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Due to the nature of OAE’s early stage, research plans and monitoring efforts are still maturing and often changing. The status of measurements as documented here was last reported in June 2025 — changes made to project monitoring plans since then will be updated in a future version of the database.
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Monitoring, reporting, and verification (MRV) refers to the frameworks used to quantify how much CO₂ has been estimated as durably removed through carbon dioxide removal (CDR). For ocean alkalinity enhancement (OAE), both measurements and models — also informed by measurements — form the foundation for MRV.
Early-stage real-world OAE projects are testing the suitability and practical limitations of current modeling and measurement strategies, as laid out in Carbon to Sea’s independent review of the first OAE credits. Assessing the adequacy and accuracy of modeled predictions and monitoring plans is central to evaluating OAE and its uncertainties. This database focuses on what monitoring looks like in practice — which parameters are measured, where and how data are collected, and the purposes those data are intended to serve. It does not ****assess the adequacy of any project’s MRV or validate carbon removal estimates.
However, in the absence of a shared regulatory standard, there remains ambiguity around what monitoring for OAE entails, which scientific uncertainties may have been answered, and how closely perceptions align with actual field practice. Measurement plans are public, but often organized in various formats, bespoke to site conditions, and driven by a project’s academic or private nature. This makes evaluating results, learning, and guiding future efforts difficult.
In 2025, Carbon to Sea conducted a structured MRV landscape analysis — combining a desktop review of public documentation with 37 expert interviews across academia, the private sector, MRV providers, CDR buyers, and project funders. Coming out of this analysis, Carbon to Sea sought to document activities in an effort to benchmark a starting point against which we can track improvements in MRV quality and confidence. Drawing on gracious contributions from project leads, expert interviews, and synthesizing public documents, this database presents a meta-analysis of over 250 measurements indexed across these initial research projects.
This database does not rank or evaluate projects — it documents varying approaches and looks for emerging themes and areas of alignment in order to surface insight and opportunity. We present a meta-analysis of prioritization given each project's opportunities and constraints, not a proxy for safety, efficacy, or scientific value. To enable insights across projects, this database displays percentages of measurements relative to each project's total number of measurements. Importantly, this database maps measurements taken during OAE additions only — it does not yet analyze baseline data collection conducted before alkalinity enhancement begins, but in some instances these overlap with what is documented here.
The database provides the first structured documentation of how monitoring is being practiced and prioritized across these initial OAE field efforts. For the first time, we can see which parameters are measured for OAE field research, how and where data is collected, and for what purpose. This creates a tool for exploring, learning, and planning field research — documenting decision-making, successes, and lessons learned in OAE monitoring over time.
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We are deeply grateful to all participants of the landscape analysis and in particular the field project teams who voluntarily shared their time and insights that contributed to this database, enabling us to look more closely at how first-of-a-kind OAE MRV is being carried out in practice.
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This meta-analysis offers a first structured view into how initial OAE field projects are monitored. It does not intend to prescribe solutions — but to surface patterns, gaps, and areas where alignment is beginning to take shape or, conversely, still needed. Its value lies in enabling learning over time, sharpening questions, and informing future monitoring strategies. Some key takeaways include: