2.2) Performance-based assessment

The framework combines several types of indicator assessment:

• Disclosure-based: Assess whether relevant policies, practices and metrics are reported.
• Absolute performance: Compares outcomes against predefined thresholds.
• Relative performance: Compare quantitative outcomes against Materiality Group peers.
• Capping metrics: Cap most Theme Scores at 3 unless critical requirements are met, helping prevent high scores based on disclosure alone and mitigate greenwashing risk.

2.3) Business-specific materiality

Materiality is linked to a company’s business activities rather than only its headline sector. Revenue segments representing more than 10% of total revenues can influence the materiality assessment, allowing diversified companies to be assessed against ESG issues relevant to significant parts of their business.

2.4) Data and methodology characteristics

• 222 scoring indicators selected from more than 900 company-level ESG data points.
• Publicly available company disclosures are used for the core ESG Score.
• Segment-driven materiality considers significant revenue segments representing more than 10% of company revenue.
• Scores are rule-based and deterministic; the score calculation does not use AI.
• Scores and underlying data are refreshed as new disclosures become available, with weekly recalculation.

3) LSEG ESG Scores Plus

LSEG ESG Scores Plus extends the core ESG Score with external risk and impact signals, providing a broader view of a company's sustainability footprint and business model.

• Green Revenues: Exposure to revenues from green products and services.
• Green / ESG Issuance: Exposure to sustainable or ESG-linked debt issuance.
• ESG Controversies: Relevant external controversy signals derived from news sources.
• Sovereign ESG Risk: ESG risk associated with the countries in which a company operates.

Three complementary lenses are available: Risk combines controversies and sovereign exposure; Impact combines green revenues and green issuance; Complete combines all four signals.

4) Which score should I use?

5) Build the migration dataset

The API journey should form the core of the implementation.. The next step is to retrieve the relevant ESG fields through LSEG data services and bring them into the asset manager’s analytical workflow.

Preparation Step

Start with importing necessary libraries and open session.

5.1 ) Define the portfolio

Start with the portfolio universe, including security identifiers and, where relevant, portfolio weights.

Here is how we define the portfolio

5.2) Retrieve legacy ESG Scores

Retrieve the ESG fields currently used in the investment process. This establishes the baseline against which the migration will be assessed.

Here is how we retrieve the legacy data

5.3) Retrieve new Overall and Pillar Scores

Retrieve the new Overall ESG Score together with Environmental, Social and Governance Pillar Scores.

Here is how we retrieve the new scores

5.4) Retrieve Themes and materiality

Add Theme Scores and relevant materiality information. These fields become particularly important when investigating companies whose assessment changes materially.

Here is how we retrieve Themes and materiality

5.5) Retrieve ESG Scores Plus

Add the Risk, Impact and/or Complete dimensions required by the investment strategy.

Here is how we retrieve ESG Scores Plus

5.6) Join and prepare the migration dataset

Bring legacy and new measures into a common issuer-level dataset ready for portfolio analysis.

Implementation should consider:

• Missing observations and differences in coverage.
• Score fiscal year, date and status.
• Differences in historical availability.
• Multiple securities belonging to the same issuer.
• Portfolio weights and aggregation methodology.
• Currency or identifier consistency where relevant.
• Whether comparisons use the same company universe and observation period.

The new LSEG ESG Scores are available from FY2022 onwards, and scores may be flagged as indicative while disclosures are still being completed. Scores are refreshed weekly as new information becomes available.

Here is how we create the clean migration dataset

The crosstab above is a matrix: rows are the legacy score year, columns are the new score year, and each cell counts the companies with that combination. Most companies should sit on the diagonal (same year for both scores); many off-diagonal companies would mean the two scores are not always comparable period-for-period.

We now keep only the companies that have both a legacy and a new Overall ESG Score, and rescale their weights so they sum back to 1 within this smaller set.

6) Run a portfolio migration diagnostic

Once the migration dataset is built, the objective is not simply to compare two averages. The analysis should identify what changed, where it changed and why.

Using a portfolio of approximately 100 companies, the diagnostic could include:

6.1 ) Compare portfolio distributions

Compare the distribution of legacy and new ESG Scores across the same holdings. For visualisation only, legacy scores may be divided by 20 to display a 0–100 legacy score alongside a 0–5 new score on a common chart scale. This is a visualisation technique only. Dividing a legacy score by 20 does not convert it into a new ESG Score or make the two methodologies equivalent.

Here is how we compare score distributions

6.2) Measure ranking changes

Analyse whether companies retain similar relative positions under the two methodologies. Pearson correlation measures the linear relationship between the legacy and new scores, while Spearman correlation measures whether companies retain a similar relative ranking.

A scatter plot and fitted regression line help visualise the relationship. However, a strong correlation does not mean that the scores are equivalent or that legacy values can be converted directly into new LSEG ESG Scores. The two frameworks use different methodologies and scoring scales.

Here is how we analyse the relationship and ranking changes between the scores.

The following scatter plot provides a visual comparison of the legacy and new scores. The fitted line shows the overall linear relationship, while R-squared indicates how closely the observations align with that relationship.

The scatter plot and R-squared value should be used as diagnostic tools only. The fitted line is not a score-conversion formula because the legacy and new scores are based on different methodologies, scoring architectures and scales.

Pearson correlation describes the linear relationship between the two scores, while Spearman rank correlation indicates how consistently company rankings are preserved.

After examining the overall relationship, the quartile analysis shows whether individual companies retain similar relative positions under the new methodology.

The crosstab above is a matrix: rows are the legacy quartile, columns are the new quartile, and each cell is the number of companies in that combination. Numbers on the diagonal are companies that stayed in the same quartile; numbers off the diagonal moved quartile after the transition.

Next, we list the companies that moved into or out of the top and bottom quartiles.

6.3) Identify the largest movers

Identify holdings with the largest differences between their legacy and new assessments. For these companies, drill down from:

Overall Score → Pillar → Theme → indicator → underlying data

This helps determine whether the change is associated with measurable performance, materiality, a specific sustainability Theme or another methodological difference.

Here is how we identify and explain the largest movers

6.4) Identify companies crossing investment thresholds

This is particularly important when using ESG Scores directly in investment rules.

Suppose an existing strategy excludes companies with a legacy ESG Score below 30. The equivalent rule should not automatically become a new ESG Score below 1.5.

Instead:

1. Apply the existing rule to the legacy portfolio.
2. Analyse the distribution of the new ESG Scores for the same universe.
3. Identify holdings whose eligibility changes.
4. Investigate the Pillar, Theme and materiality drivers for those changes.
5. Evaluate alternative thresholds against the strategy’s investment objective.
6. Recalibrate and document the new rule before implementation.

Here is how we test existing portfolio thresholds

The table above compares candidate thresholds on the new score. For each one: Excluded and Weight excluded show how strict the rule would be; Newly excluded are companies that pass the legacy rule but would be excluded under the new one; Newly eligible are companies excluded today that would pass under the new rule. Use this to pick a threshold, rather than assuming 30 / 20 = 1.5.

6.5) Assess portfolio and sector impacts

Move beyond company-level changes to assess whether the migration systematically changes:

• Weighted-average portfolio ESG characteristics.
• Sector and industry exposures.
• Top and bottom ESG exposures.
• Portfolio concentration.
• Exclusions or investable universe.
• Risk and impact characteristics.

Here is how we measure the portfolio impact

Now the same comparison broken down by sector: number of companies, portfolio weight, and average percentile rank under the legacy and new scores. Rank_Change is positive for sectors that moved up in relative ranking after the transition, negative for sectors that moved down.

Finally, we compare the two exclusion rules from section 5.4 on the whole portfolio: how many companies each rule keeps, how much portfolio weight it excludes, and the weighted ESG Scores Plus Risk and Impact of the companies that remain.

7) Explain a score change: worked company example

For one material portfolio mover, the tutorial should demonstrate the full analytical journey.

We pick the company with the largest rank change.

7.1 ) Start with the Overall ESG Score

Compare the company’s legacy assessment with its new Overall Score and establish the magnitude of the change.

7.2) Move to the Pillars

Identify whether Environmental, Social or Governance contributes most strongly to the new assessment.

7.3) Drill into Themes

Determine which of the 12 Themes explain the Pillar result and examine their materiality to the company’s business model.

7.4) Inspect indicators and underlying data

For the most important Theme, identify the disclosure, absolute-performance, relative-performance and/or capping indicators influencing the assessment.

7.5) Add ESG Scores Plus

Finally, assess whether external Risk or Impact signals change the view of the company.

This provides a transparent path from a portfolio-level signal to the underlying sustainability information:
Portfolio → Company → Overall → Pillar → Theme → Indicator → Data → Risk / Impact

8) Use migration to improve the investment model

Migration does not have to mean replacing one ESG variable with another. The new architecture can support different levels of integration.

8.1 ) Simple ESG integration

Use the Overall ESG Score as the primary portfolio-level signal and the Environmental, Social and Governance Pillar Scores for attribution and monitoring.

If the existing investment process applies a minimum legacy ESG Score, the threshold should not be converted directly to the new 0–5 scale. Instead, use the overlapping portfolio data to identify a candidate threshold that preserves approximately the same portfolio coverage as the legacy rule.

The following example uses percentile-based recalibration. It first calculates the proportion of companies passing the legacy threshold and then identifies a candidate LSEG ESG Score threshold that selects a similar proportion of the portfolio.

The candidate threshold preserves approximately the same company-level portfolio coverage as the legacy rule. In other words, it selects a similar proportion of companies, but not necessarily the same companies or the same proportion of portfolio weight. The candidate threshold is an analytical starting point and should not be interpreted as a direct conversion or a methodologically equivalent replacement for the legacy threshold. Before selecting the final production threshold, review the companies moving into and out of scope, together with their portfolio weights and sector distribution.

8.2) Custom sustainability strategy

Use Theme Scores and underlying indicators to construct an investment signal aligned with the strategy’s specific sustainability objectives. For example, a climate-focused strategy may place greater emphasis on Climate Transition and Energy & Resource Use, while a social strategy may focus on Human Rights & Community or Labour Relations.

Theme selection and weighting should be defined by the investment objective rather than inferred from the legacy Category Scores. Legacy Categories and the new Themes are not one-to-one replacements.

When constructing a custom signal, consider:

• whether each Theme is material to the company’s business activities;
• how non-material and missing Theme values are handled;
• whether weights are fixed across the portfolio or vary with company materiality;
• whether the signal is used for ranking, screening or portfolio construction; and
• how the custom methodology will be documented and monitored.

8.3) Separate management quality, risk and impact

Use the core LSEG ESG Score to assess how a company manages material ESG issues, and use ESG Scores Plus Risk and Impact as separate analytical overlays.

Keeping these dimensions separate helps avoid interpreting management quality, external ESG risk and positive sustainability impact as the same concept. For example, two companies with similar core ESG Scores may have different Risk or Impact profiles.

The overlays can be used to flag companies for further review, support portfolio monitoring or provide additional inputs to an investment model. The appropriate use and weighting should be defined by the investment objective rather than automatically combined into a single custom score.

Differences in Risk and Impact within the same core-score group show why these dimensions should remain visible as separate overlays.

Depending on the investment objective, an overlay may be used for monitoring, additional due diligence, ranking or portfolio constraints. The results should not automatically be combined into a custom composite score without a clearly defined methodology and governance process.

9) Practical migration checklist

Before moving an investment process into production:

• Map every legacy ESG field currently used to its intended role in the new framework.
• Retrieve overlapping legacy and new data for a consistent investment universe.
• Review coverage, missing data, dates and historical availability.
• Compare score distributions and portfolio rankings.
• Identify the largest company-level movers.
• Investigate movers using Pillars, Themes, materiality, indicators and underlying data.
• Test existing exclusions, thresholds and portfolio rules.
• Recalibrate thresholds using portfolio analysis rather than translating legacy values mechanically.
• Assess portfolio, sector and investable-universe impacts.
• Decide whether Themes or ESG Scores Plus can improve the existing investment model.
• Document methodology and model changes for governance purposes.
• Run legacy and new approaches in parallel, where appropriate, before production cutover.

Moving from score replacement to model migration

The transition to the new LSEG ESG Scores should therefore be treated as a model migration rather than a score conversion.

For asset managers, the opportunity is not only to reproduce an existing workflow using a new field. The redesigned architecture provides a more granular way to understand management quality, material sustainability issues, measurable performance, external ESG risk and positive impact, allowing clients to determine which dimensions are relevant to their investment strategy and how they should influence portfolio decisions.

The practical migration process therefore becomes:

retrieve → map → compare → diagnose → recalibrate → enhance → implement

Methodology reference: LSEG ESG Scores Methodology, July 2026.

Conclusion

Migrating to the new LSEG ESG Scores framework is more than a simple field replacement exercise. The introduction of business-specific materiality, enhanced performance measures, and ESG Scores Plus can lead to meaningful differences in how companies and portfolios are assessed. As a result, asset managers should take the opportunity to understand the drivers behind score changes rather than rely solely on direct comparisons with legacy scores.

By following a structured migration workflow, from retrieving legacy and new ESG datasets to analyzing score movements, materiality impacts, and portfolio-level effects, firms can gain greater transparency into the transition and make informed adjustments to their investment processes. The additional perspectives provided by ESG Scores Plus can further enrich sustainability assessments by incorporating risk and impact considerations alongside traditional ESG analysis.

Using Python and the LSEG Data Library, these migration and validation exercises can be integrated into existing portfolio analytics workflows, helping investment teams evaluate the implications of the new framework at scale. Ultimately, a well-planned migration enables asset managers to move beyond score mapping and towards a deeper understanding of the sustainability characteristics that influence portfolio allocation decisions.