Leeuwerik, Roald Niels Christiaan ORCID: https://orcid.org/0000-0002-8656-6253 (2025) Tracing Prescribed Knowledge Flows in Wastewater Management Policies: An AI-Assisted, Governmentality-Informed Framework with Insights from Indonesia. Environmental Management . DOI https://doi.org/10.1007/s00267-025-02277-0.

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Abstract

Policy documents allow for the study of prescribed knowledge flows in decision-making processes. Although policy documents have been analyzed previously in wastewater studies, a more systematic approach to analyze prescribed knowledge flows remains to be developed. Guided by governmentality, this article proposes a framework to investigate prescribed knowledge flows and gain insights into intended stakeholder roles, techniques and technologies used to govern, as well as the nature of knowledge that should be exchanged. The framework is built upon the new possibilities by Artificial Intelligence (AI) by developing a prompt to identify prescribed knowledge flows. Building on an analysis of Indonesian policies, the study presented in this paper illustrates how a decentralized and community-led approach for wastewater management is planned. The approach intends to drive behavioral change and community-led management initiatives, thereby protecting public health and environmental quality. However, challenges include scarce details on prescribed stakeholder roles and an incomplete operationalization of national and/or regional provisions. While verification of AI output remains necessary, AI support saves time by reducing the need for full-text reading and summarization of identified prescribed knowledge flows. The method described in this paper can be used by decision-makers to facilitate critical inquiry of policies, or by non-governmental stakeholders to better understand complex legal texts and opportunities for involvement in decision-making.

Document Type:	Article
Programme Area:	PA3
Research affiliation:	Social Sciences > Deliberation, Valuation and Sustainability
Refereed:	Yes
Open Access Journal?:	No
DOI:	https://doi.org/10.1007/s00267-025-02277-0
ISSN:	0364-152X
Date Deposited:	06 Oct 2025 12:26
Last Modified:	06 Oct 2025 12:26
URI:	https://cris.leibniz-zmt.de/id/eprint/5741

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