Riskthinking.AI launches Climate Risk Commons™

Free access to enterprise-grade Climate Digital Twin software analytics and data for Students and Researchers Worldwide.

TORONTO, ON, CANADA, June 25, 2026 /EINPresswire.com/ -- -EXECUTIVE SUMMARY
Climate Risk Commons™ is an open technical standard for physical climate-risk analysis. It defines the shared contracts, reference pipeline, APIs, SDKs, data and tooling that enable climate hazards, physical assets, and impact functions to interoperate - so analyses built by different teams on different data can be compared, reproduced, and audited. Its core specification, the Open Climate Risk Framework (OCRF), provides the field with a single, coherent architecture, and anyone can run the Commons end-to-end on open reference data.

The problem it solves is fragmentation. Climate-risk analysis is fragmented across data vendors, asset taxonomies, hazard models, impact functions, and reporting workflows. Researchers cannot easily compare outputs, audit assumptions, or combine sources, and each group rebuilds the same plumbing before asking a new question. As disclosure, adaptation, portfolio risk, infrastructure resilience, and insurance applications mature, the field needs a common technical layer to build on rather than around. The Commons is that layer.

Riskthinking.AI is opening access to universities and research institutions because that is where standards are tested, extended, and trusted. A shared foundation makes research comparable and reproducible; open infrastructure provides the same enterprise-grade tools to a single lab that the world’s largest institutions rely on; and the open framework lets researchers contribute their own hazards, asset data, and impact functions rather than inherit someone else’s. The result is a genuine common- and a jump-start for Climate

-FULL RELEASE
Riskthinking.AI, the climate risk analytics company founded by Dr. Ron Dembo, today announced free access to its Climate Digital Twin (CDT™) for universities, research institutions and scientists worldwide. Through CDTexpress, researchers gain direct access to the same enterprise-grade, bank-level infrastructure Riskthinking.AI provides to the world’s largest financial institutions - at no cost for non-commercial research.

It is the same infrastructure built for the world’s top 100 banks, offered on identical terms to a small, underfunded research group in a developing economy.

-THE PROBLEM: NO COMMON STANDARD, NO ACCESS
There is no shortage of open data sources and tools on climate risk. Universities, government agencies and private vendors each maintain their own hazard models, scenario sets and resolution standards, with little consistency between them.
And even where the data exists, the infrastructure to compute on it at scale - the kind tier-one banks take for granted - has remained locked behind enterprise budgets. For a researcher without one, the tools are often as hard to access as the science is hard to do.

“Climate data was never short on volume. It was short on a common standard, and that is what keeps researchers locked out.”
- Dr. Ron Dembo, CEO & Founder, Riskthinking.AI

-THE RESPONSE: BANK-GRADE INFRASTRUCTURE, OPEN TO ALL
Through CDTexpress, researchers access the full Climate Digital Twin, built on a single standardized data model: over 50 climate hazards modelled, 241 billion geospatially aligned global locations across 193 countries, all IPCC emissions scenarios, and 15 time horizons running to 2100.

But access on its own is not the point. On the same engine, researchers can hold their work to a common standard - comparing data and experiments that were never comparable - and upload and work with the thousands of open-source datasets that grow by the day, building their own pricing models and impact functions rather than accepting someone else’s. Riskthinking.AI calls the underlying approach Climate 2.0: stochastic, multi-hazard, coherent across hazards and countries, and built for the extreme tail, not just the average case.

-TOWARD A CLIMATE RISK COMMONS
Riskthinking.AI describes the program as a contribution to a “climate risk commons”: a shared, openly accessible pool of standardized data, analytics, and computing infrastructure that researchers can build on collectively, instead of each institution rebuilding or buying its own tools. Putting the same tools in everyone’s hands turns isolated efforts into a true commons.

The company points to applications such as flood and rainfall risk assessment for agricultural regions, or hazard exposure analysis for informal settlements, where comparable data and tools have historically been either priced beyond reach or technically out of reach. Riskthinking.AI says the aim is to jump-start Climate 2.0 analysis in a world still working in Climate 1.0 - and to do it everywhere at once, not only where budgets already allow.

“The barrier to good climate research was never the science. It was access. CDT gives any university researcher the same standardized pipeline our commercial clients use, built on a single methodology rather than five. That is what a climate common signifies in practice.”
- Dr. Ron Dembo, CEO & Founder, Riskthinking.AI

-ACCESS
Researchers at universities and other research institutions, as well as certain not-for-profit organizations, anywhere in the world can request CDT access by contacting Riskthinking.AI directly. Institutions that join the Climate Risk Commons can arrange automatic enrolment for all their bona fide students, researchers, and faculty. They can add and replace data sets, build and add their own models, and design and add their own damage functions.

“If this data only serves the institutions that can already afford it, we have not solved the climate risk problem. We have just priced some people out of knowing it.”
- Dr. Ron Dembo, CEO & Founder, Riskthinking.AI

ABOUT RISKTHINKING.AI
Riskthinking.AI has built the Climate Digital Twin (CDT™), a CLIMATE 2.0, stochastic, multi-hazard platform for measuring, pricing and managing physical climate risk.
While conventional models apply a single deterministic scenario, the CDT runs thousands of distinct climate futures simultaneously, producing a full probability distribution rather than a point estimate. Clients include banks, insurers, asset managers, and financial data providers responding to a rising regulatory floor for climate risk disclosure.

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