Systems-thinking for environmental policy coherence: Stakeholder knowledge, fuzzy logic, and causal reasoning. Issue 136 (October 2022)

• Record Type: Journal Article
• Authors: Castro, Cyndi V.
• Is Part Of: Environmental science & policy. Issue 136(2022)
• Page Start: 413
• Page End: 427
• Publication Date: 2022-10
• Subjects: Participatory modeling -- Policy coherence -- Policy resistance -- Fuzzy cognitive mapping -- Nature-based solutions -- Causal loop mapping; Environmental policy -- Periodicals; Environmental sciences -- Periodicals; Environnement -- Politique gouvernementale -- Périodiques; Sciences de l'environnement -- Périodiques; Environmental policy; Environmental sciences; Periodicals; Electronic journals; 363.70561
• DOI: 10.1016/j.envsci.2022.07.001
• Languages: English
• ISSNs: 1462-9011

Abstract

Abstract: Environmental policies are often chosen according to physical characteristics that disregard the complex interactions between decision-makers, society, and nature. Environmental policy resistance has been identified as stemming from such complexities, yet we lack an understanding of how social and physical factors interrelate to inform policy design. The identification of synergies and trade-offs among various management strategies is necessary to generate optimal results from limited institutional resources. Participatory modeling has been used within the environmental community to aid decision-making by bringing together diverse stakeholders and defining their shared understanding of complex systems, which are commonly depicted by causal feedbacks. While such approaches have increased awareness of system complexity, causal diagrams often result in numerous feedback loops that are difficult to disentangle without further, data-intensive modeling. When investigating the complexities of human decision-making, we often lack robust empirical datasets to quantify human behavior and environmental feedbacks. Fuzzy logic may be used to convert qualitative relationships into semi-quantitative representations for numerical simulation. However, sole reliance upon computer-simulated outputs may obscure our understanding of the underlying system dynamics. Therefore, the aim of this study is to present and demonstrate a mixed-methods approach for better understanding: 1) how the system will respond to unique management strategies, in terms of policy synergies and conflicts, and 2) why the system behaves as such, according to causal feedbacks embedded within the system dynamics. This framework is demonstrated through a case study of nature-based solutions and policymaking in Houston, Texas, USA. Graphical Abstract: ga1 Highlights: Policy resistance occurs when system response undermines well-intended strategies. Stakeholder beliefs may trigger causal feedbacks of policy synergy and/or conflict. Causal loop models reveal feedbacks but are difficult to quantify at large-scale. Fuzzy maps quantify system cause-effect but may obscure intuitive causation. A combined systems-thinking approach is demonstrated for improved policy coherence.