OP here — really appreciate these questions because they get at the real limitations of carbon accounting frameworks.

*1. "Carbon ≠ carbon": different gases, different externalities*

Totally agree. CO₂ mass alone is a simplification. That's why GHG Protocol uses GWP factors to convert different gases into CO₂e: - CH₄: 28–34× CO₂ - N₂O: 265–298× - SF₆/HFCs: 10,000×+

But even GWP misses important dimensions: - Timing effects (short-lived vs. long-lived gases) - Toxicity and pollution - Ozone impacts - Ecosystem and social externalities

So in our system, carbon accounting is just the starting layer. CSRD already forces companies to track water, biodiversity, pollution, and circularity on top of climate (ESRS E2-E5).

*2. Re: ESG ratings not correlating with lower emissions*

Fully agree with the critique. Most ESG scores measure: - Disclosures instead of actual performance - Policies instead of physics - Governance/social weighting that dilutes environmental signals

That's why we avoid "ESG scores" completely. We follow: - Strict GHG Protocol methods - Audit-grade emission-factor calculations - CSRD/BRSR/SEC climate-rule compliance

The 2023 study you cited is exactly why deterministic calculation matters more than ratings.

*3. On porous graphene and carbon-capture edge cases*

This is where things get interesting.

Under GHG Protocol: - Manufacturing the filter → positive emissions (Scope 1/2/3) - Capturing CO₂ → potential removal - But: only counts as removal if storage is permanent (>100 yrs) and third-party verified (e.g., Puro.earth, CDR.fyi) - Temporary use (e.g., carbonation) is not removal—just delayed re-emission

In our accounting model we separate: - Emissions (tCO₂e released) - Avoidance (vs. baseline) - Removals (atmospheric drawdown) - Permanence categories (geological, mineralization, engineered, biomass) - Uncertainty ranges (required under CSRD ESRS E1)

Your graphene example is exactly the type of nuance that standard ESG dashboards usually ignore.

*4. Genuine curiosity*

Do you work in carbon accounting, lifecycle analysis, or climate methodology? Your questions suggest real hands-on experience with the edge cases. We're building Velumin's methodology to handle exactly these scenarios—would love to hear more about your experience if you're open to it.

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*Side note: Still interested in the original topic* — for compliance-heavy systems, I'm trying to understand where experienced engineers think AI architecture review breaks down vs. where it actually outperforms humans (especially in checklist coverage).