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Carbon Pool Demand Analytics

Python

Identify the best candidates for new carbon pools by comparing off-chain demand with available on-chain supply

Screenshots

Carbon Pool Demand Analytics screenshot 1
Carbon Pool Demand Analytics screenshot 2

Problem Statement

The current carbon pools on-chain (BCT, NCT, MCO2, UBO, NBO) do not offer sufficient differentiation to allow proper pricing of carbon tonnage being consumed off-chain. We aim to identify clusters of demand for legitimate offsetting in the off-chain market, and scope out pool candidates that have sufficient volume of credits to be liquid, yet are differentiated enough from the current pools to justify separate liquidity.

Solution

We are working with the standard Python data science stack (pandas, Plotly). Our project combines off-chain data from the Verra registry, as well as on-chain data from a Bridged Carbon Subgraph and a manually labeled dataset. We are building on top of the Toucan Protocol and within the broader on-chain carbon markets spurred by KlimaDAO.

Hackathon

ETHAmsterdam

2024

Prizes

  • 🏆

    📈 Toucan —🥈 Protocol Improvement or Extension

Contributors