Riskthinking.ai - Climate Earth Digital Twin (CDT)
Data category
- Environmental data
- Ratings
- Research data
The data offers solutions for:
- Climate scenario analysis
- Environmental impact analysis and insight
- Geospatial/location data
- Investment decisions and portfolio insight
- Physical risk
- Reporting: Impact
Who are the data users?
- Corporates
- Financial institutions
- Government
- Investors
- Trustees
Brief description of the data offering
Flat Files:
Flat files are available for purchase and can be refreshed as needed for clients who simply want to bring raw data into the environment with a minimum of system integration. Files may be purchased for a specific climate risk factor or a specific geographic region as well as a specific scenario or SSP or climatology time horizon.
API:
API-based access allows clients on-demand and near real-time access to both CDT’s physical asset and climate data sets as well as dynamic climate risk scoring capabilities for proprietary physical assets.
CDT In Your Cloud:
A secure deployment pattern allows a copy of Riskthinking.AI’s CDT to be hosted locally within a client owned and operated VPC, providing clients with control over the use of any proprietary data.
Where and how do you source your data?
Riskthinking.AI has introduced a patented and mathematically-consistent approach to simulate the future uncertainty and impacts of climate change. This approach powers our Climate Earth Digital Twin (CDT) and generates all of its data and analytics.
The foundation of our CDT is a clean, structured and completely integrated dataset that enables Riskthinking.AI to consistently apply our methodology across global locations, horizons and risk factors.
The CDT aligns more than two trillion data points across multiple integrated data layers. These layers cover physical assets, climate hazards, economic risks, emissions and socioeconomic data. The following table presents the main data layers in the CDT.
The CDT’s physical asset database currently covers more than 80,000 parent companies, 350,000 subsidiary companies, and 5,000,000 physical assets.
To build this database, Artificial Intelligence (“AI”) and Natural Language Processing (“NLP”) are used to harvest physical assets from publicly available sources, identify the owners, and assign the asset type. Computer vision, applied to publicly available satellite images, is used to model physical asset footprint and size.
For every location on Earth, the Climate Earth Digital Twin covers the uncertainty distributions of more than 20 climate hazards.
To systematically apply our stochastic approach, Riskthinking.AI requires historical and forward-looking uncertainty distributions for multiple climate hazards. These distributions represent the full array of expert projections and their probabilities. It is essential to consider every projection – including the extremes – when measuring the future uncertainty and potential impact of climate change.
The CDT includes significant climate hazard data to fulfill the requirements of our stochastic approach. All source data meets a minimum set of integrity requirements, including that they are published by a peer-reviewed journal (e.g., Nature Climate Change), intergovernmental organisation (e.g. Intergovernmental Panel on Climate Change) or government agency (e.g., National Oceanic and Atmospheric Administration (“NOAA”)).
What is the cost for your data offering?
Flat Files:
Prices starting at $150,000 for a single risk factor and geographic region
API:
Prices for API access are usage based and start at $150,000
CDT In Your Cloud:
Prices for a CDT Clone In Your Cloud are usage based