This is how VAI’s engine is expected to evolve, toward more granularity even at the level of the methodology. However, the more granular one goes, the more data is required from transactions and market quotes that match the desired level of granularity.
For instance, in order to train the model to perform valuation for methodology VM0021 from Verra, a statistically significant number of datapoints would be required from projects that apply exactly that methodology.
Part of VAI’s mission is to increase price transparency and thus facilitate liquidity and deployment of capital into the carbon markets. As such, we do see the market evolving in that direction, where there would be abundance of data inputs on various levels of granularity. However, we are not there yet.