Décarbonisation
Steelldy Indices
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1.1 Proprietary Multidimensional Benchmark Evaluating the Integrity, Durability, and Liquidity of Carbon Credits
The Climate Credit Quality Index (CCQI) developed by STEELLDY is a next-generation proprietary benchmark specifically designed to address the transparency and standardization gaps characterizing the voluntary carbon credit market. Unlike traditional indices, which are limited to price aggregations or transaction volumes, the CCQI integrates a multidimensional assessment of the intrinsic quality of carbon credits, covering three fundamental areas: environmental integrity (verifiability of emission reductions, project additionality, permanence of sequestration), social and ecological sustainability (co-benefits for local communities, biodiversity, respect for land rights), and market liquidity (market depth, transaction frequency, quality of counterparties).
The CCQI is fundamentally different from the CCQI (Carbon Credit Quality Initiative) led by Environmental Defense Fund, Oeko-Institut, and WWF-US, which proposes a qualitative and binary assessment of credit quality based on rigorous scientific criteria but without integrating real-time market data. The STEELLDY index, on the other hand, is designed for integration into algorithmic trading and portfolio management systems, with continuous updating and fine granularity that allow its use as an explanatory variable in quantitative risk models. This distinction is crucial for the stochastic modeling that is the subject of this report: it is the STEELLDY version of the CCQI, with its established correlation with carbon prices and its sensitivity to fiscal risk factors, that constitutes the relevant predictor of the fiscal risk-adjusted performance of the PCCs.
1.2 Optimal Weighting of Quality Factors via 52-Signal Cross-Validation and Bayesian Methodology
The construction of the CCQI index is based on an advanced statistical methodology that combines cross-validation on 52 distinct signals with a Bayesian weighting approach. The 52 signals integrated into the model cover all dimensions of carbon credit quality: |i| verification and monitoring metrics (satellite, IoT, third-party audit reports), |ii| regulatory risk indicators (alignment with CORSIA standards, Article 6 of the Paris Agreement, national regulatory developments), |iii| market risk factors (historical volatility, correlation with energy prices, liquidity spreads), and |iv| composite ESG criteria (environmental, social, governance scores of the emitting project).
The Bayesian component of the methodology offers a decisive advantage for integration into fiscal risk models: it allows for incremental updating of the index weights as new information becomes available, with explicit quantification of the uncertainty associated with each estimate. In the context of Pillar Two, where tax rules are evolving rapidly and jurisprudence is still in its infancy, this ability to integrate new information with a probabilistic measure of confidence is particularly valuable. Cross-validation ensures that the weighting model is not overfitted to historical data and retains its predictive capability in unobserved market regimes, such as those induced by the systematic application of the top-up tax.
1.3 Correlation with the ICE EUA Index (ρ = 0.78) as a Market Robustness Indicator
A major external validation element for the CCQI index lies in its high correlation with the ICE EUA (European Union Allowances) index, the benchmark for EU emissions allowances. The correlation coefficient of 0.78 established between the two indices gives the CCQI credible market grounding, while preserving its specificity compared to the voluntary carbon credit market. This substantial but imperfect correlation (ρ < 1) indicates that the CCQI captures risk and return factors specific to the voluntary market—particularly the quality and integrity of credits—that are not fully reflected in the prices of regulated EUA allowances.
