Tabellab
Corporate Finance & Valuation Intermediate

ESG Ratings Methodology: Indicators, Normalisation, Weights & Scores

Build an ESG rating the way analysts do: check data coverage, normalise raw indicators (min-max, z-score, percentile rank), flip lower-is-better metrics, roll them up into E, S and G pillars with sector materiality weights, deduct controversies, map scores to letter grades and test how weights and a second method change the result. Fictional companies, educational only, not investment advice; no affiliation with any rating provider.

8 lessons 8 labs 2 h Certificate

The first lesson is free โ€” no account needed.

About this course

An ESG ratings methodology course that builds an ESG rating from raw indicators for a set of fictional companies in a spreadsheet, for ESG and sustainability analysts, investment students, investor relations staff and anyone who wants to know how a score is made. You measure data coverage and fill one gap with a median estimate, rescale indicators with min-max, z-score and percentile rank and see what an outlier does, flip lower-is-better metrics so that 100 always means best, average indicators into E, S and G pillars and weight them into one score, look up sector materiality weights, deduct controversy points with a floor at zero, map scores to letter grades with an approximate lookup, and test how a second set of weights and a second method move the ranks with CORREL and a scatter chart. A simplified illustration with no affiliation with any rating provider. Educational only, not investment advice. The first lab is free.

Lessons

  1. Raw ESG Indicators & Data Coverage: Counting What Is Really ReportedLab14 minFree
  2. Normalisation: Min-Max, Z-Score and Percentile RankLab15 min Pro
  3. Direction: Turning Lower-Is-Better Indicators into ScoresLab14 min Pro
  4. Pillar Scores: Averaging Indicators into E, S and G, Then WeightingLab15 min Pro
  5. Industry Materiality: Sector Weights with a LookupLab15 min Pro
  6. Controversy Deductions: Penalties, a Floor at Zero and a WatchlistLab14 min Pro
  7. Letter Grades: Mapping Scores with an Approximate VLOOKUPLab14 min Pro
  8. Weight Sensitivity & Rating Disagreement: Ranks, CORREL and a ScatterLab17 min Pro