African supply chains are more visible—and more vulnerable—than ever. New disclosure rules, investor scrutiny and green trade barriers are forcing buyers to understand not just who their suppliers are, but how they behave. Yet traditional ESG assessments—forms, audits, certificates—miss too much, too often. This article outlines how African corporates and multinationals sourcing from the continent can build richer, fairer supplier ESG scores by incorporating “alternative data”: satellites, registries, public procurement feeds, sanctions lists, worker voice signals, and conflict/event datasets. Done well, these sources can reveal genuine performance, not just polished policy.

1. Why supplier ESG scoring is moving—fast

Regulation is the prime mover. The ISSB’s IFRS S1 and S2 standards, now the global baseline for investor‑grade sustainability disclosure, require companies to refer to and consider industry‑based SASB guidance—pulling supply‑chain topics firmly into mainstream reporting and assurance workflows.

Africa‑relevant regimes are multiplying:

• CSDDD (EU)—the Corporate Sustainability Due Diligence Directive—entered into force on 25 July 2024 and gives Member States two years to transpose mandatory human‑rights and environmental due diligence. In scope companies (including certain non‑EU firms) must identify, prevent, mitigate and account for adverse impacts across their chain of activities.
• Germany’s Supply Chain Due Diligence Act (LkSG) has applied since 1 January 2023 (≥3,000 employees) and from 2024 covers companies with ≥1,000 employees in Germany, with effects cascading to indirect suppliers worldwide.
• EU Deforestation Regulation (EUDR), while subject to phasing changes and a subsequent one‑year delay announced in 2025, still points buyers toward farm‑level traceability for commodities such as cocoa—central to West African exporters. (We note the Commission’s evolving timeline and reporting requirements.)
• On the continent, the Johannesburg Stock Exchange (JSE) published sustainability and climate disclosure guidance aligned with ISSB/TCFD and tailored to South African conditions—useful context for local issuers and their supplier engagement.
• Data protection laws—South Africa’s POPIA, Nigeria’s Data Protection Act 2023 (NDPA) and Kenya’s Data Protection Act 2019—set hard boundaries for collecting and processing personal data during supplier monitoring and worker‑voice programmes.

That stack of rules changes the procurement question from “do you have a policy?” to “can you evidence performance?” Alternative data helps answer.

2. Why alternative data, and why Africa?

Three realities shape supplier scoring on the continent:

1. Data deserts and uneven digitisation. Many suppliers—especially SMEs and informal operators—lack polished sustainability reports; address data and corporate identities can be inconsistent across registries. Alternative data provides triangulation rather than a single point of truth.

2. Material risks are spatial. Deforestation, water stress, illegal fishing and gas flaring are geographical phenomena. Satellites and AIS (ship tracking) can observe them directly, often faster than audits can.

3. Regulatory pull from export markets. Cocoa, timber, palm oil, rubber and coffee face EUDR due diligence; EU and German due‑diligence laws will pressure buyers to maintain credible, continuous oversight of supplier impacts.

Alternative data does not replace on‑site engagement or certification; it prioritises where to look, provides independent corroboration and surfaces leading indicators rather than lagging paperwork.

3. A practical taxonomy of alternative data for supplier scoring

(A) Geospatial/remote sensing

• Deforestation and land‑use change. Global Forest Watch and WRI datasets help quantify forest loss near supplier sites and, for cocoa, estimate future risk and farm distributions in Côte d’Ivoire and Ghana.
• Methane and gas flaring. World Bank/NOAA’s Global Gas Flaring Tracker and emerging methane‑sensing missions (e.g., MethaneSAT; GHGSat; Sentinel‑5P/TROPOMI analyses) spotlight emissions around oil, gas and waste facilities.
• Air quality and NOx/SO₂ hotspots near mines and smelters. Peer‑reviewed work shows how Sentinel‑5P can see pollutant signatures across the Copperbelt and industrial regions.
• Night‑time lights (NTL) as activity proxies. Decades of research show NTL correlates with economic activity; it can be cautiously used to proxy operating intensity at industrial sites when combined with other data.

(B) Trade, shipping and fisheries

• AIS data for illegal, unreported and unregulated (IUU) fishing risks around African waters via Global Fishing Watch insights.

(C) Public registries and corporate identity

• Beneficial ownership. Nigeria has enacted an open beneficial ownership register under CAMA/PSC reforms (now advanced by NDPA institutions); Ghana and Kenya operate BO regimes—vital for de‑risking shell suppliers.
• Open Supply Hub (formerly Open Apparel Registry) consolidates facility identities and geographies, often covering African manufacturing clusters.

(D) Sanctions and watchlists

• UN, EU and OFAC consolidated lists provide structured data (XML/CSV) for screening supplier entities and owners.

(E) Open contracting and public procurement

• OCDS feeds from Ghana PPA, Kenya PPRA and Nigeria BPP can flag suppliers with public‑sector performance history, awards and amendments—useful for integrity checks and delivery reliability.

(F) Event and controversy datasets

• News and event scraping via the GDELT Project (global media metadata) can surface local incidents tied to supplier names/regions; ACLED contributes conflict/location risk overlays.

(G) Worker‑voice and grievance signals

• Mobile, multilingual worker‑voice tools (e.g., survey and grievance hotlines trialled in West African cocoa and plantations) generate continuous social risk indicators beyond sporadic audits.

(H) Risk lists and sector guidance

• For social risk baselining, the ILO’s forced‑labour estimates and the US DoL’s list of goods made with child or forced labour (e.g., cocoa in Côte d’Ivoire and Ghana) provide context to calibrate social risk priors.

4. From pixels to performance: turning signals into metrics

Environmental (E)

• Deforestation proximity score: for agricultural suppliers, compute the proportion of their farm or sourcing polygon overlapping Global Forest Watch alerts in the past 12–24 months, normalised by regional baselines. High overlap → red flag; zero overlap + strong buffer zones → green.
• Flaring/methane intensity: for oil & gas, compare VIIRS‑derived flaring volumes near operated assets to peer medians; spike detection over rolling windows weights recency. (Nigeria remains among top global flarers, making this a priority metric for buyers and lenders engaging the sector.)
• Air quality co‑exposure: for mining/metals, use Sentinel‑5P NO₂/SO₂ anomalies to adjust site‑level risk where pollutant plumes consistently exceed local background, indicating possible emissions management gaps.

Social (S)

• Worker‑voice sentiment and case closure rates: normalise response rates, issue severity and closure timeliness from grievance systems; weight outcomes higher than participation alone.
• Child/forced labour risk overlays: where commodities appear on structured risk lists, increase prior risk and lower the threshold for on‑site validation and remediation planning.

Governance (G)

• Identity resolution and beneficial ownership: assign higher governance scores where the supplier’s beneficial owners are declared in jurisdictional registers and screen clean against UN/EU/OFAC. Missing or opaque ownership lowers confidence and triggers enhanced due diligence.
• Public procurement integrity: analyse OCDS award and amendment patterns (e.g., frequent single‑bid awards, repeated extensions) as soft signals of process risk—never determinative, but useful triage.

Caveat: Many signals are proxies. For instance, night‑time lights correlate with activity but do not directly measure energy efficiency or labour practices; use NTL only as a cross‑check with on‑site and financial data.

5. A scoring architecture that works for African supply bases

A robust supplier ESG score should be:

1. Risk‑anchored and outcome‑oriented. Start with a baseline risk prior by sector and geography (using SASB/ISSB industry topics, ILO/DoL lists, conflict overlays). Update continuously with observations.

2. Modular. Keep data layers independent (identity, E, S, G) so a weak identity match doesn’t drown out strong environmental performance—and vice versa.

3. Explainable. Every subscores’ inputs, weights, and thresholds must be auditable (think internal model card).

4. Confidence‑weighted. Combine Score and Confidence so buyers see both performance and evidence quality.

Illustrative framework

• Identity & Eligibility Gate (pass/fail + confidence): Entity resolution (legal name, registration, geocode), beneficial ownership match, sanctions screen. Confidence depends on registry match quality and document freshness.
• Baseline Risk Prior (0–100):-
  – Sector (SASB/ISSB materiality guidance), Geography (deforestation/methane/conflict), Commodity‑specific social risk.
• Observed Performance Modules:
  – E: Deforestation proximity, methane/flaring intensity, air quality anomalies, water‑stress proxy (regional data), environmental fines or incidents (news/GDELT).
  – S: Worker‑voice outcomes, injury rates (if available), recruitment fee/ID retention indicators, grievance case severity & remediation.
  – G: BO transparency, sanctions clean, procurement integrity signals, audit participation and responsiveness.
• Aggregation:
  – Compute Adjusted ESG Score = 0.35E + 0.35S + 0.30*G, then map through a materiality vector (per industry) so, for example, E carries more weight in forestry and S in apparel/agriculture. (Use ISSB/SASB industry guidance as the source of those vectors; explain deviation.)
  – Compute Confidence Index (0–1) from source diversity, recency, method quality (e.g., peer‑reviewed vs. blog), and cross‑source agreement.
• Risk‑to‑Action Tiers:
  – Green (≥70, high confidence): Maintain; sample audits.
  – Amber (50–69 or low confidence): Enhance due diligence; set corrective action plans.
  – Red (<50 or sanctions hit): Suspend onboarding; escalate to remediation or disengagement as per CSDDD/UNGP.

6. Case vignettes: what alternative data reveals

Vignette 1: Cocoa cooperative, Ashanti–Western corridor (Ghana)
A buyer seeks EUDR‑readiness with a cooperative supplying beans. Supplier paperwork is immaculate, but the score’s E module flags elevated deforestation proximity in the past 18 months. Overlay with sector‑specific research and the Cocoa Deforestation Risk Assessment dataset increases the prior risk. The S module shows good worker‑voice participation but some unresolved grievance cases related to wage delays. The G module is positive on ownership transparency and sanctions. The outcome: Amber—engage to polygon‑map farms, strengthen agroforestry practices and remediate payment issues before expanding volumes.

Vignette 2: Mid‑stream oil services vendor, Niger Delta (Nigeria)
Identity is clean; BO records exist. But E module shows persistently high flaring near sites and occasional methane plume detections from public sources. News scanning notes a minor spill reported locally; no regulator fines recorded. Recommendation: Amber/Red depending on remediation commitments—tie contract renewal to measurable reductions evidenced by satellite time series.

These vignettes show how alternative data prompts earlier, targeted conversations—before risk crystallises into regulatory breaches or reputational damage.

7. Doing it legally and ethically

Alternative data collection intersects with privacy and rights. Keep three guardrails:

• Purpose limitation & lawful basis. If collecting worker‑voice data in South Africa, Nigeria or Kenya, document lawful basis, minimise data and provide clear consent/opt‑out paths per POPIA, NDPA and Kenya DPA. Appoint an Information/Data Protection Officer where required and conduct DPIAs for new processing.
• No surveillance by stealth. Worker‑voice tools must be voluntary, anonymous where possible, multilingual and zero‑cost to workers. Transparency to suppliers is essential; focus on remediation, not punishment.
• Traceability ≠ blame. Under UNGP and OECD guidance, due diligence is about identifying and addressing impacts, not box‑ticking or abrupt disengagement that harms communities. Document actions taken and support suppliers to improve.

8. Building the scoring system: a 12‑step blueprint

1. Define scope and materiality. Use ISSB/SASB to pinpoint what matters for each supplier category.

2. Map identities. Harmonise names, registration numbers and geocodes. Pull beneficial ownership where available (Nigeria, Ghana, Kenya), note gaps.

3. Screen lists. UN/EU/OFAC lists monthly (or via APIs) with fuzzy matching to catch transliterations.

4. Assemble geospatial layers. Deforestation alerts, protected areas, water stress, flaring/methane, air pollutant hotspots. (Document sources and update cadence.)

5. Connect OCDS and public procurement feeds. Look for delivery risk signals (late completions, amendments).

6. Stand up worker‑voice/grievance channels for priority tiers; set KPIs such as response rates and case closure times.

7. Ingest news/event streams. GDELT for media mentions; ACLED to flag conflict proximity.

8. Design the score + confidence. Weight by industry materiality; keep human‑in‑the‑loop review for flags.

9. Governance & controls. Maintain a model card; log every data source, transformation and threshold. Audit quarterly.

10. Supplier transparency. Share score drivers with suppliers and co‑create corrective actions; embed into contracts and incentives (price premia for verified improvements).

11. Legal/ethics checks. Run POPIA/NDPA/DPA compliance assessments; store personal data locally when required; minimal retention.

12. Iterate. Compare scores to audit outcomes and incident logs to calibrate and de‑bias.

9. Special notes for commodity hotspots

Cocoa (Côte d’Ivoire & Ghana).

• Use polygon mapping of farms and cooperative boundaries; overlay with WRI/CFI datasets and Trase cocoa flow maps to assess company‑level exposure to deforestation and traceability gaps. With EUDR phasing shifting, keep a living compliance roadmap; invest in farmer support to avoid exclusion.

Fisheries (West & East Africa).

• Cross‑reference supplier vessels’ AIS patterns (loitering near marine protected areas, “going dark”) with port calls and licences; use Global Fishing Watch research to set thresholds for investigation.

Oil & gas.

• Track flaring volumes and methane plumes as leading indicators; engage suppliers on measured reductions and public disclosure plans aligned to climate transition.

10. Common pitfalls—and how to avoid them

• Confusing correlation with causation. A deforestation alert near a supplier doesn’t automatically implicate them—design verification steps and right‑of‑reply. (EUDR practice expects geolocation, not guilt by proximity.)
• Over‑reliance on one source. Blend at least three independent sources per module where possible (e.g., satellite + registry + worker‑voice).
• Static scoring. ESG risks are dynamic. Refresh geospatial layers monthly during harvesting seasons; event datasets weekly; sanctions daily for critical vendors.
• Privacy missteps. Never harvest personal data from social media without lawful basis. Keep worker‑voice datasets anonymised; segregate PII and limit retention per local law.
• Perverse incentives. Scoring should not punish smallholders for transparency; offer improvement pathways and support (agroforestry, traceability, grievance mechanisms). This aligns with UNGP/OECD expectations.

11. What “good” looks like in 12–18 months

• Coverage: 90%+ of tier‑1 suppliers with identity resolved and confidence ≥0.8; tier‑2 mapping underway for high‑risk commodities.
• Responsiveness: Median time from event flag (e.g., local news of protest/spill) to supplier engagement <10 business days.
• Measurable outcomes: e.g., 30% reduction in flaring intensity at contracted sites; 75% grievance case closure within 30 days; verified zero illegal deforestation in sourcing polygons.
• Assurance‑ready evidence: Traceable datasets, versioned analysis notebooks, and a standing explanation of how ISSB industry guidance informed weights and thresholds.

12. The African advantage

Africa’s “late mover” position in enterprise systems can be an advantage: many buyers can leapfrog legacy ESG processes by starting digital and geospatial‑first, then adding human verification where it counts. Open infrastructure is improving quickly—Open Supply Hub for facilities, OCDS feeds for procurement transparency, and beneficial ownership reforms across the region. Combined, these enable practical, fair, and cost‑effective supplier scoring—rooted in real‑world performance rather than paperwork alone.

Appendix: indicative data sources to seed your programme

• Standards & guidance: ISSB IFRS S1/S2 + SASB industry guidance; JSE Sustainability Disclosure Guidance (South Africa).
• Environment: Global Forest Watch; World Bank/NOAA Gas Flaring Tracker; Sentinel‑5P/TROPOMI studies; Global Fishing Watch.
• Social risk context: ILO forced‑labour estimates; US DoL (TVPRA) list; sector initiatives’ traceability work in cocoa.
• Governance: Open Ownership country pages; UN/EU/OFAC sanctions lists; OCDS portals in Ghana, Kenya, Nigeria.
• News & events: GDELT (media metadata); ACLED (conflict).

Closing thought

Supplier ESG scoring is moving from forms to facts. For African buyers and global firms sourcing from the continent, the winning play is to combine alternative data with grounded, respectful engagement. Use satellites and registries to find the signal; use human relationships to act on it. That is what credible due diligence—and resilient, inclusive growth—looks like.