New Sustainable1 dataset flags corporate conduct assessed against UNGC principles
LONDON and NEW YORK, June 12, 2026 /PRNewswire/ -- S&P Global Energy, the leading independent provider of information, data, analysis, benchmark prices and workflow solutions for the commodities, energy expansion and energy transition markets, today announced that S&P Global Sustainable1 has launched the United Nations Global Compact (UNGC) Screening Dataset.1
The dataset is a one-stop screening solution that provides a structured method to help investment managers, bankers and non-financial corporates assess whether companies are aligned with the 10 UNGC Principles. S&P Global Sustainable1 has applied the UNGC Screening Dataset to a proprietary list of 16,500 companies globally and expects to cover an estimated 24,000.
This dataset combines two evidence streams that enable customers to identify potential misalignment across human rights, labor, environment and anti-corruption principles, providing companies with timely and critical oversight of evolving risks:
- Controversy Screening: Tracks corporate controversies linked to one or more UNGC principles.
- Business Involvement Screening: Flags corporate revenues originating from specific controversial products.
"When investors evaluate portfolio risk, understanding any controversies companies are involved in can be a critical step. To help investors understand these risks, we have launched the S&P Global Sustainable1 UNGC Screening Dataset. This comprehensive, foundational tool identifies corporate conduct assessed against UNGC principles and better informs investment decisions," said Thomas Yagel, Head of Sustainable1 at S&P Global Energy. "The UNGC Screening Dataset provides clear and actionable UNGC alignment labels, enabling investors to integrate S&P Global Sustainable1 insights into their decision-making, portfolio construction and ongoing risk oversight."
Leveraging S&P Global's?AI capabilities, this new dataset utilizes proprietary AI and machine learning?models?to systematically?identify, classify, and quantify ESG and?business?risks.?The AI models continuously screen?millions of public sources globally - across news, NGOs, regulators, and other stakeholders - in multiple languages to detect emerging risk incidents in?real time. These?AI-driven?insights are then?validated?and contextualized by a dedicated Controversy Research team within Sustainable1, strengthening accuracy, consistency and decision-ready?risk indicators for investors.
Using this dataset, S&P Global Sustainable1 published a white paper, "How S&P Global data helps investors navigate the risks of corporate controversies", which found that misalignment in companies is most frequently linked to human rights-related controversies. The concentration of cases tied to human rights - and, to a lesser extent, environmental impacts and corruption - provides indicators for investors as to where they may face the most persistent and material sources of controversy-driven risk. By integrating these risk indicators into portfolio construction and ongoing oversight, investors can more consistently identify elevated exposure; engage companies from a clearer baseline of evidence and strengthen accountability expectations aligned with the UNGC principles.
Details of the UNGC Screening Dataset are available here. Read the full white paper here.
For more information on S&P Global products and solutions, please reach out to our teams here.
1 The UN is not affiliated with, does not endorse, and has not reviewed the S&P Global Sustainable1 UNGC Screening Dataset.
Media Contact
Kathleen Tanzy
S&P Global Energy, Americas/EMEA
+1 917-331-4607
kathleen.tanzy@spglobal.com
About S&P Global Energy
At S&P Global Energy, our comprehensive view of global energy and commodities markets enables our customers to make superior decisions and create long-term, sustainable value. Our four core capabilities are: Platts for pricing and news; CERA for research and advisory; Horizons for energy expansion and sustainability solutions; and Events for industry collaboration.
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