Sovereign ESG revisited
The current sovereign environmental, social and governance (ESG) scoring framework is far from perfect. Disparate methodologies and assessment criteria have created different mixed sets of Environmental scores (E scores), resulting in difficulties when comparing and contrasting country rankings across ESG providers.
What constitutes good and meaningful environmental assessment remains unclear, however, and it remains questionable whether assessment is being done accurately, rigorously and transparently.
The World Bank’s 2021 report “Demystifying Sovereign ESG” examines these issues in the first-ever empirically based assessment of the product offerings of seven of the industry’s leading sovereign ESG providers, including FTSE Russell/Beyond Ratings.
The World Bank study provides an empirically based assessment of sovereign ESG as a sector, the way leading sovereign ESG providers compare and contrast with each other and the way their respective sovereign ESG products contribute to the industry’s increasing demand for being able to measure sustainability within different investments.
This paper introduces comprehensive enhancements to the Environmental Pillar Score of the FTSE Russell Sovereign Risk Methodology, which is designed to measure financially material risk from ESG factors for sovereign issuers with data available for 151 countries from 1999 onwards.
The improvements focus on:
- Better integration of forward-looking climate risks, including temperature alignment and physical risk.
- Enhanced data coverage and quality of the underlying metrics.
- A wider distribution of scores to better differentiate between high performers and laggards.
- Adjustments to eliminate income bias in Environmental scores.
These enhancements respond to various challenges with regards to sovereign ESG metrics that have been identified in comparative research conducted by the World Bank. They include a lack of transparency and harmonisation, high correlations with income level and a sometimes-significant lag in the availability of data.
