ESG Risk Decoded
ESG risk assessment aims to measure the environmental, social, and governance (ESG) material factors that will drive long-term financial value in a particular business. Research has proven that the consideration of ESG factors has a positive impact on companies’ market return and on investors’ outcomes.
Clarity AI has developed a methodology designed to assess how ESG issues potentially affect companies and governments’ future financial performance, represented by scores.
Although the measurement of financial risks linked to sustainability is becoming widely known and used in sustainable investing, it faces several challenges:
- ESG data availability is limited and incomplete, with few organizations including non-financial topics in their reports.
- ESG data quality can be poor and its measurement heterogeneous, requiring robust data quality and consistency checks.
- There are multiple perspectives on the financial materiality of ESG Risk issues, resulting in poor correlation among different ESG scores.
Given the complexity of ESG metrics, which are the most granular level of information related to sustainability, the market has proposed different ways of summarizing and aggregating all this information.
The most important and frequent ones are ESG Scores and ESG Ratings. Essentially, these two measures do something very similar. They aggregate all this complexity into a simple final letter or number in an attempt to make certain dimensions of sustainability comparable across companies. ESG Ratings usually are built by analysts and they include personal opinions whereas ESG Scores, on the other hand, are more systematic but fundamentally, they provide the same level of visibility.
-
ESG risk assessment aims to measure the environmental, social, and governance (ESG) material factors that will drive long-term financial value in a particular business. Research has proven that the consideration of ESG factors has a positive impact on companies’ market return and on investors’ outcomes.
Clarity AI has developed a methodology designed to assess how ESG issues potentially affect companies and governments’ future financial performance, represented by scores.
-
Although the measurement of financial risks linked to sustainability is becoming widely known and used in sustainable investing, it faces several challenges:
- ESG data availability is limited and incomplete, with few organizations including non-financial topics in their reports.
- ESG data quality can be poor and its measurement heterogeneous, requiring robust data quality and consistency checks.
- There are multiple perspectives on the financial materiality of ESG Risk issues, resulting in poor correlation among different ESG scores.
-
Given the complexity of ESG metrics, which are the most granular level of information related to sustainability, the market has proposed different ways of summarizing and aggregating all this information.
The most important and frequent ones are ESG Scores and ESG Ratings. Essentially, these two measures do something very similar. They aggregate all this complexity into a simple final letter or number in an attempt to make certain dimensions of sustainability comparable across companies. ESG Ratings usually are built by analysts and they include personal opinions whereas ESG Scores, on the other hand, are more systematic but fundamentally, they provide the same level of visibility.
Assess Your Investments with Custom or Industry-consensus ESG Risk Scores
Leverage SASB standards or create your own scoring profile
- Market-leading coverage and data quality: Ensure the quality of your analysis through accurate and comparable data, powered by Clarity AI’s reliability checks
- Custom scores: Create scoring profiles based on the sustainability issues you want to consider, their materiality, and the methodology to fill in missing data
- Industry-consensus scores: Leverage common score-building methodologies, combined with sustainability issues materiality as defined by the Sustainability Account Standards Board (SASB) and industry references
- Not a black box: Get full transparency for every metric down to the report source, estimation model or news article
Monitor Controversies and Screen Exposures to Enhance Your ESG Risk Analysis
Controversies
Leverage Natural Language Processing (NLP) techniques to get an accurate representation of companies in your portfolio
Exposure Screens
Identify companies that derive revenue from involvement in controversial business activities
ESG Risk Raw Data
Source the underlying data for your ESG Risk analysis, with over 70 indicators across all pillars, both quantitative and qualitative
Research & Insights
-
June 13, 2023 · 28 min readESG Risk
Mastering ESG Metrics, Scores, and Ratings: How to Effectively Build Sustainable Portfolios
Integrating ESG into investment strategies has become the norm, and technology can help democratize access to ESG information.
Learn more -
July 12, 2023 · 13 min readESG Risk
Measuring ESG Risk: ESG Controversies Lead to a 2% to 5% Stock Underperformance after Six Months
Analysis finds correlation between ESG Risks derived from Controversies and market underperformance. Clarity AI controversies predict loss of company value in the mid-term, with an average impact of -5% for high severity...
Learn more -
November 18, 2022 · 2 min readAI
How Advanced Technology Can Increase Data Reliability
Only about 17% of those publicly listed companies report their direct emissions. But those 6,500 companies emit about 74% of the Scope 1 emissions of the full universe of public companies. So while we donʼt have all the...
Learn more