Sustain ESG Lab
Signals ranked by momentum, risk, and change across 5,000+ listed companies — each translated into one of four actions.
What's moving
The biggest signal shifts in the last 30 days. Each row shows what changed, why, and what to do.
Board-level governance issue flagged. Incidents +45% vs last quarter.
3 quarters without improvement. Controversy 2.1× sector avg.
3Q positive momentum → upgraded from Improving to Leader.
Stable → Improving. Climate commitments tightening vs last quarter.
Biggest 12-month improvers
Top names gaining ESG score over the last 12 months.
| # | Company | Sector | ESG | Action | 12m trend |
|---|---|---|---|---|---|
| 1 | Siemens AG SIE.DE | Industrials | 78 | Add | +14 |
| 2 | Schneider Electric SU.PA | Industrials | 74 | Add | +11 |
| 3 | Unilever plc ULVR.L | Consumer Staples | 71 | Add | +9 |
| 4 | AstraZeneca plc AZN.L | Healthcare | 69 | Watch | +8 |
| 5 | RELX plc REL.L | Media | 66 | Watch | +7 |
Dig deeper
Five structured modules — each answers a specific portfolio or research question.
ESG Leaders
01Top-scoring companies across the universe — the highest-conviction ESG positions.
Use when: building a positive-tilt portfolio.
Risk & Exclusion Monitor
02Companies with high controversy exposure — flagged for exclusion or close watch.
Use when: running exclusion screens.
Momentum Signals
03Who's getting better, who's getting worse — and how fast.
Use when: spotting changes before they're priced in.
Exposure Map
04ESG risk by sector and country — where exposure is concentrated.
Use when: making allocation and country-weight decisions.
Portfolio Impact
05See your portfolio's ESG score, what drives it, and which swaps would improve it.
Use when: stress-testing portfolio construction with what-if scenarios.
Free and open
All analytics published openly. No paywall, no login, no tracking.
Transparent by design
Every score, threshold, and limitation is documented. Nothing is a black box.
Built for decisions
Portfolio managers, researchers, and allocators. Transparent methodology, open access.
Where these signals come from
Our signal engine in 5 steps — entity resolution, event normalisation, cross-source validation.
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This platform does not overlay third-party ESG datasets. It runs a structured transformation pipeline that resolves fragmented entity data, normalises inconsistent event signals, and validates across sources before any score is constructed. The value is in the issuer-level data engine — not the raw inputs.
Source ingestion
Live event streams, climate commitment registries, issuer identity databases. No single source is treated as authoritative.
Entity resolution
Names, tickers, ISINs, LEIs reconciled into one issuer graph. 70,000+ records, duplicates collapsed.
Event normalisation
Incidents classified by severity and pillar. Duplicates deduplicated across sources. Temporal alignment.
Cross-source validation
Claims tested across sources. Commitments cross-referenced with incident patterns. Contradictions flagged.
Signal construction
Quality, Risk, Momentum layers computed independently on validated data. State classification and transition detection produce decision-ready outputs — not raw scores.
How to read this page
Every signal translates to one of five actions. Plain-English labels sit alongside the underlying analyst state from our pipeline.
Signals
What's moved in the last 30 days across 5,000+ listed companies — filtered, ranked, and action-tagged.
How to read state transitions
The five transition types that our pipeline tracks and what each signal means.
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Analytics
Five structured modules — open any to explore the latest outputs.
ESG Leaders
01Top-scoring companies across the universe — the highest-conviction ESG positions.
Use when: building a positive-tilt portfolio.
Risk & Exclusion Monitor
02Companies with high controversy exposure — flagged for exclusion or close watch.
Use when: running exclusion screens.
Momentum Signals
03Who's getting better, who's getting worse — and how fast.
Use when: spotting changes before they price in.
Exposure Map
04ESG risk by sector and country — where exposure is concentrated.
Use when: making allocation decisions.
Portfolio Impact
05Portfolio ESG score, drivers, and what-if swaps — test substitutions before you make them.
Use when: stress-testing portfolio construction.
Momentum Signals
Who's getting better, who's getting worse — and how fast.
Biggest improvers (12m)
Showing 5 of 15| # | Company | Sector | ESG | Action | 12m |
|---|---|---|---|---|---|
| 1 | Siemens AG | Industrials | 78 | Add | +14 |
| 2 | Schneider Electric | Industrials | 74 | Add | +11 |
| 3 | Unilever plc | Cons. Staples | 71 | Add | +9 |
| 4 | Vestas Wind Systems | Industrials | 70 | Add | +8 |
| 5 | AstraZeneca plc | Healthcare | 69 | Watch | +8 |
| 6 | ABB Ltd | Industrials | 68 | Add | +6 |
| 7 | RELX plc | Media | 66 | Watch | +7 |
| 8 | GSK plc | Healthcare | 64 | Watch | +6 |
| 9 | Nestlé | Cons. Staples | 62 | Watch | +5 |
| 10 | Ørsted | Utilities | 75 | Add | +5 |
| 11 | SAP SE | Technology | 67 | Watch | +5 |
| 12 | Novo Nordisk | Healthcare | 68 | Watch | +4 |
| 13 | ASML Holding | Technology | 65 | Watch | +4 |
| 14 | Linde plc | Materials | 63 | Watch | +3 |
| 15 | Iberdrola | Utilities | 72 | Add | +3 |
Biggest decliners (12m)
Showing 5 of 15| # | Company | Sector | ESG | Action | 12m |
|---|---|---|---|---|---|
| 1 | BP plc | Energy | 28 | Avoid | −12 |
| 2 | Glencore plc | Materials | 31 | Avoid | −10 |
| 3 | Shell plc | Energy | 33 | Avoid | −9 |
| 4 | Rio Tinto | Materials | 36 | Trim | −7 |
| 5 | TotalEnergies | Energy | 34 | Avoid | −6 |
| 6 | ExxonMobil | Energy | 30 | Avoid | −5 |
| 7 | Chevron | Energy | 32 | Avoid | −5 |
| 8 | HSBC Holdings | Financials | 48 | Trim | −4 |
| 9 | Anglo American | Materials | 38 | Trim | −4 |
| 10 | Equinor | Energy | 45 | Trim | −3 |
| 11 | ArcelorMittal | Materials | 39 | Trim | −3 |
| 12 | Saudi Aramco | Energy | 29 | Avoid | −3 |
| 13 | Barclays | Financials | 50 | Trim | −2 |
| 14 | Petrobras | Energy | 35 | Avoid | −2 |
| 15 | Vale S.A. | Materials | 37 | Trim | −2 |
Portfolio check
Paste up to 50 tickers or upload a CSV. Get a composite ESG score, action breakdown, and EU SFDR alignment estimate.
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Signal history — last 90 days
Methodology
A transformation pipeline for ESG decision-making — not data aggregation.
Source ingestion
Raw feeds from live event streams, climate commitment registries, and issuer identity databases. No single source is treated as authoritative.
Entity resolution & issuer mapping
Company names, tickers, ISINs, and LEIs reconciled into a single issuer graph. Duplicates, subsidiaries, and name variants collapsed to canonical entities across 70,000+ records.
Event normalisation
Incidents classified by severity, pillar, and type. Duplicate events deduplicated across sources. Temporal alignment prevents double-counting at different lag windows.
Cross-source validation
Claims from one source tested against others. Climate commitments cross-referenced with incident patterns. Entity-level consistency checks flag contradictions before scoring.
Signal construction
Quality, Risk, and Momentum layers computed independently on validated data. Each entity receives a continuous 0–100 ESG score:
No binary pass/fail thresholds. Evidence-strength language throughout.
State classification
Companies assigned to dynamic states — Leader, Improving, Stable, Deteriorating, Excluded. Transitions between states are the primary analytical output.
Sector adjustments
Oil & Gas and Mining subject to a hard cap of 45 on composite scores. Coal and Tobacco capped at 35. These caps apply regardless of other inputs.
Time horizon separation
Short-term (risk shocks), medium-term (momentum), long-term (structural quality). Prevents short-term volatility from distorting long-term positioning.
Score thresholds
Band cuts at 65 / 57 / 50 / 43. These map to Leader / Improving / Stable / Deteriorating / Excluded, and to plain-English actions Add / Watch / Hold / Trim / Avoid.
Limitations
Incident data has inherent media coverage bias. Scores reflect reported controversy exposure, not comprehensive ESG performance. Updated quarterly. SFDR is treated as a disclosure regime, not a scoring system. This platform does not constitute investment advice.
What is Sustain ESG Lab
An institutional analytics platform that transforms fragmented ESG data into decision-ready signals for investors, researchers, and wealth managers.
What we're building
ESG ratings as currently consumed are static, opaque, and slow to react. Our mission is to rebuild them as a transformation pipeline — entity resolution, event normalisation, and cross-source validation that turns fragmented data into clear analyst-grade signals. We don't aggregate; we transform.
Where we're headed
A market where every portfolio manager, researcher, and wealth advisor has access to the same quality of ESG signal that quant funds spend millions to produce internally — translated into decisions, not data dumps. Coverage at 5,000+ listed companies today, with a path to physical climate risk integration and private-market extensions ahead.
Operating principles
Raw data sources have known biases. We normalise, validate, and reconcile across sources before producing any signal.
Every signal carries an analyst label (Overweight / Positive Bias / Neutral / Monitor / Underweight) and a plain-English action (Add / Watch / Hold / Trim / Avoid). Same signal, two registers.
Score thresholds, sector caps, and time horizons are documented and stable. We publish how we score before we tell you what we score.
Media-coverage bias, sector-specific data gaps, and the difference between exclusion screens and SFDR classification — we say so. Loudly.
Talk to us
For institutional access, research collaborations, or data partnerships: