How location data can support financial due diligence for deforestation risk

Geospatial data on exposure to deforestation in commodity supply chains can help banks and investors get to grips with the TNFD’s recommendations on nature-related financial disclosures.

29 Mar 2023

Ben Levett

Photo credit: Tropical forests in South Kalimantan, Indonesia (Muslim Hanafi/Shutterstock)

While action to date has been patchy and limited, financial institutions are becoming increasingly aware of the importance of nature-focused risk screening and the need to ramp up their due diligence processes to protect their reputations and ready themselves for upcoming requirements.

Best-practice initiatives such as the Taskforce on Nature-related Financial Disclosures (TNFD) are putting biodiversity and deforestation impact assessments front and centre. That message is reinforced by stringent deforestation-free supply chain validation requirements for high-risk agricultural commodity exports to the European Union and beyond from 2024. These new requirements bring material credit risk as well as reputational risk to banks and investors with financing links to the sector.

With this push, due diligence frameworks – such as the TNFD’s LEAP-FI – are highlighting the crucial importance of geospatial analysis. Only with this detailed location-based approach can financial institutions properly understand the level of risk in supply chains and determine the need for further information gathering. Better profiling the source of risk, whether to subnational regions or, better still, to specific operations and physical assets, is also crucial if they are to truly incorporate clear engagement and mitigation plans from day one of any financing relationship.

Location, location, location

While limited data on company-specific sourcing at the farm and concession level remains a barrier, there are a number of tools that can help fill these gaps and prioritise further location-specific due diligence efforts. For example, Trase Finance provides financial institutions with different granularities of location information so that they can tailor their decision-making processes according to the amount of information known about the company.

At the most generic level, country-commodity ‘context’ data, in combination with wider global assessments such as on environmental impacts of consumption of agricultural commodities by country and commodity, can give some high-level perspective on commodity sourcing pattern risk when only very limited information may be available at present (for instance, for some downstream companies). Even basic disclosure data on a company’s commodity purchases by country of origin and country of import can offer some broad, if blunt, insights on differential risks between sourcing countries and commodities.

However, impacts on nature such as deforestation are highly regionalised and likely to differ hugely within a sourcing country. Here, risk flags where traders are sourcing from high-risk biomes such as the Amazon and deforestation exposure metrics linked to traders’ subnational sourcing patterns provide the intermediate step towards a location-based screening approach.

Trase data estimates where a trader (and, by proxy, downstream companies that are customers of that trader) has significant exposure to a biome that is on a financial institutions’ exclusion list, subject to specific bank policy requirements (such as certification), or more broadly deemed an environmentally ‘sensitive’ location requiring more detailed screening.

Managing risk from the EU’s deforestation regulation

Trase data can also highlight where a trader has risk that falls within the deforestation definitions that are either within scope of the EU deforestation-free products regulation (such as the Amazon) or is likely to be included in scope following upcoming reviews (such as the ‘other wooded land’ of much of the Cerrado). Just under 100 subsidiaries and parent organisations flag as having exposure (greater than 50 ha) to soy deforestation associated with sourcing from the Amazon biome in 2020; for example, Bunge at almost 5,000 ha.

In addition, biome classification helps flag up when deforestation in a sourcing region is at heightened risk of being illegal under national laws (for instance, in the Atlantic Forest in Paraguay, ADM is the highest risk organisation on this metric). Legality under producer country legislation is relevant not only for the EU deforestation regulation but also for the UK Environment Act and proposed US legislation on corporate due diligence for forest-risk commodities.

For traders, a far more detailed level of due diligence analysis is also possible. Risk assessments can go beyond biome analysis into subnational administrative sourcing regions; for example municipalities in Brazil with both ‘hotspot’ exposures and full sourcing risk profiles at this more granular scale.

The hotspots narrow in on the small number of local regions, such as municipalities, that in many cases make up the vast majority of the total national deforestation risk. Around two thirds of the traders mapped by Trase flag with some hotspot exposure while around 5% come back with over 70% of identified volumes being sourced from risk hotspots. That flags up disproportionately high impact sourcing and highlights where engagement with a trader can really narrow in on addressing sourcing, monitoring and reporting shortcomings linked to a handful of specific locations.

The full risk profile view then looks more broadly at the whole sourcing picture to determine whether a trader has pockets of high-risk sourcing that could be addressed or whether high-risk sourcing is endemic. For example, Amaggi Louis Dreyfus Zen-Noh Holdings has around 70% of its volume from very high-risk regions and approximately a further 10% unknown, making it one of the highest risk Brazil soy traders by sourcing. It is also a significant ‘hidden’ risk for its joint venture owners – another due diligence risk flag for these traders (see figure). It is one of 100 or so organisations that source over half of their export volumes from either very high or high-risk regions.

The profiling also highlights indirect and unidentified sourcing, another high-risk sourcing flag, especially for contexts such as Cote D’Ivoire Cocoa. Such ‘untraceable’ sourcing creates blindspots and can often obscure high deforestation risk. These metrics allow a financial institution to better understand the depth and breadth of a trader's sourcing risk and to direct any subsequent detailed engagement plans or ‘key performance targets’ that relate to a company’s particular weakness. Almost half of cocoa traders come back with 100% of volumes through indirect sourcing, and even a major name like Cargill has around 40% indirect sourcing.

Taking due diligence to the next level

Prototyping the next level of location-specific due diligence, Trase Finance has been trialling a dataset for Indonesian palm oil that links deforestation exposure through to assets, such as mills and refineries, where possible. In 2020, the top 10 riskiest mills together had a deforestation exposure of 113,000 ha, while for the top 10 riskiest refineries it was around 208,000 ha (around 8% and 15% of national risk respectively) (see figure).

Asset risk can then be linked to operators, associated traders and export flows, and to the asset’s district risk sourcing. For example, some 47,000 ha of Royal Golden Eagle’s deforestation exposure was linked to the Kutai Nusantara refinery, operated by its Apical (PT Kutai Refinery Nusantara) group member. Over half of that refinery’s risk came from the districts of Paser, Bulungan and Berau, in Kalimantan Timur/Utara. In terms of asset-to-asset linkages, the deforestation risk for the Kutai Nusantara refinery is traced to 69 different mills, over half that risk coming from the top 10 mills, including around 10% from the single riskiest mill (Pipit Mutiara Indah).

Looking ahead, new EU regulations bring requirements on traceability and with this potentiallymore data to assist due diligence. Financial institutions looking to assess their exposure already have tools using satellite data combined with biodiversity and land-use datasets to guide project finance and due diligence where locations are known, using policy commitment data to track a company’s risk management and reporting, and using land-conversion impact metrics linked to assets, traders and importers, such as offered by Trase, to dig into supply chain risk. Robust location-based due diligence on nature-related risks can provide intelligence to not only manage exposure, but to help finance positive change.

Trase partners Stockholm Environment Institute, Global Canopy and Neural Alpha will publish guidance on due diligence for deforestation risk on 25 April. To find out more contact Pei Chi Wong, Senior Research Associate,

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