As the voluntary carbon market continues to attract increased attention, activity, and funding, there’s an acute need for more and better data on the projects that issue credits.
After all, as companies look to invest hundreds of thousands, if not millions, of dollars into their carbon offsetting (and wider sustainability) efforts, it’s worthwhile to have the best data on the projects that are available to them.
To that end, we’ve aggregated data on nearly 20,000 projects and over 275,000 retirement transactions in order to provide the best overview of the market. We’ve augmented that data with pricing, information on brokers and corporates, as well as inputs from our partners like BeZero, Emsurge, and AirCarbon Exchange.
In order to go several steps beyond, we’ve begun to tap into the wealth of data that exists in the project design documents (PDDs) and other documents associated with projects. This information is highly valuable to those that are looking into projects — indeed, one analyst in this space described his job as distilling a 250 page PDDs into 5 page overviews.
To date, the only way to do that has been to download the projects manually, and scan through them one-by-one. This allows users to uncover the data they need, but it’s not scalable or efficient.
We have digitized PDDs from Gold Standard and Verra projects, enabling two key functionalities: keyword and phrase searches, and table extraction. Below are some use cases for how these methods may be applied.
A while back, a broker I had spoken with shared their frustration: a client, who sponsored a famous bicycle race, was looking to offset only with projects that had anything to do with bikes. But, how could one find that info?
Looking for the needle in the haystack projects is a great use case for PDD searching. By searching for ‘bicycle’ as a keyword, we were able to identify ~30 projects that may be a fit. Not all projects are as relevant as the Bikes for the Planet project (which was recently auctioned on AirCarbon), but this method allows anyone to create a list of potential projects to engage with as they look for niche or highly sector-specific projects.
For those looking to uncover more technical information about projects, being able to filter through the projects to pull out all relevant ones can save hours of research. That can be something as straightforward as identifying all projects that mention the use of ‘LiDAR’ or ‘remote sensing’, or it can be used to find projects that include a financial analysis as part of their application.
For instance, the screenshot above shows an example result of a project that mentions both ‘IRR’ and ‘NPV’. Those that are looking at setting up their own project and want to see how past projects have modeled growth, or those looking for projects to invest in, will find this type of information invaluable.
Searching for special characters, like @, allows users to find contact info for projects very quickly. Someone looking to better understand the VCM market in Brazilian forestry, for example, would likely want to speak with as many people from as many projects as possible. Searching for @ among all relevant PDDs will pull in multiple contact points for each project, allowing users to quickly identify a list of contacts to engage across multiple projects.
Land use projects that allocate credits to a buffer pool need to explain their risks (or lack thereof), in order to determine the size of the buffer. This data can be found in the Risk Element documentation, or in the PDD, if it’s included. Our table extraction capability allows users to quickly identify which projects have these tables associated with them — by searching only for projects that have specific keywords in their tables — and extract the data for further analysis. This can help analysts identify the projects with the highest (or lowest) natural risk, and input this datapoint into their potential investment or purchasing decisions.
One data point that we have already begun to incorporate into our Premium Dashboard is the annual emissions during project lifetime. Each project reports the average annual emissions reductions / credits, but this can be misleading: projects rarely have a consistent number of credits they generate annually. In some years, they may even generate negative credits. In order to be able to accurately assess the number of credits a project will generate, it’s important to know the actual annual forecasts, which we’ve extracted and begun to incorporate into our database.
It’s also a useful tool to be able to compare project forecasts with what the actual issuances have been year over year. For example, if a project developer consistently overestimates the number of credits their projects will issue, it’s useful to know prior to making any deals with them.
We’ve incorporated this document search and table extraction functionality into our API, so corporate users who are interested in building bespoke analysis on top of our data can do so using the same authentication key, by querying a separate endpoint.
Overall, there are hundreds of uses for the dashboard. If you’re curious to learn how it may be useful to your work, please get in touch with carbon@alliedcrowds.com!