Upright Launches Climate Risk Tool Spanning 2026 to 2100

Upright has launched a physical and transition climate risk assessment capability on its platform, letting companies and investors quantify climate exposure across operations, supply chains and portfolios without submitting additional questionnaires or data requests. The tool draws on global climate science datasets, including NASA NEX-GDDP-CMIP6, ISIMIP, WRI Aqueduct Floods and the STORM cyclone dataset, mapped against IPCC climate scenarios, and connects that data to company-level information on what a business produces, where it operates and how its value chain functions. Physical risk can be assessed for individual years from 2026 through 2100 across three IPCC warming scenarios, while transition risk is assessed by mapping a company's products and services against Upright's proprietary taxonomy to produce site- and product-level results.

 

The launch extends Upright's existing sustainability risk analysis business into procurement and wider supplier and value-chain risk assessment, following what the company describes as customer demand to consolidate a fragmented set of point solutions into a single platform. For companies, the tool is intended to support climate risk reporting compliance, screen supplier bases without sending questionnaires, and identify which sites or suppliers carry the greatest exposure to direct engagement, mitigation or dual-sourcing decisions. For investors, it is designed to screen climate exposure across public and private portfolios and assess new holdings or targets without requesting additional data from the companies involved.

 

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"We've spent years mapping companies to their products and value chains and translating sustainability exposure into financial terms, so adding product-level, financial-grade climate risk was a no-brainer when customers were increasingly asking us for it," said Annu Nieminen, founder and CEO of Upright.

 

"The sustainability market is under pressure from every direction: regulation keeps shifting, budgets are tighter, and AI is changing what customers expect from software," Nieminen said. "We've decided to lean into that rather than defend the old way of working. Let machines do the data production, make the underlying evidence traceable, and use the same infrastructure for more of the work."