Climate Risk Commons Framework

The Climate Risk Commons Framework provides a reusable Rust computation core and typed Python bindings for climate-risk calculations. It turns climate-hazard distributions into comparable impact microscores, scenario branches, and portfolio-level VaR/CVaR risk metrics.

This repository owns the native computation layer. The Python distribution is named crc-framework and imported as crc_framework. It packages the PyO3 extension together with a typed binding layer. Higher-level, application- specific Python functionality is intended to live in a separate package that depends on and may re-export this API.

Features

• Empirical, tabulated, and parametric probability distributions
• Explicit parametric fitting with diagnostics and acceptance constraints
• Built-in and composable exposure-to-impact transforms
• Climate impact registry keyed by risk factor and scenario context
• Microscores that turn distribution quantiles into binary risk outcomes
• Spanning sets, VaR, CVaR, and factor-level attribution
• H3-based IPCC region, continent, and country lookups

Installation

Install the published Python wheel with:

python -m pip install crc-framework

The package requires Python 3.9 or later. Installing a compatible prebuilt wheel does not require a Rust toolchain. Building from source requires Rust 1.85 or later and a C compiler suitable for building Python extensions.

Verify the installation:

python -c "import crc_framework; print(crc_framework.__doc__)"

For local development, create an environment and install the project in editable mode:

python -m venv .venv
.venv/bin/python -m pip install --upgrade pip maturin
.venv/bin/python -m pip install -e ".[test]"

After changing Rust code, rebuild the extension:

.venv/bin/maturin develop

Core concepts

Distributions

All calculations are expressed through a distribution interface with pdf, cdf, ppf, quantiles, and sample operations. Scalar and NumPy array inputs are supported for the evaluation methods.

Choose a distribution according to the form of the available data:

• EmpiricalDistribution for raw observations.
• TabulatedDistribution for explicit probability/value pairs.
• FittedDistribution for a known parametric family and its parameters.
• HurdleDistribution for a point mass followed by a truncated parametric tail.
• PointMassDistribution for a value that is constant at every probability.

TabulatedDistribution does not extrapolate by default. Querying outside its declared probability range raises an error unless extrapolate=True is set.

Probability convention

All metric APIs use non-exceedance probability q in [0, 1]. Return periods are accepted only when constructing a tabulated distribution. For upper-tail hazards, a return period T is converted to q = 1 - 1/T; therefore, the upper-tail 100-year return level is queried at q=0.99.

Fitting

Fitting is always explicit. Metrics sample the distribution supplied by the caller and never silently substitute a fitted curve. This makes the modelling choice auditable.

fit_distribution accepts raw observations or an EmpiricalDistribution and selects among the supported families using the Kolmogorov–Smirnov p-value. The supported families are genextreme, weibull_min, weibull_max, skewnorm, gumbel_r, gumbel_l, and genpareto. The returned FitResult includes KS, RMSE, and R-squared diagnostics. Passing a tabulated or fitted distribution is an error: those values are not independent observations.

For a standalone L-moment estimate of GEV or Gumbel observations, select the estimator explicitly:

from crc_framework import fit_distribution

result = fit_distribution(annual_extremes, family="genextreme", method="lmoments")

This uses unbiased sample L-moments (Hosking, 1990), with an exact numerical solution of the GEV L-skewness equation. It supports genextreme, gumbel_r and gumbel_l, requires at least four finite, nonconstant observations, and performs no likelihood optimization or automatic family selection. GEV shape uses the SciPy convention (c = -xi); the finite-mean domain is c > -1. Degenerate L-skewness is rejected rather than clipped. Fit constraints can accept or reject the estimated distribution without refitting it.

The native implementation sorts once, uses compensated sums in scaled and centered coordinates, and reuses that sort for diagnostics. Work is O(n log n) with O(n) storage. The Python binding releases the GIL during native fitting. The returned KS p-value is a descriptive diagnostic, not a calibrated hypothesis test after parameter estimation. Defaults and the existing sample fitter are unchanged. See NIST's L-moments reference for background and the Hosking references.

Use fit_quantiles for probability/value knots. It minimizes weighted value-space differences between the supplied values and the selected family's PPF, requires an explicit family, and reports residual diagnostics rather than KS statistics:

from crc_framework import TabulatedDistribution, fit_quantiles

curve = TabulatedDistribution.from_return_periods(
    [5, 10, 25, 50, 100],
    [0.2, 0.6, 1.1, 1.5, 1.9],
    tail="upper",
)
fit = fit_quantiles(curve, family="gumbel_r")

For a known point mass, use fit_hurdle_quantiles and supply its probability; the framework does not infer an exact mass from sparse zero-valued knots:

from crc_framework import fit_hurdle_quantiles

fit = fit_hurdle_quantiles(
    curve,
    family="gumbel_r",
    atom_probability=0.5,
    atom_location=0.0,
)

TabulatedDistribution remains the lossless interpolation of the supplied knots. Parametric and hurdle fits are lossy, so systems that require source reconstruction must persist the original probability/value pairs separately.

Create microscores from flood exposure

from crc_framework import (
    RiskFactor,
    ScenarioMetadata,
    TabulatedDistribution,
    TransformContext,
    generate_microscores,
    impacts,
)

exposure = TabulatedDistribution.from_return_periods(
    periods=[10, 20, 50, 100, 200, 500],
    values=[0.1, 0.2, 0.5, 0.9, 1.4, 2.0],
    tail="upper",
)

impact = impacts.for_factor(
    RiskFactor.CFLOOD,
    context=TransformContext(
        continent="Europe",
        building_type="Commercial buildings",
    ),
)(exposure)

suite = generate_microscores(
    exposure,
    impact=impact,
    probabilities=[0.95, 0.99],
    metadata=ScenarioMetadata(factor=RiskFactor.CFLOOD.value),
)

generate_microscores returns a MicroscoreSuite. Use suite.scores to inspect sampled exposure and impact values, suite.at(q) to obtain the binary outcome at a requested probability, and exposure_statistics and impact_statistics for summary diagnostics.

Fit observations explicitly

import numpy as np

from crc_framework import EmpiricalDistribution, FitConstraints, fit_distribution

observations = np.array([0.1, 0.2, 0.4, 0.7, 1.1, 1.8])
empirical = EmpiricalDistribution(observations)
fit = fit_distribution(
    empirical,
    family="auto",
    constraints=FitConstraints(probability=0.99, maximum_value=3.0),
)
median = fit.distribution.ppf(0.5)
print(fit.distribution.family, fit.diagnostics.ks_pvalue, median)

Pass family="auto" (the default) to select a family, a family name to fit a specific family, or use fit_all to inspect every candidate. Use quality_metrics to calculate diagnostics for a selected fitted distribution.

Evaluate impacts

Impact functions expose two deliberately distinct operations:

• impact.evaluate(values, context=...) evaluates event-aligned exposure values. Input shape and order are preserved, including for decreasing or non-monotonic callables. This is the appropriate operation when return periods continue to identify the source hazard events.
• impact(distribution, probabilities=..., context=...) transforms an exposure distribution into an impact distribution. Decreasing built-in transforms reorder the resulting quantiles so the output remains a valid distribution for risk metrics.

The built-in LinearImpact, SigmoidImpact, and PiecewiseLinearImpact support both operations. CallableImpact adapts a vectorized NumPy callable for point evaluation, while the backward-compatible CallableTransform also supports distribution transformation.

import numpy as np

from crc_framework import CallableImpact, LinearImpact, generate_microscores

impact_function = LinearImpact(slope=0.25, maximum=1.0)
event_impacts = impact_function.evaluate(np.array([0.5, 2.0]))
impact = impact_function(exposure)
suite = generate_microscores(
    exposure,
    impact=impact,
    probabilities=[0.95, 0.99],
)

custom = CallableImpact(lambda values: np.clip(values / 2.0, 0.0, 1.0))
custom_event_impacts = custom.evaluate(np.array([0.5, 2.0]))

impacts.for_factor(...) selects a registry-backed climate transform. Supply a TransformContext with the relevant geography, building type, and historical values; pass overrides when an application needs to replace transform parameters. Registry-backed impacts support the same point and distribution interfaces, with point evaluation delegated directly to the native registry.

Aggregate factor outcomes

Each microscore can be converted to a BinaryOutcome. Combine outcomes into the full set of independent downside/upside branches, then calculate VaR and CVaR at one or more confidence levels:

from crc_framework import BinaryOutcome, compute_risk, compute_spanning_set

outcomes = [
    BinaryOutcome("flood", downside_probability=0.05, downside_impact=0.4),
    BinaryOutcome("fire", downside_probability=0.10, downside_impact=0.2),
]

branches = compute_spanning_set(outcomes)
risk = compute_risk(outcomes, levels=[0.80, 0.95])
level_95 = risk.at(0.95)

print(len(branches))  # 4: two possible outcomes per factor
print(level_95.var, level_95.cvar)

Branch generation grows as 2**n for n factors. Set max_branches on compute_spanning_set or compute_risk to enforce an application-specific limit. RiskLevel.attribution reports each factor's contribution to VaR and CVaR.

Spatial helpers

Spatial helpers accept an H3 cell as an integer or string, normalize it to H3 resolution 4, and return None when no reference mapping exists.

from crc_framework import lookup_geography, lookup_ipcc_region

cell = 600550049193132031
geography = lookup_geography(cell)
region = lookup_ipcc_region(cell)

if geography is not None:
    print(geography.continent, geography.countries)
print(region)

lookup_continent returns a continent name, lookup_ipcc_region returns the IPCC region, and lookup_geography returns a Geography object with the continent and intersecting ISO3 country codes.

Public API

The package exports the following primary interfaces:

• Distributions: EmpiricalDistribution, TabulatedDistribution, FittedDistribution, HurdleDistribution, PointMassDistribution, fit_distribution, fit_quantiles, fit_hurdle_quantiles, fit_all, and quality_metrics.
• Transforms: impacts, ImpactRegistry, ImpactFunction, ClimateImpact, LinearImpact, SigmoidImpact, PiecewiseLinearImpact, CallableImpact, and CallableTransform.
• Metrics: generate_microscores, compute_spanning_set, and compute_risk.
• Models and constants: ScenarioMetadata, TransformContext, RiskFactor, Pathway, RISK_FACTORS, PATHWAYS, and HORIZONS.
• Spatial lookup: lookup_continent, lookup_ipcc_region, and lookup_geography.

Package structure

• crates/core contains the reusable Rust algorithms and reference data.
• src/lib.rs exposes the Rust core through PyO3.
• python/crc_framework contains the typed Python binding layer and type information shipped in the wheel.

Consumers should import the stable crc_framework API rather than importing crc_framework._core directly. The _core module is an implementation detail and may change between releases.

License

CRC Framework is licensed under the GNU Affero General Public License, version 3 or any later version (AGPL-3.0-or-later). See LICENSE.

Parts of the numerical distribution implementation are derived from or informed by SciPy and Cephes. Their applicable copyright notices and license terms are preserved in THIRD_PARTY_NOTICES.md.

Development

cargo fmt --all -- --check
cargo clippy --workspace --all-targets -- -D warnings
cargo test --workspace
.venv/bin/python -m mypy
.venv/bin/python -m pytest
.venv/bin/maturin build --release

Run maturin develop again after changing the Rust extension. Python-only changes are available directly from the editable installation.