A Fuzzy Cognitive Map method for integrated and participatory water governance and indicators affecting drinking water supplies

S. Shahvi, P.-E. Mellander, P. Jordan, O. Fenton

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)
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Drinking water governance is challenging with different perceptions and priorities among stakeholders in different countries. To make provision for drinking water protection in agricultural areas, governance systems need to be mapped for bottlenecks to be identified and solutions highlighted. To address this a system thinking approach was used in an explanatory network analysis of Fuzzy Cognitive Maps (FCM) that were created during face to face interviews with stakeholder representative groups (individuals, policy developers, researchers, and regulators). Two exercises were designed and facilitated to obtain stakeholder maps on A) the water governance framework from stakeholders' own perspective with a ranking of actors in terms of their perceived importance and B) a list of importance factors and how these were connected for the provision of good drinking water quality supplies in agricultural areas. Causal relationships were subsequently drawn around each subject allowing mapping. A graph theory Hierarchy Index (h) approach examined if stakeholder groups preferred top down hierarchical governance or a more inclusive democratic governance approach. Finally, an auto-associative neural network method was deployed on group maps for examination during steady-state conditions for three scenarios to be explored i.e. changing "Farmers knowledge", "best management practice (BMP) uptake" and "Farmers behaviour and belief" to the highest level of influence and seeing how the system reacted. Results of Exercise A showed that all stakeholder representative groups had a different perception of the water governance framework. Most stakeholder groups had a democratic point of view regarding water governance structures and the ranking and importance of the actors within the framework. Results of Exercise B demonstrated that most of the groups have similar opinions regarding the highest ranked factors affecting drinking water quality and the possible environmental ecological policy options. In this second exercise, only one representative group showed a democratic outlook whereas all others had a hierarchal outlook. Scenario testing of policy options enabled bottlenecks and possible solutions to be identified. By boosting "Farmers behaviour and belief" to the highest possible level, resulted in a large increase in other factors - a scenario where farmers could benefit from the outcome. This would be achieved by enhancing farmers' willingness and intention to participate and implement BMPs. Better results would be achieved if farmers believed in the method and could benefit from the outcome. Also keeping "Farmers knowledge" at the highest point had a positive influence on the other factors. This can be achieved by enhancing farmers training and knowledge transfer by local and national actors. This method is widely applicable and should be considered for more integrated and participatory approaches to drinking water governance. [Abstract copyright: Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.]
Original languageEnglish
Article number142193
Pages (from-to)1-14
Number of pages14
JournalScience of the Total Environment
Early online date7 Sept 2020
Publication statusPublished (in print/issue) - 1 Jan 2021

Bibliographical note

Funding Information:
This work has been carried out under the WaterProtect research project supported by the European Union Horizon 2020 - Research and innovation Framework programme [grant no. 727450 ]. The authors express their gratitude to all stakeholders participating in the interviews and to the Agricultural Catchments Programme for hosting the project.

Publisher Copyright:
© 2020 The Authors

Copyright 2020 Elsevier B.V., All rights reserved.


  • Agriculture
  • Water governance
  • Water quality
  • Fuzzy Cognitive Map
  • Policy


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