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Understanding Behavioural Patterns as a Means to Unravel Leverage Points for Transformation

Can we understand the influence of stakeholder decisions along the supply chain of a natural product? How can we use this knowledge to identify how can we move towards more sustainable use of nature’s contributions? Can we pinpoint the short and long-term changes that could be applied to make our supply chains more sustainable to safeguard biodiversity? During the Kunming-Montreal Global Biodiversity Framework of the Conference of the Parties (COP) new targets for 2030 were set to ensure that humans can “live in harmony with nature” by 2050. To achieve this, a transformative change in both nature and society is essential, guiding us towards a more just and sustainable future for everyone.

Within the BIOTRAILS project, our focus lies in understanding the role of established policies, personal and group interests, social norms and beliefs across stakeholder groups. This helps us identify leverage points for transforming society and safeguarding biodiversity. We also explore the role of human dimension in shaping these relationships through stakeholder interviews within the four supply chains (cocoa from Peru, handicrafts from Brazil, aquaculture products from Greece and gold from Ghana).


We have laid the foundation for the development of causal inference models (Structural Equation Modeling) to capture stakeholders’ norms and beliefs within each supply chain in relation to their perception of the value of biodiversity. In the first version of the deliverable, we have developed conceptual and qualitative models, which reveal the main attributes identified by the stakeholders as relevant for change. These models will be validated by stakeholders during the first round of project workshops and then be quantified using the data collected during the workshops. Such models form the basis for exploring scenarios for future developments following the work of IPBES on future pathways to sustainability. The ultimate goal is for these models to serve as a means of identifying leverage points for transforming these supply chains.