An ESG score aims to gauge a company’s performance on ESG issues and exposure to ESG-related risks. Typically, analysts calculate the score against a set of metrics and express that on a number scale or through a letter ranking system. Although experts are developing comprehensive rating methods, ESG ratings can help companies manage sustainability-related risks or opportunities and provide a structured framework to approach corporate sustainability initiatives.
Though the popularity of ESG ratings has accelerated significantly over the past few years, so has the cynicism — primarily due to lack of complete transparency and knowledge — around their merit in driving rational investment decisions. Some label ESG ratings as a system fraught with subjective methodology; others point toward its incapacity to predict and integrate ESG signals into a final score on a near/real-time basis. Some territories are even viewing ESG ratings as a Greenwashing on select parameters.
Data is the king, but challenges remain!
Although we cannot debate the importance of data in ESG investing, corporates need more transparency to make the right choices. Here are a few of the emerging challenges related to ESG performance benchmarking and ratings:
Garbage in, garbage out: Driven by corporate reporting standards and the different taxonomies and methodologies used by data providers, the quality and consistency of ESG data is currently the biggest challenge.
Like other investment strategies, the “garbage in, garbage out” concept also applies to green investing. Stakeholders and investors demand baseline, standardized data to support relevance, objectivity and comparability. However, at present, they are receiving fragmented data from multiple sources, such as company reports, news articles, vendors and rating agencies. In order to put an end to greenwashing and enable investors to make informed, precise and transparent decisions, ESG data integration must evolve quickly.
ESG reporting needs to mature and have the same rigor and relevance as financial disclosures so that investors can understand the economic impact of ESG strategies and targets.
Lack of transparency in data aggregation: Understanding data inputs, assumptions and limitations are essential to understanding results. For example, some rating firms “overweight” particular ESG themes. Enterprise will need to determine whether these themes are material for them. Some rating firms also calibrate their score with sentiment analysis, while others do not.
Lack of correlation between ESG scores: With investors trying to compare like-for-like, data consistency is another big challenge. There is an argument that standardization of scoring methodologies is not always appropriate since different enterprises will face different materiality of risk.
Lack of available ESG data: This is a major challenge for both data providers and evaluators. There is disparity across industries, with better quality data available for higher carbon sectors, such as oil and gas, and a lack of data for other sectors, such as agriculture and forestry. The latter have not been focusing heavily on CO2 output, but they must work to catch up in this new landscape. Data sets for less material sectors are being developed but are still immature.
Another challenge is to ascribe a score based on the past year’s performance while making sense of long-term commitments from companies that state their intent to become net-zero by 2050.
This is a brave new world, and newer systems and organizations are merging to form the International Sustainability Standards Board (ISSB), which might bring more clarity. The Securities and Exchange Board of India (SEBI) is pushing companies toward greater disclosure in India and plans to keep a close eye on ESG ratings.
Greater and open disclosure will be the key to building a new and transparent world.
The article was first published on businessinsider.in
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