Deforestation is increasingly recognized as a critical barrier to achieving internationally agreed goals on combating biodiversity loss, mitigating anthropogenic climate change, and achieving sustainable and equitable development. In tropical forests, deforestation is driven largely by the expansion of industrial-scale commodity agriculture, as well as logging for timber. Many countries have responded with ‘demand-side’ laws requiring due diligence which necessitates knowledge of the harvest origin¹ of such commodities in international trade, most notably the recent European Union Deforestation Regulation (EUDR). Effective implementation of the EUDR, and agreements like the Convention on International Trade in Endangered Species (CITES), or enforcement of the US Lacey Act and relevant sanctions, requires credible and cost-effective methods for scrutinizing declared location of harvest and determining the origin of particular commodities and products in trade.

In a recent peer-reviewed paper published in Nature Plants, World Forest ID presents an innovative Artificial Intelligence (AI)-enabled ‘origin model’, which allows for efficient determination of the origin of agricultural and timber specimens in international trade, based on scaled, ground-truthed data. While the paper demonstrates the effectiveness of the model using temperate tree data, it was developed to facilitate objective verification of geolocation claims and therefore the traceability systems required to comply with policies aiming to tackle the trade in ‘conflict’, or illegally harvested timber, as well as agricultural commodities originating from illegally converted forests.

This briefing summarizes the implications of the research for a policy-focused audience.

THE INSIGHT SERIES

World Forest ID’s Insight series is designed to communicate the outputs of our long form research in a timely manner, by summarizing data snapshots and interim learning.