Trase data enables companies to assess their supply chain deforestation exposure via import markets, commodity traders and physical assets.

Understand your company’s deforestation exposure

Highlight where action is needed to address deforestation risks in complex commodity supply chains using Trase data and tools.

Deliver commitments to deforestation-free supply chains

Trase spatial data reveals the deforestation risks associated with specific assets in producer countries.

Highlight asset risk using spatial data

Trase provides data and insight on commodity supply chains, helping companies measure, manage and reduce their deforestation exposure.

Trase teamed up with sustainability consultancy AdAstra to help a leading food manufacturer understand how soy in animal feed changes the carbon footprint of its chicken products.

Using data from Trase, AdAstra identified the origins of the soy and calculated corresponding land conversion emission factors at subnational level with their data solution Orbae.

The insights generated by the solution enable companies to take action on their land conversion hotspots with high-accuracy data, regardless of their traceability knowledge.

“Tapping into Trase data takes our own data to the next level. By revealing the origins of soy subnationally and combining it with our land conversion data in Orbae, we’re able to sufficiently overcome our clients’ lack of traceability to push their understanding of their land conversion risk closer to the reality on the ground.

The project on soy offers a blueprint for other commodities such as corn, rice and palm oil used in animal feed, and paves the way for the consideration of additional important metrics such as land occupation, biodiversity loss and water stress.”

Dr Jürgen Reinhard, Co-founder, AdAstra Sustainability

Powering action on carbon emissions from land conversion

Trase and AdAstra

Companies that have committed to reducing deforestation in their products and supply chains need a tool to identify areas of risk and guide action to address them.

Trase and the Consumer Goods Forum’s Forest Positive Coalition developed an approach to risk benchmarking sourcing regions for soy and beef to aid implementation of voluntary deforestation commitments.

One important application is to classify sourcing regions as negligible risk and non-negligible risk to enable companies that can trace volumes to negligible risk sourcing regions to claim deforestation and conversion-free (DCF) volumes from these regions without plot-level traceability.

Trase and the Consumer Goods Forum helped companies make progress towards zero-deforestation commitments

Consumer Goods Forum

Identifying hotspots enables consumer brands to understand the level of deforestation risk they could be exposed to as well as which of their suppliers or intermediate countries of sourcing account for the largest component of risk. This can help brands take action by prioritising areas of their supply chain to focus on and identifying which suppliers to engage with. The process can inform practice at company level, but also the collective management and reduction of risk.

“Whilst the exposure of luxury brands to cattle-linked deforestation may be smaller than other industries, the industry has a significant role to play in forest protection and preservation. Trase’s assessment helped ReLI members understand their individual and collective risk in terms of soy embedded in the leather they purchase. Bringing transparency to this risk is an important step informing how and where members can most effectively act towards eliminating deforestation in leather supply chains.”

Cliodhnagh Conlon, Associate Director, BSR

Luxury fashion brands in the Responsible Luxury Initiative worked with sustainable business advisers BSR and Trase to map their leather supply chains and understand levels of deforestation risk exposure through animal feed.

Companies

1Understand your company’s deforestation exposure

2Deliver commitments to deforestation-free supply chains

3Highlight asset risk using spatial data

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