demand management (WDM) involves two clearly defined but interconnected tasks that computer-based support tools need to address

WDMLEGISLATION WDMDESIGN

 Forecasting and backcasting

 Uncertainty and risk

 Cross-sectoral planning

 Prior- evaluation: Identifying effective tools and support of targeting implementation effort

 Post- programme evaluation: to monitor programme effectiveness

←Developing the evidence-base→

For the legislation stage the evidence-base is required to legitimise the introduction of economic regulatory mechanisms to support investment in comprehensive demand-side management, whereas for the design stage the evidence-base is required to achieve the lowest cost per m³ saved and address issues such as affordability and social acceptability. Recognition of these different objectives is important because it allows support tool tasks to be clearly distinguished between these two stages of WDM implementation.

The evaluation research for assessing the effectiveness of Bayesian networks in facilitating implementation of WDM strategies required a research methodology that incorporated both model development and model evaluation. Section 2.4 in Chapter 2 describes the four-stage research methodology in detail. In summary, for model development, interviews with practitioners working on demand management in the case study area were used to develop causal maps of the WDM planning process from which a number of Bayesian networks (Bns) were developed. The resulting models were populated using data collected from the Sofia water company and from household surveys conducted during 2006. Two approaches to model evaluation were then employed to examine the effectiveness of the developed Bn models in facilitating the implementation of WDM strategies. The first, a technical evaluation, examined the adequacy of Bayesian analytical methods through a number of desk studies. The second, a subjective evaluation, assessed the usefulness of Bns from the perspective of the end-user. Technical and subjective evaluations are two of three possible types of evaluation described by Adelman (1992). The third type of evaluation, an empirical evaluation, would have required a longitudinal study to

compare model outputs with actual programme performance, and was not possible within the time constraints of this study.