Fraud and misrepresentation in forest products supply chains is often associated with illegal logging, but the extent of fraud in the U.S. forest products market, and the availability of forensic expertise to detect it, is unknown. We used forensic wood anatomy to test 183 specimens from 73 consumer products acquired from major U.S. retailers, surveyed U.S. experts regarding their forensic wood anatomy capacity, and conducted a proficiency-testing program of those experts. 62% of tested products (45 of 73) had one or more type of fraudulent or misrepresented claim. Survey respondents reported a total capacity of 830 wood specimens per year, and participants’ identification accuracy ranged from 6% to 92%. Given the extent of fraud and misrepresentation, U.S. wood forensic wood anatomy capacity does not scale with the need for such expertise. We call for increased training in forensic wood anatomy and its broader application in forest products supply chains to eliminate fraud and combat illegal logging.

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Developing algorithms that identify potentially illegal trade shipments is a non-trivial task, exacerbated by the size of shipment data as well as the unavailability of positive training data. In collaboration with conservation organizations, we develop a framework that incorporates machine learning and domain knowledge to tackle this challenge. Modeling the task as anomaly detection, we propose a simple and effective embedding-based anomaly detection approach for categorical data that provides better performance and scalability than the current state-of-art, along with a negative sampling approach that can efficiently train the proposed model. Additionally, we show how our model aids the interpretability of results which is crucial for the task. Domain knowledge, though sparse and scattered across multiple open data sources, is ingested with input of domain experts to create rules that highlight actionable results. The application framework demonstrates the applicability of our proposed approach on real world trade data. An interface combined with the framework presents a complete system that can ingest, detect and aid in the analysis of suspicious timber trades.

Russia contains almost one-quarter of the world's forests, exceeding the combined forest area of Brazil and Canada. There are significant obstacles to the development of a sustainable forestry sector in Russia including aging infrastructure, low processing capacity, poor transportation networks, poor financial and judicial institutional quality, and widespread corruption. In 2007, the Russian government announced an ad valorem export tax on unprocessed timber, or roundwood. By investigating the export tax in relation to Russia's timber trade with China, this paper explores the motivations and challenges behind Russia's desire to support a more robust domestic forest products sector.Key words: Russia, Russian Far East, timber processing, export tax, roundwood, investment, trade, World Trade Organization (WTO) RÉSUMÉLa Russie compte pour près du quart des forêts mondiales, surpassant la superficie forestière combinée du Brésil et du Canada. D'importants obstacles entravent le développement d'un secteur forestier durable en Russie, notamment à cause d'infrastructures désuètes, d'une faible capacité de transformation, d'un réseau de transport déficient, de la qualité restreinte des institutions financières et juridiques et de la corruption généralisée. En 2007, le gouvernement russe a annoncé une taxe à l' exportation ad valorem sur les ventes de bois de sciage russe ou sur les billots russes. Tout en faisant état de la taxe à l' exportation en relation avec les ventes de bois de sciage russe à la Chine, cet article explore les motivations et les défis derrière la volonté de la Russie d'appuyer un secteur domestique plus robuste des produits forestiers.

Detecting illegal shipments in the global timber trade poses a massive challenge to enforcement agencies. The massive volume and complexity of timber shipments and obfuscations within international trade data, intentional or not, necessitates an automated system to aid in detecting specific shipments that potentially contain illegally harvested wood. To address these requirements we build a novel human-in-the-loop visual analytics system called TIMBERSLEUTH. TimberSleuth uses a novel scoring model reinforced through human feedback to improve upon the relevance of the results of the system while using an off-the-shelf anomaly detection model. Detailed evaluation is performed using real data with synthetic anomalies to test the machine intelligence that drives the system. We design interactive visualizations to enable analysis of pertinent details of anomalous trade records so that analysts can determine if a record is relevant and provide iterative feedback. This feedback is utilized by the machine learning model to improve the precision of the output.