BeZero Carbon’s next generation of dynamic baselines
Geospatial and Earth Observation team
BeZero Carbon has developed state-of-the-art, statistically matched dynamic baselines to assess what might have happened in a carbon project area in the absence of carbon finance. We are integrating this next generation of dynamic baselines with our carbon ratings and platform, currently for Avoided Unplanned Deforestation (AUD) projects, with other project types to follow.
Dr Aoibheann Brady, Senior Geostatistical Scientist at BeZero, introduces how we use statistically matched dynamic baselines to overcome the challenges of assessing counterfactual baseline scenarios.
Statistical matching uses remote sensing and other geospatial data to pair map pixels in the project area with control pixels in the wider landscape. The rationale is that paired pixels should be similar in all relevant respects, except for carbon finance - analogous to case-controls in a clinical drug trial.
We use satellite monitoring to dynamically track and compare carbon emissions in the matched pairs through time. This informs the risk of over-crediting, specifically where this risk is driven by the project’s baseline assumption, against which carbon credits are issued.
We use a two step matching procedure: (1) land stratification, matching lands by ownership, protection status, concessions and indigenous reserves, restricted by distance, and excluding other carbon projects in the landscape; and (2) statistical matching within strata, pairing pixels according to all other control variables relevant to the project, such as accessibility by road or river, and distance to recent deforestation. There is no perfect set of control pixels, however, so we repeat the matching process many times in a bootstrapping framework to quantify uncertainty.
Our previous generation of baseline assessment was similarly dynamic. For every AUD project on our platform, we have assessed changes in the project’s reference region (where applicable) and in buffer zones around the project area over time, accounting for administrative units, other carbon projects, conservation areas and human pressures.
The major development in this next generation of dynamic baselines lies in the sophistication of how we constrain and select control pixels, and how we determine confidence intervals around our results. We build on statistical frameworks pioneered in the medical sciences, scaling these for application to big spatial data (tens of millions of pixels), and combining them with machine learning and expert knowledge, to rigorously assess baseline assumptions project by project.
Important aspects of our new dynamic baselines product, compared with other third-party assessments include: (1) careful consideration and parameterisation of factors relevant for deforestation at the project level, determined through extensive review of project documents, wider literature, and machine learning; (2) BeZero’s proprietary algorithms for statistical matching and quantification of uncertainty; and, crucially (3) nuanced, analyst-led interpretation of outputs and the weight that we give these in our risk-based analysis of carbon credit quality.