Trase

Trase
Name	Trase
Type	Non-profit initiative
Founded	2015
Headquarters	London
Focus	Supply chain transparency, commodity traceability, deforestation risk

Trase is an initiative that maps supply chains of agricultural commodities to reveal links between production regions and consumption markets. It connects producer municipalities and exporter companies with downstream traders, processors, and importing countries to inform policy, corporate sourcing, and civil society action. Trase integrates geospatial, customs, corporate registry, and certification data to make supply-chain risks about commodities like soy, beef, palm oil, and timber transparent.

Overview

Trase was launched as a collaboration between the Stockholm Environment Institute, the Global Canopy, and the University of Oxford with partners including the World Resources Institute and IUCN. It produces commodity-by-commodity profiles linking producing municipalities in countries such as Brazil, Argentina, Paraguay, Indonesia, and Malaysia to importing markets like China, European Union, United States, Japan, and South Korea. Trase outputs have been used by actors such as Unilever, Nestlé, Cargill, Bunge Limited, and Olam International as well as by governments including the UK Department for Environment, Food and Rural Affairs and the Brazilian Ministry of Agriculture. The platform complements datasets from institutions like FAO, UN Comtrade, Eurostat, and INTERPOL in efforts to reduce deforestation linked to commodity supply chains.

Methodology

Trase combines export customs data, corporate registry information, and municipality-level production maps from sources such as the MapBiomas project and the INPE satellite-derived products to perform origin-to-trade mapping. The methodology uses reconciliation algorithms informed by the International Trade Centre classifications and Harmonized System codes to link shipments to trader identities and export ports such as Port of Santos and Port of Belém. Trase employs spatial overlays with land-cover datasets from Global Forest Watch and the European Space Agency to assess deforestation risk at the municipality level. Analytic steps draw on research practices from groups like CIFOR, CIAT, and the Climate Policy Initiative to attribute volumes and value flows across nodes including trader, processor, retailer, and importer entities.

Data Sources and Coverage

Primary inputs include customs records from national authorities, corporate registries such as Companies House (UK), Registro Mercantil registries across Latin America, and maritime data from providers like MarineTraffic. Trase integrates satellite-derived land-cover time series from Landsat, Sentinel, and products like the Global Land Analysis & Discovery (GLAD) alerts. Commodity-specific datasets derive from projects such as Soy Moratorium monitoring, PalmTrace, and GFW Commodities. Coverage spans major producer regions in Amazonas (Brazil), Mato Grosso, Santa Cruz (Bolivia), Corrientes (Argentina), and provinces in Sumatra and Kalimantan with temporal scopes typically from 2000 to the present. The initiative cross-references import statistics from agencies like US Census Bureau Foreign Trade, China Customs, and Eurostat to allocate flows to importers including Tesco, Walmart, Carrefour, JD.com, and Amazon (company).

Impact and Applications

Trase outputs have informed corporate zero-deforestation commitments by companies such as Mondelez International, PepsiCo, and Kraft Heinz and supported campaigns from NGOs including Greenpeace, Rainforest Alliance, and Friends of the Earth. Policymakers in the European Commission and the Parliament of the United Kingdom have cited Trase analyses in discussions of due diligence laws like the EU Deforestation Regulation and proposals related to the UK Environment Act. Financial institutions including BlackRock, HSBC, Barclays, and BNP Paribas have used Trase-style data to assess portfolio exposure to agricultural-land conversion. Academic studies from institutions such as Harvard University, Stanford University, London School of Economics, and University of São Paulo have applied Trase datasets to analyze land-use change, leakage, and trade dynamics.

Criticism and Limitations

Critiques of Trase have been raised by researchers at Oxford University and commentators in outlets like The Guardian regarding data completeness and attribution uncertainty. Limitations include gaps in customs transparency in countries such as China and Vietnam, inconsistencies in corporate registries in jurisdictions like Panama and Luxembourg, and the difficulty of tracing blended commodities processed by multinationals including Marfrig Global Foods and JBS S.A.. Methodological challenges involve matching HS codes to agricultural products, temporal mismatches between satellite alerts and trade flows, and distinguishing legal from illegal land conversion in regions such as Rondônia and Para. Scholars from CIFOR and think tanks like the Chatham House have recommended complementary field verification, supplier engagement, and improved disclosure from traders.

Governance and Funding

Trase is governed by a consortium model involving research partners including the Stockholm Environment Institute, Global Canopy, and academic centers at the University of Oxford and University of Cambridge. Funding has come from philanthropic foundations like the Children's Investment Fund Foundation, the Good Energies Foundation, and the Grantham Foundation for the Protection of the Environment as well as multilateral donors such as the European Commission and bilateral agencies like the Swedish International Development Cooperation Agency (Sida). Project collaborations include technical partnerships with Google Earth Engine, ESRI, and data-sharing agreements with NGOs such as ProForest and governmental institutions including Brazilian Institute of Environment and Renewable Natural Resources (IBAMA). Governance arrangements emphasize open data principles aligned with initiatives like the Open Data Charter and peer review by academic partners including Imperial College London.

Category:Environmental data initiatives