Sustainability Scoring Model for SME Suppliers
Saudi SME suppliers are being asked to report carbon data to their buyers. Most have no tool to do it. Nasun scores them from operational data they already have, and connects that score to financing incentives in existing Saudi supply chain infrastructure.
About Nasun
Saudi Arabia's largest industrial buyers are under growing pressure to report the carbon emissions of their entire supply chains. That pressure flows directly to the thousands of SMEs supplying them, companies expected to produce sustainability data as a condition of keeping their contracts, yet with no affordable tool to generate it.
Nasun addresses this with a machine-learning scoring model that estimates a supplier's sustainability performance using basic operational data (sector, energy spend, fleet size, headcount) and outputs a verified score tied to financing incentives. Higher-scoring suppliers access better financing rates through existing supply chain financing infrastructure.
Sustainability-linked supply chain finance is proven globally, moving billions annually across major international supply chains. Every existing platform, however, relies on formal ESG rating agencies that do not cover small suppliers in data-scarce markets. Nasun is built specifically for that gap, the first sustainability scoring tool designed for Saudi SMEs, developed under institutional research support at KAUST.
Market Opportunity
1
The Problem
Global sustainability regulations are tightening fast. Large industrial buyers across Saudi Arabia are now required to account for the carbon emissions of their entire supply chains, creating a compliance demand that flows directly to every SME supplier beneath them. For these suppliers, the stakes are simple: produce sustainability data or lose the contract.
2
The Gap
Globally, sustainability-linked supply chain finance platforms move billions of dollars annually by tying supplier financing rates to verified ESG scores. Every existing platform, however, relies on formal ESG rating agencies that require structured reporting infrastructure to function. Small and mid-size suppliers in emerging markets, including Saudi Arabia, fall entirely outside that coverage. No tool exists today that is designed for data-scarce suppliers in the Gulf.
Simultaneously, Saudi SMEs face an $80 billion financing gap, with the majority of micro and small enterprises underserved by traditional lenders.
3
Our Solution
Nasun fills both gaps with one mechanism. Our machine-learning model estimates a supplier's sustainability performance from basic operational inputs and outputs a verified score. That score integrates directly into existing Saudi supply chain financing infrastructure, where it determines financing rates. Greener suppliers get cheaper capital. Buyers get verified supply chain sustainability data. The financing channel gets a qualified, sustainability-screened supplier base.
The Scoring Model
INPUT
-
Sector & Industry
-
Energy & Fuel Spend
-
Fleet Size
-
Headcount
-
Certifications
-
Free-text description
ML MODEL
1. Applies the GHG Protocol formula to calculate Scope 1 + Scope 2 emissions
2. A linear regression model estimates emissions from company inputs
3. Benchmarks the result against sector-average emissions, plus a capped bonus for certifications held
4. An NLP layer reads any free-text description for qualitative context (shown alongside the score, not used to change it)
OUTPUT
A
Green, preferential rate
B
Standard rate
C
Improvement pathway
Score + Financing Rate
Key Results
Synthetic dataset calibrated against real Saudi sector emissions data (EDGAR, Climate Transparency). Not yet validated on real supplier data.
Model accuracy on held-out test data
R² = 0.986 | MAE = 4,848 kg CO₂e
Average emissions by sector
200 synthetic suppliers
Institutional Partnerships
The Team
Contact and Request a Demo