This Anti-Corruption Helpdesk brief was produced in response to a query from a U4 Partner Agency. The U4 Helpdesk is operated by Transparency International in collaboration with the U4 Anti-Corruption Resource Centre based at the Chr. Michelsen Institute.
Query
Please provide an overview of existing estimates on corruption-related revenue losses in the forestry sector and oversight mechanisms to address corruption risks.
Summary
Corrupt networks in the forestry sector involve diverse actors, and corruption ranges from bribery to state capture. Estimating corruption-related revenue losses is hindered by scarce and inconsistent data, underreporting of corruption, varying definitions of illegality and the lack of reliable administrative and judicial records on corruption offences. As a result, existing estimates differ widely and rarely isolate corruption’s fiscal impact from broader illegal logging. A range of oversight measures has been developed from new regulations such as the regulation on deforestation-free products (EUDR), prevention tools, such as corruption risk management systems and beneficial ownership transparency, to data driven and artificial intelligence based solutions for early detection of corruption risks.
Main points
- Corruption in the forestry sector is shaped by a complex interplay of local political–economic conditions and broader structural factors, such as the high economic value of forest resources, weak and opaque governance systems, unclear land tenure and limited oversight in remote forest areas.
- Corrupt networks in the forestry sector involve a wide array of actors, including forestry officials, other government agencies, law enforcement, private logging companies and organised crime groups, with the specific constellation of actors varying by local contexts and type of illegal activity.
- Corruption in the forestry sector ranges from petty forms (e.g. bribery) to high-level corruption, such as state capture, enabling both illegal activities (e.g. timber laundering, logging in protected areas) and illicit access to legal rights (e.g. obtaining logging permits through bribes).
- Two broad groups of studies estimate losses in the forestry sector: those assessing overall economic and revenue losses (including but not limited to corruption) and those estimating losses specifically attributable to corruption. Most rely on indirect estimation methods (such as trade data discrepancies, wood balance analyses, import source analyses, expert surveys) or hybrid approaches to gauge the scale of illegal logging and associated losses.
- Estimating corruption-related losses remains constrained by scarce and inconsistent data, underreporting of corruption offences, differing definitions of illegality and a lack of reliable, machine-readable administrative and judicial records. As a result, estimates vary and rarely isolate the fiscal impact of corruption from broader illegal logging activity.
- Notable global assessments illustrate the scale of economic losses linked to illegal logging: for example, the World Bank (2019) estimates a combined value of illegal logging, fishing, and wildlife trade at over US$1 trillion annually, with source countries losing US$7 billion to US$12 billion in revenue each year and foregone tax revenues from illegal logging alone reaching US$6 billion to US$9 billion.
- Despite broad recognition that corruption facilitates illegal logging and influences related losses, very few studies quantify corruption-specific revenue impacts, and those that do either lack a clear methodological description or analyse only broad cross-country correlations between corruption and forest loss, relying on distant proxies for corruption.
- Complementing regulatory measures, effective anti-corruption oversight of the forestry sector can be done using prevention and detection tools, corruption risk management, transparency and due diligence systems, beneficial ownership checks and emerging data driven and AI solutions.
Authors
Miloš Resimić
Reviewers
Jamie Bergin
David Aled Williams (U4)
Date
12/01/2026
