Methods and Data

Methods

Trase’s supply chain mapping balances scale and data resolution. Our methods to link points of production, via trading companies, to places of import build on an enhanced form of material flow analysis called Spatially Explicit Information on Production to Consumption Systems (SEI-PCS) originally developed by Godar et al. 2015.

Mapping commodity supply chains: methods and data sources

Watch FAQ video: 'How does Trase map supply chains?' The starting point for applying the SEI-PCS approach to a specific country and commodity is national-level export data, linking countries of production to downstream traders and countries of import. We then apply the SEI-PCS approach to map subnational trade flows, discriminating production regions down to the lowest level of government administrative unit that the data and the complexity of the supply chain allow. Often this is defined by the availability of production data at subnational scales. Whilst it may be possible in some contexts to link supply chains to individual farms or production areas, the core focus of Trase is on mapping to subnational regions of production. We designate two different approaches for this sub-national analysis:

• Version 1 models rely heavily on modelling techniques, typically using transportation costs and constrained optimisation algorithms to allocate export volumes to individual production regions.
• Version 2 models use a more data-driven approach that identifies the subnational origin of individual material trade flows within the trade and customs information, and triangulates this information with other independent data sets.

If you are interested in more detail please read our manual on the supply chain mapping methods (SEI-PCS) that underpin Trase Supply Chains.

Trase does not use any private or confidential information. All data sources are either publicly available (including from government and industry websites as well as repositories) or available for purchase (such as from trade intelligence companies and government repositories). A description of all the methods and data sources currently used in Trase to apply the SEI-PCS approach to map subnational trade flows can be found for:

Subnational and company sustainability indicators

In systematically linking individual supply chain actors with specific production regions, Trase links actors to sustainability risks and investment opportunities associated with different production areas - with a particular focus on deforestation risk. Trase unlocks the power of spatially explicit data sets on deforestation and other environmental and social impacts, as well as the agricultural, economic and political conditions associated with different production regions.

To date, Trase has focused specifically on the transparency of agricultural commodity supply chains and the links between commodity exports and deforestation risk.

The methodology and data sources on how Trase assesses ‘commodity deforestation’ and ‘commodity deforestation exposure’ (previously 'commodity deforestation risk') can be found here:

Mapping ownership and financing of commodity trade on Trase Finance

As part of the Trase product offering, Trase Finance uses publicly available and vendor-procured data to map the financing and ownership of the trading companies involved in the trade of commodities covered by Trase.

Trase Finance uses the information on company sourcing patterns and associated exposure to deforestation risk supplied by Trase’s subnational supply chain maps to assess the exposure of financial institutions to deforestation risk. It quantifies the deforestation risk exposure of financial institutions (and other minority shareholders) holding equity investments in commodity traders.

The deforestation risk mapped in Trase Finance is only a partial picture of a financial institution’s deforestation risk based on their exposure through their direct financing of commodity traders mapped by Trase Earth. In some cases the data is not available to tell us who a company’s lenders are and how much capital is devoted to forest-risk activities. Data gaps in financial flow data (particularly in terms of debt and for private companies) and the limited subset of companies and commodities mapped mean that their total exposure to deforestation risk is likely to be much higher. Please see https://trase.finance/methodology for the detailed methods and data sources.