Carbdown Greenhouse Experiment 2025/2026
Embracing Variation Over Replication in Enhanced Weathering
In our collective effort to fight local weather change, enhanced weathering (EW)—a course of involving spreading crushed minerals on soils to naturally seize carbon dioxide—holds promising potential. But, as our recent experiments have demonstratedsuccess in carbon dioxide elimination (CDR) by way of enhanced weathering critically depends upon discovering the optimum mixture of rock kind and soil.
In our upcoming new Carbdown Greenhouse Experiment 2025/2026, we’re considerably shifting our strategy. Slightly than extensively replicating a smaller variety of soil-rock mixtures, we have now chosen to intentionally prioritize testing a broader variety of mixtures, every with fewer replicates. This strategic pivot goals to maximise the variety of soil sorts and rock amendments assessed, responding to our earlier findings that highlighted the dramatic variability in CDR outcomes relying on particular soil-rock interactions.
Our earlier Carbdown experiments underscored that many theoretically promising mixtures resulted in unexpectedly low CDR charges, even below optimized greenhouse situations. For instance, basalt, generally thought to be a dependable rock for EW, confirmed disappointing efficiency in quite a few soil contexts (all soils had been from Germany, although). Conversely, extremely reactive supplies like metal slag demonstrated vital variability in efficiency relying on soil kind. These insights underline a crucial realization: reaching dependable carbon elimination by enhanced weathering calls for an in depth understanding of the nuanced interaction between particular soils and rock amendments. And you then additionally want a correct measurement strategy to achieve dependable MRV.
Our upcoming experiment will make use of tons of of recent soil-rock variations, with just one replicate per therapy, enormously increasing the breadth of our dataset. This design aligns carefully with sensible useful resource and house constraints and leverages our data-intensive strategy to allow superior predictive modeling by way of numerical and machine-learning strategies. Our final purpose shouldn’t be merely to determine profitable mixtures but in addition to systematically remove ineffective ones, thus streamlining future sensible deployments.
Are you curious about diving deeper into our reasoning behind prioritizing variation over replication and exploring how this progressive strategy may reshape the way forward for enhanced weathering analysis?
