To help businesses and governments realize more sustainable supply chains, new approaches are using spatial data and machine-learning techniques to connect Earth observation data to conventional economic tools.
The proliferation of global supply chains mean the environmental and social impacts of consumption are often experienced far away from the consumer. These chains, and as a consequence the connections between consumption and impacts, are often opaque.
This opacity challenges many sustainability goals. For example, producers, traders and retailers cannot confidently meet demand for deforestation- or labour exploitation-free products. Markets, investors and businesses cannot effectively target measures to make supply chains more sustainable.
This article describes how new approaches are being implemented using spatial data and machine-learning techniques to connect Earth observation data to conventional economic tools, in order to help businesses and governments improve sustainability. Several examples are mentioned in the article, including the Trase initiative led by SEI and Global Canopy, the Soft Commodity Risk Platform, Carbon Tracker, WattTime and Global Fishing Watch.
Tool / Trase permite a los gobiernos, empresas y otros abordar los impactos ambientales de las cadenas de suministro.
Feature / Trase initiative launches its 2020 review of commodity deforestation, supply chains and zero-deforestation commitments.
Journal article / A novel methodological approach to inform deforestation policies and to assess potential effectiveness of supply chain zero-deforestation commitments.
