Learning from Policy Experiments in Adaptation Governance

Belinda McFadgen, PhD researcher, IVM, VU University, Amsterdam

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Overview

Research gap and questions

Proposed analytical framework

Application of framework to case study in the Netherlands

Conclusions

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Research context and questions

Knowledge for Climate- “climate proof the Netherlands”

Adaptation governance- learning

policy experiments

“learning our way out”

“develop governance arrangements that connect new ideas”

Questions: What design features are most successful at enhancing learning effects?

what is learning and how can it be measured?

what is a policy experiment and how can we evaluate its design features?

how does design relate to learning?

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Policy Experiments

Aim: to find a definition applicable to adaptation governance• Bearing in mind the relevance to social-ecological systems!

Found in the literature:• Social experiments- e.g. RCTs• Real-life experiments- e.g. scientific studies in real world context• Sustainability experiments- e.g. innovation outside traditional governance context

Need for boundaries• Experimental gap• Controlled environment• Application to policy making

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Tentative definition

1) A policy experiment attempts to test a policy innovation in a field setting, whether an innovation in technology, concept, or governance process. Testing ranges from explicit findings of cause and effect to establishing a baseline for monitoring effects in a contextualized setting;

2) An experiment provides a “protected space” by temporarily changing the institutional context; and

3) It produces evidence for policy making so it has a connection with government policy and seeks to influence it.

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Policy Experiment: Analysed using an institutional arrangements approach- Ostrom’s rule typology.

Learning: ‘relatively enduring alterations of thought or behavioural intentions

that result from experience and that are concerned with the

attainment (or revision) of public policy’ (Sabatier 1987).

Analytical model

Within experiment:

Cognitive

Normative

Relational learning

Policy impact:

Credible

Salient

Legitimate knowledge

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Analytical model

Systematic framework for describing experiments: theory on what learning effects are expected from different designs.

boundary rule access

position rule initiator, facilitator

information rule knowledge distribution

information rule level of interaction

information rule source of knowledge

information rule type of knowledge

choice rule power distribution at decision nodes

aggregation rule how decisions are made

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Ideal Types

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Case study- Oosterschelde ecosystem engineer policy experiment

• Location- Oosterschelde National Park, Zeeland, South-west Netherlands. Unique for its outstanding natural features, shellfish industry, and recreation opportunities (e.g. diving).

• Problem- Dike causing erosion of sand causing loss of inter-tidal flats in estuary.

• Effects- Nature: habitat loss for migratory birds and other mammals; Safety: dike partially vulnerable to wave impact.

• Solution- Larger project- sand nourishment; specific project- use of oyster reefs as “eco engineers”- 200m long structures that work to prevent erosion and maintain biodiversity as “living reefs.

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Results

• Boundary• Main stakeholder types represented;• Open access- as a requirement;

• Information• Expert and lay knowledge utilised; • Regular discussion of alternative policy options• Regular interaction and dissemination of scientific information

• Position• Initator non-state actor, collaborative effort• Facilitator role by state officials

• Power• 3 decision nodes each with “Arnstein’s Ladder”• 70% engaged early on• Different actors at different nodes

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Results

Cognitive learning-***

Normative learning- *

Relational learning- *

Credibility- ***

Salience- **

Legitimacy- **

Result- Technocratic type with boundary characteristics

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Discussion and conclusion

Surprises:• Despite reflexiveness (discussion on alternative goals) there was little normative

learning• Established actor networks meant no apparent change in relational learning• Salience and legitimacy of the knowledge very high- perhaps because of the high

regard for scientific knowledge

Science-policy issues:• Length of experiment• Generalisability of results- introduction of oysters to other areas; meeting carrying

capacity

Conclusion:• Framework is broad and allows for a systematic analysis with focus on participation,

information, and power and how these features relate to learning;• Recognises role of science as cornerstone of experimenting with policy.