Arabesque: Arabesque S-Ray® Score Layer

Arabesque S-Ray allows investors to measure the sustainability of thousands of corporations worldwide.


The Arabesque S-Ray® Score Layer data feed provides four lenses for analysis on the sustainability of the world’s largest corporations by way of the GC Score, ESG Score, Preferences Filter, and Temperature™ Score. The Feature layer data feed serves as the building blocks for the GC and ESG scores and provides even more insight into a company’s sustainability performance on 22 well-defined sustainability topics.

Through big data and machine learning, these scores, covering over 8,000 companies, combine over 200 ESG metrics with news signals from over 30,000 sources across 170 countries. The Temperature score is built from publicly disclosed greenhouse gas emissions data and covers over 3,100 companies.

– Gain a deeper understanding of the potential reputational risk facing a company. The GC Score provides a normative assessment of each company based on the principles of the United Nations Global Compact (Human Rights, Labor Rights, Environment and Anti-Corruption).
– Identify companies that are better positioned to outperform over the long run. The ESG Score is based on a sector specific analysis of each company’s performance on financially material environmental, social, and governance (ESG) issues.
– Assess how corporations report on greenhouse gas emissions and contribute to global temperature rise through the Temperature™ Score. All three scores can be used with the Preference Filter, which provides a better understanding of each company’s business activities through analysis of revenue streams.

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About the Provider:

Founded in 2013 following a management buyout from Barclays Bank, Arabesque has been built in cooperation with leading academics from universities including Harvard, Stanford, Oxford and Cambridge. Their proprietary technology, Arabesque S-Ray®, allows investors to measure the sustainability of thousands of companies worldwide. The tool applies a quantitative and algorithmic approach to ESG data, empowering all stakeholders to make better decisions for a more sustainable future.

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