Keywords
knowledge integration; qualitative systems analysis; policy design; transdisciplinarity
Start Date
15-9-2020 10:40 AM
End Date
15-9-2020 9:40 AM
Abstract
In drought-affected water catchments, water management requires dealing with multiple, potentially conflicting, objectives of different water users on different levels. Policies to achieve a specific objective can have fostering or hindering impacts on the effectiveness of policies to achieve other objectives. To inform integrated water management in the Lurín river catchment, Peru, we used conceptual modeling to identify policy interactions (PI) and to design coherent policy mixes. Aiming at including the best available knowledge, we consulted local actors (Peruvian experts/stakeholders) and technical experts (German academics). This paper reflects on our experience and asks: If, how and why do PI assessments diverge between local actors and technical experts? How can both perspectives be meaningfully combined/integrated? Using a cross-impact balance (CIB) approach, we built a qualitative PI model to design policy mixes for the Lurín catchment. We identified 14 main objectives with 2-5 alternative policies each (47 policies) (validated during a one-day workshop with local stakeholders) and then interviewed 19 local actors and 10 technical experts to assess hindering and fostering between-policy impacts. Interview data were first condensed into 2 (local actor; expert) semi-formalized PI models. Then, methods to compare, combine and integrate these into one PI model were tested and reflected. Model ensemble and impact statement analyses revealed that locals and experts had some agreement but diverged considerably on policy interactions, in part due to referencing different knowledge systems (i.e. academic knowledge vs. local experience). Also, the two conceptual models resulted in diverging sets of deduced policy-mixes, presenting a challenge for integration on the level of models and on the level of results. Approaches combining perspectives (e.g. summing models) as well as more complex approaches integrating both models into one all revealed advantages and disadvantages as to, e. g., transparency and legitimacy.
Combining local knowledge and technical expertise in water research: Experiences from the Río Lurín catchment, Peru
In drought-affected water catchments, water management requires dealing with multiple, potentially conflicting, objectives of different water users on different levels. Policies to achieve a specific objective can have fostering or hindering impacts on the effectiveness of policies to achieve other objectives. To inform integrated water management in the Lurín river catchment, Peru, we used conceptual modeling to identify policy interactions (PI) and to design coherent policy mixes. Aiming at including the best available knowledge, we consulted local actors (Peruvian experts/stakeholders) and technical experts (German academics). This paper reflects on our experience and asks: If, how and why do PI assessments diverge between local actors and technical experts? How can both perspectives be meaningfully combined/integrated? Using a cross-impact balance (CIB) approach, we built a qualitative PI model to design policy mixes for the Lurín catchment. We identified 14 main objectives with 2-5 alternative policies each (47 policies) (validated during a one-day workshop with local stakeholders) and then interviewed 19 local actors and 10 technical experts to assess hindering and fostering between-policy impacts. Interview data were first condensed into 2 (local actor; expert) semi-formalized PI models. Then, methods to compare, combine and integrate these into one PI model were tested and reflected. Model ensemble and impact statement analyses revealed that locals and experts had some agreement but diverged considerably on policy interactions, in part due to referencing different knowledge systems (i.e. academic knowledge vs. local experience). Also, the two conceptual models resulted in diverging sets of deduced policy-mixes, presenting a challenge for integration on the level of models and on the level of results. Approaches combining perspectives (e.g. summing models) as well as more complex approaches integrating both models into one all revealed advantages and disadvantages as to, e. g., transparency and legitimacy.
Stream and Session
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