Improving accountability, sustainability and traceability in timber supply chains

Research Project, 2025 – 2028

Illegal timber trade is the most profitable natural resource crime, costing 30-157billion USD a year globally. Complex supply chains obscure timber sources, which often leavestraders’ declaration of origin as the sole evidence of provenance, despite the possibility of fraud.Recently, there has been a growing interest in using chemical markers, such as Stable IsotopeRatios Analysis (SIRA) or Trace Elements (TEs) to verify origin claims by matching levels ofmarkers within wood tissue to location-specific values predicted from reference data.Unfortunately, the use of SIRA and TE is currently limited by the need for large numbers ofreference samples from all species of interest and by not taking advantage of other types ofrelevant data.

We will develop machine learning models for inferring timber harvest location that make use ofSIRA and TE data from all species of interest, as well as environmental and satellite imagerydata. Our models will be developed using data from World Forest ID - a US-based not-for-profitthat has collected 9000 tree samples from 360 species in over 60 countries. While our methodwill be applicable globally, we will focus on several endangered tropical species present inCentral Africa. Our work will empower authorities worldwide in enforcing regulations like theEU Deforestation Regulation and enable companies to monitor their own supply chains.