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abstract 526

Facilitating policy-oriented learning about the multi-actor dimension of water governance

Author(s): Ça?r? B. Muluk
Author(s): Leon M. Hermans, Çagri B. Muluk
Leon Hermans, Assistant Professor, Delft University of Technology, Faculty of Technology, Policy and Management, P.O. Box 5015, 2600 GA Delft, The Netherlands Tel. ++31-15-2785776; Fax ++31-15- 2786233; Email L.M.Hermans@tudelft.nl

Keyword(s): policy learning, evaluation, actor analysis, EU Water Framework Directive, theory-driven evaluation

Article:

Poster:

Session: OS5e Multiple and multisector uses

AbstractPolicy-oriented learning is critical to water governance in a

context of constant change. The complexity of water systems and the unpredictable changes require adaptive water

governance. Policies should be based on the best available knowledge and leave room for flexibility in their

implementation. Their implementation should be monitored and evaluated at regular intervals, to enable learning

about a policy’s impacts as its implementation unfolds. This should ensure that adjustments are made when necessary

and that emerging insights feed into subsequent policy cycles.

Learning is required about the physical water

system and about the human system through which policy measures affect the physical resources base.

Unfortunately, such learning is complicated by numerous factors, including a lack of monitoring data, time-lag effects

between the implementation of policy measures and their impacts, as well as thresholds and leaps in nonlinear

ecological processes. Also, water governance involves various actors with different roles and responsibilities. It is

difficult to understand how institutional and socio-economic policy measures work in these multi-actor networks, as

well as to organize collaborative learning among those actors. Especially for this multi-actor dimension, there seems

to be a need for new tools and approaches that facilitate learning for water governance.

Model-based actor

analysis offers a promising approach to facilitate such learning that addresses the multi-actor complexities. Model-

based actor analysis approaches cover several methods that help to understand the perceptions, resources and

values of the actors involved, and/or the structural characteristics of actor networks. This helps to gain insight into the

factors that influence the outcomes of actors’ interactions. Past application of these approaches for policy

development suggest that they yield interesting new insights and have the potential to contribute to the interaction and

learning processes among actors. Especially the models that analyze actors’ perceptions, such as dynamic actor

network analysis, are promising for policy learning. These models are compatible with the idea of theory-driven

evaluations, using actors’ inputs to reconstruct critical assumptions behind policy mechanisms and to identify different

success criteria.

The paper will illustrate the use of dynamic actor network analysis to evaluate early

experiences with efforts to support the implementation of the EU Water Framework Directive in Turkey. The results

show some important strengths and limitations of the application of the Water Framework Directive to support water

governance in Turkey. More generally, the findings confirm that model-based approaches for actor analysis can

make an important contribution to policy-oriented learning for better water governance.

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Facilitating policy-oriented learning about the multi-actor dimension of water governance