geopolrisk-py review
A focused GeoPolRisk library for supply-risk calculations, not general statistical analysis.
Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
geopolrisk-py is a Python library that applies the GeoPolRisk method to raw-material criticality and supply-risk analysis. It is aimed at researchers and analysts working with life-cycle assessment, comparative risk analysis, or company-specific trade exposure. Users specify materials, countries or economic units, and years; calculations draw on bundled mining production, trade, and governance data. This is a specialist analytical tool, not a general-purpose statistics package, so it suits work centered on supply risk rather than broad statistical exploration.
The library focuses on concentration and trade measures. It calculates production concentration using the Herfindahl-Hirschman Index and estimates import risk from mapped trade data, alongside GeoPolRisk scores and characterization factors. For company-specific assessments, trade data can be supplied in Excel files, and users can define custom regions. Mapping and harmonization utilities cover raw materials, countries, and trade codes. Those capabilities make it relevant where assessments must connect company trade records with standardized geopolitical and production information; they do not extend into general hypothesis tests, regression models, or Bayesian analysis.
As open-source software with a free plan, geopolrisk-py can be used on Linux, Windows, and macOS in a self-hosted setup. Results can be exported to Excel and stored in SQLite, while characterization factors can be exported for import into openLCA. The listed support channels are community and email. Buyers seeking a narrowly defined supply-risk workflow and these output paths may find the scope a good fit. Analysts who need a broad statistical workbench or conventional modeling procedures should choose a more general package instead.
geopolrisk-py pros and cons
- Where it wins
- Calculates GeoPolRisk scores and characterization factors by material, country, and year
- Supports company-specific assessments from Excel trade data and custom regions
- Exports to Excel, SQLite, and openLCA-importable characterization factors
- Where it doesn't
- Focused on raw-material supply risk rather than general statistical analysis
- Does not provide general hypothesis-testing, regression, or Bayesian procedures
- Requires Python and user-managed setup across desktop platforms
geopolrisk-py fact sheet, pricing and score →
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