About a-team Marketing Services
The knowledge platform for the financial technology industry
The knowledge platform for the financial technology industry

A-Team Insight Blogs

Clarity AI, GIST Impact and S&P Launch Biodiversity Tools for Investors

Subscribe to our newsletter

A partnership service between sustainability technology platform Clarity AI and impact data intelligence provider GIST Impact is among new biodiversity tools unveiled for financial institutions in the past week.

They join S&P Global unit Sustainable 1, which has taken the sheets off a new dataset that helps investors track the nature-based impact and dependencies of their portfolios.

New York-based Clarity AI and Nyon, Switzerland-headquartered GIST have partnered to develop a biodiversity impact tool. It combines the former’s database, which covers the ESG performances of more than 30,000 companies, with GIST’s metrics and analytics.

The service will help clients identify and calculate their exposure to companies that have a negative impact on biodiversity.

S&P Sustainable1, meanwhile, has launched its Nature & Biodiversity Risk dataset, which covers 17,000 companies and more than 1.6 million assets. It includes new nature-related risk metrics, among which is a dependency score and ecosystem footprint gauge that gives clients greater visibility into a company or asset’s dependency and impact on nature.

Biodiversity is a fast-growing segment of the ESG investment market, with associated funds attracting more than US$1 billion this year, according to Clarity AI.

“The imperative for investors to account for biodiversity impacts in their decision-making has never been greater,” said Pavan Sukhdev, founder and chief executive of GIST Impact. “This data is critical for effective risk management, and to support action to curb further nature loss.”

S&P said its dataset had already given context to the importance of biodiversity risk mitigation. It said that by applying the data set to the S&P 1200 it found that:

  • 85% of the world’s largest companies have a significant dependency on nature;
  • 46% have at least one asset located in a Key Biodiversity Area (KBA) that could be exposed to future reputational and regulatory risks;
  • S&P 1200 companies used an estimated 22 million hectares of land for their direct operations in 2021 to generate USD28.9 trillion revenue.

This indicates “the critical importance of greater transparency for market participants on nature-related risks and opportunities”, said S&P Global Sustainable1 global head of research and methodology Steve Bullock. “This new dataset signals a maturation of the conversation on nature and provides clear metrics quantifying the nature related dependency and impact of over 1.6 million global real assets.”

Subscribe to our newsletter

Related content

WEBINAR

Recorded Webinar: Market data in the cloud

Over the past several years, the topic of market data in the cloud has been hotly debated – latency has been an issue, which data to put in the cloud has been discussed, and lines have been drawn. But where are we now, and how have the lines been redrawn? This webinar will consider progress...

BLOG

A-Team Group Data Management Awards USA Winners Announced at DMS NYC 2025

A-Team Group has announced the winners of its 4th annual Data Management Insight Awards USA 2025, and we extend our congratulations to the individuals and companies recognised with awards this year. The event shines a light on the top providers of data management solutions, services, and consultancy for the capital markets across the United States....

EVENT

TradingTech Summit London

Now in its 15th year the TradingTech Summit London brings together the European trading technology capital markets industry and examines the latest changes and innovations in trading technology and explores how technology is being deployed to create an edge in sell side and buy side capital markets financial institutions.

GUIDE

The Reference Data Utility Handbook

The potential of a reference data utility model has been discussed for many years, and while early implementations failed to gain traction, the model has now come of age as financial institutions look for new data management models that can solve the challenges of operational cost reduction, improved data quality and regulatory compliance. The multi-tenanted...