Systems-thinking for environmental policy coherence: Stakeholder knowledge, fuzzy logic, and causal reasoning

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.