Sustainability Analysis · Supply Chain ESG

Food Supply Chain ESG Risk Dashboard

Where a company sources its ingredients determines a large part of its ESG risk profile. This dashboard maps supply chain risk across 8 key food-sourcing countries using four independently measured dimensions carbon & deforestation, water stress, labor rights, and EU regulatory exposure.

8 Sourcing Countries 4 ESG Dimensions ✓ 3 of 4 dimensions: verified open datasets Data: 2023

Data integrity note: Three of four dimensions in this dashboard use verified, downloadable open datasets WRI Aqueduct 4.0 (water stress), Walk Free Global Slavery Index 2023 (labor rights), and Our World in Data / GHG by sector 2023 (carbon & deforestation). EUDR regulatory exposure is a documented qualitative assessment based on EU Regulation 2023/1115 commodity classifications. All scores are normalized to a 1–10 scale from their original units.

Why This Analysis

When sustainability teams at food companies talk about reducing ESG risk, they often focus on what is directly visible their own operations, energy use, packaging. But for most large food companies, the majority of environmental and social impact sits upstream: in the farms, forests, and supply networks of the countries where their ingredients originate.

A German food company sourcing cocoa, palm oil, coffee, or soy is effectively importing the ESG risk profile of those supply chains. That risk is no longer optional to manage. Regulation particularly the EU Deforestation Regulation (EUDR), the German Supply Chain Act (LkSG), and the forthcoming CSRD value chain requirements is making supply chain ESG transparency a legal obligation, not just a reputational consideration.

This analysis addresses a practical question: if you are a procurement, sustainability, or ESG analyst at a European food company, which sourcing countries carry the highest risk and across which dimensions?

Why food specifically?

Food supply chains are among the most globally dispersed and risk-exposed of any industry. They rely on agricultural commodities from tropical regions where deforestation, water scarcity, and labour vulnerabilities are most acute and where regulatory scrutiny is now highest.

Why these 8 countries?

These countries represent the dominant sourcing origins for commodities most commonly found in European food products: soy, palm oil, cocoa, coffee, beef, rice, spices, and seafood. Together they cover the most material supply chain ESG exposure for EU food importers.

Why 4 dimensions?

Supply chain ESG risk is multi-dimensional. Carbon, water stress, labour rights, and regulatory compliance each capture a different type of exposure a country that appears low-risk on one dimension can be critical on another, which a single score would obscure.

Why now?

The EUDR came into force in December 2024. The German LkSG has required supply chain due diligence since 2023. CSRD Scope 3 reporting is expanding. Companies that cannot map their supply chain origins are already behind on compliance obligations.

Key framing: Supply chain ESG risk does not sit in a company's offices or factories. It sits in the countries where ingredients are grown and those risks are now regulated, measurable, and financially material.

The Four Risk Dimensions

Each country is scored 1–10 per dimension (higher = greater risk), then averaged into a composite. Three dimensions use raw data downloaded from open datasets and normalized to 1–10. The EUDR dimension is a documented qualitative assessment.

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Carbon & Deforestation
Agriculture + land-use change emissions (Mt CO₂eq, 2023). Negative LULUCF (forest sinks) treated as zero deforestation risk.
✓ Real data Our World in Data / GHG by sector
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Water Stress
Baseline water stress score, WRI Aqueduct 4.0 (0–5 scale). Converted to 1–10 using linear mapping.
✓ Real data WRI Aqueduct 4.0, 2023
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Labor Rights Risk
Total vulnerability score (0–100) from Walk Free GSI 2023. Normalized within this 8-country sample to 1–10.
✓ Real data Walk Free GSI 2023
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EUDR Regulatory Exposure
Documented qualitative assessment based on EU Regulation 2023/1115 commodity risk classifications and country designations.
⚠ Documented qualitative EU Reg 2023/1115
Critical risk (8.0–10)
High risk (6.0–7.9)
Medium risk (4.0–5.9)
Low risk (1.0–3.9)

Risk Heatmap All 8 Countries

Ranked by composite ESG risk score, highest first. Click any row to explore the detailed breakdown.

Country Key Commodities 🌳 Carbon & Deforestation 💧 Water Stress ⚠️ Labor Rights 📋 EUDR Exposure Composite Risk Level
All scores 1–10. Composite = equal-weighted average. Carbon & Water & Labor = verified open data. EUDR = documented qualitative assessment.

Country Detail

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Select a country
Click any row in the table above

Click a row to see the risk breakdown.

EUDR Commodity Exposure

EU Regulation 2023/1115 restricts imports of commodities linked to deforestation. Filled bars show exposure level (5 = maximum). Source: European Commission.

Source: EU Regulation 2023/1115 (EUDR), in force December 2024

Key Findings from the Real Data

The verified data reveals patterns that differ meaningfully from conventional assumptions about food supply chain risk. Understanding these differences matters for how companies prioritise due diligence.

Brazil: carbon risk, not water risk

Brazil has the highest carbon score (10.0) driven by 916 Mt of land-use change emissions, the largest in this sample. But its water stress score is just 1.5 (Low), because the Amazon basin makes Brazil water-abundant. Companies focused only on water stress would systematically underestimate Brazil's ESG exposure.

Ivory Coast: social and regulatory, not carbon

Ivory Coast has very low absolute carbon emissions (1.0) because it is a small economy. But its labor vulnerability score (7.6) and EUDR exposure (8.5) are among the highest in the sample. Its risk is concentrated in cocoa a social and regulatory challenge, not a carbon one.

Ethiopia: pure labor risk

Ethiopia has the highest labor vulnerability score in the sample (10.0 Walk Free GSI 2023), driven by food insecurity, conflict, and weak institutional protections. Its carbon and water scores are low. This is a country where the risk is entirely social, not environmental.

China: the forest sink effect

China's LULUCF score is actually -834 Mt CO₂ a net carbon sink due to large-scale reforestation. This is treated as zero deforestation risk in this analysis. China's overall risk profile is driven by water stress and labor concerns, not land-use emissions.

Data Sources & Methodology

Three dimensions use verified open datasets downloaded directly from their source institutions. The fourth (EUDR) is a documented qualitative assessment based on published regulation text. All scores are normalized to 1–10 within the 8-country peer group. The composite is an equal-weighted average.

Carbon & Deforestation
Our World in Data GHG Emissions by Sector
Agriculture + Land-Use Change & Forestry emissions (Mt CO₂eq) for 2023. Negative LULUCF (net forest sinks) set to zero only positive land-use emissions represent deforestation risk. Underlying source: Global Carbon Project / Climate Watch.
✓ Raw data downloaded and verified
Water Stress
WRI Aqueduct 4.0 Country Rankings (2023)
Baseline water stress score (0–5 scale) from the Aqueduct 4.0 country rankings dataset. Measures ratio of total water demand to available renewable supply. Converted to 1–10 using linear mapping. Open data, CC BY 4.0.
✓ Raw data downloaded and verified
Labor Rights Risk
Walk Free Global Slavery Index 2023
Total vulnerability score (0–100) from the GSI 2023 country-level dataset. Covers governance issues, lack of basic needs, inequality, disenfranchised groups, and effects of conflict. Normalized within this 8-country sample to 1–10.
✓ Raw data downloaded and verified
EUDR Regulatory Exposure
EU Regulation 2023/1115 (EUDR)
Documented qualitative assessment. Scores reflect which regulated commodities (beef, soy, palm oil, cocoa, coffee, wood) each country is a major source for, and whether those commodities are linked to deforestation under EUDR country risk classifications.
⚠ Documented qualitative not raw data
Normalization
Within-sample linear normalization
All dimensions normalized to 1–10 relative to the 8-country peer group using min-max scaling: score = 1 + (value − min) / (max − min) × 9. This preserves relative differences between countries while enabling comparison across dimensions.
Composite Score
Equal-weighted average of 4 dimensions
All four dimensions weighted equally in the composite. Users conducting sector-specific analysis may choose to weight differently for example, upweighting EUDR exposure for a cocoa-focused procurement review.

What This Means for Companies and Stakeholders

The risk scores in this dashboard reflect real, regulated, and financially material risks for anyone involved in food supply chains whether as a company sourcing ingredients, an investor evaluating ESG performance, or a policy professional designing due diligence frameworks.

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For food companies & procurement teams

Companies sourcing from Brazil, Indonesia, or Ivory Coast must demonstrate due diligence under the EUDR and German LkSG. A country-level risk map like this is the starting point for supplier prioritisation identifying which origins require deeper audits, supplier engagement, or alternative sourcing. Companies unable to map their Tier 1 and Tier 2 supply chain origins are already exposed to non-compliance risk.

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For investors & ESG analysts

Supply chain ESG risk is increasingly material to financial performance. Companies with concentrated sourcing in high-risk countries particularly for EUDR-regulated commodities face import disruption, regulatory penalties, and reputational exposure. Investors using headline ESG ratings should look beyond aggregated scores to understand the geographic origin of raw material risk, especially as CSRD Scope 3 disclosure makes this more visible and auditable.

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For sustainability & policy professionals

The variation across countries reveals a structural tension: the countries producing the most essential food commodities often have the least institutional capacity to meet sustainability standards now required by importing markets. Effective supply chain sustainability requires investment in supplier capacity building, transparent data infrastructure, and policy coherence between producing and consuming countries.

The core finding of this analysis is direct: supply chain ESG risk in food is concentrated, multi-dimensional, and now regulated. Brazil dominates on carbon and deforestation. Ethiopia and Ivory Coast carry the highest social risk. India and Thailand are the water stress stories that are routinely underestimated. And no country in this sample including Argentina, the lowest-risk origin is entirely free of EUDR compliance obligations. For any food company operating in the EU, understanding which sourcing countries carry which risks is no longer a sustainability ambition it is the minimum baseline for regulatory compliance, credible ESG reporting, and responsible procurement strategy.