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Government of Canada – DTPR AI Register

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AI-Powered CO2 Capture Performance and Cost Prediction

Energy Efficiency · Planning & Decision-making · Research & Development

What it collects

Operational data
Anonymized data
  • Process simulation data, costing data, and life cycle assessment data generated by NRCan. This is technical and operational data about CO2 capture processes, not personal data about individuals.
Run by
Natural Resources Canada (NRCan)
Where
No fixed location
Kept
Not stated by the Helpful Places.
Shared with
Accountable organization
Your copy
You cannot see the data it holds about you. What you can do

What it is for

Energy EfficiencyPlanning & Decision-makingResearch & Development

This tool uses machine learning to predict the performance, cost, and environmental impact of carbon dioxide capture technologies as part of Canada's Carbon Capture, Utilization, and Storage (CCUS) assessment framework. It processes process simulation, costing, and life cycle assessment data generated by Natural Resources Canada to help government employees evaluate CCUS options. The system is used internally by federal employees and is not disclosed to the public as an AI system. There is no direct impact on individual citizens — outputs inform policy and technology planning.

What it collects and what happens to it

Data taken in

Operational data
Anonymized data
  • Process simulation data, costing data, and life cycle assessment data generated by NRCan. This is technical and operational data about CO2 capture processes, not personal data about individuals.

Processing

Classification & Prediction
  • An AutoML (automated machine learning) pipeline that ingests process simulation, costing, and life cycle assessment data and builds a multivariate predictor of CO2 capture performance, cost, and environmental impact properties.

What it does

Deciding (Analytical AI)
Human decides
  • The AutoML pipeline produces multivariate predictions of performance, cost, and life cycle assessment properties. Government of Canada employees receive these predictions and make their own decisions about CCUS technology selection and policy.

Outputs

A recommendation or prediction
Anonymized data
  • Predicted performance metrics, cost estimates, and life cycle assessment scores for CO2 capture technologies. These are advisory outputs for government employees — not binding decisions about individuals.

Run by

Natural Resources Canada (NRCan)
  • Natural Resources Canada (NRCan) is the federal department that developed and operates this tool. NRCan generates the underlying data and deploys the system for use by Government of Canada employees.

Government of Canada AI Register — 2526-NRCan-RNCan-013

Built by

Government of Canada
  • The Government of Canada developed this AI system in-house. No external vendor is identified in the register entry.

Government of Canada AI Register — 2526-NRCan-RNCan-013

Kept for

Not stated by the Helpful Places.

Shared with

Available to the accountable organization
  • Outputs are available to Natural Resources Canada and Government of Canada employees who use the tool for CCUS technology assessment.
Not available to me
  • This system is an internal government tool for GC employees. Members of the public do not interact with it and cannot access its outputs directly. AI use is not disclosed to external users.

Stored

Not stated by the Helpful Places.

How to read the colours

Can it identify you?

Anonymized data
Data about people with the link to who is broken. Stripped of identifiers, blurred, aggregated, or noised so this system can’t reasonably tie a record back to an individual.
Pseudonymous data
Each person’s data is tied to a token (hash, ID, template) that lets this system recognise the same person across events, but the token itself doesn’t reveal a name. Reidentification is possible with extra information.
Identifiable data
The data either contains a direct identifier (name, address, account name, recognisable face or voice, plate number) or carries a token this system uses to look up legal identity during processing.

Who completes the loop?

Human decides
This mode suggests; a person decides what to do next. The AI is always advisory — a human is in the loop on every decision. Example: a triage tool ranks cases for a clinician who chooses which to see first.
Human executes
This mode decides; a person carries out the result. Example: an optimizer plans the day’s trash-collection routes, and drivers run them.
Autonomous
This mode decides and acts on its own. No person reviews each decision or carries out the resulting action.

Definitions from the DTPR standard. Amber is about your data, violet about who decides. The fuller the shape and the deeper the colour, the more identifying the data or the less a person is involved.

What you can do

Ask about this system

Questions go to the Helpful Places, not the vendor.

Your rights

  • Right to Algorithmic TransparencyThe system is used by Government of Canada employees. The register indicates AI use is not disclosed to users. As this system does not make decisions about individual citizens, broad public transparency rights are not directly applicable. Government employees using the system should be aware of its AI nature and consult the AI register entry for technical details.

Risks and safeguards

No risks or safeguards have been published for this system yet.