Toucan receives fees when liquid carbon reference tokens are redeemed for carbon credits and when off-chain carbon credits are bridged on-chain (revenue).

Toucan is an infrastructure protocol that bundles illiquid carbon credits to liquid carbon reference tokens, and bridges off-chain carbon credits on-chain. Toucan is currently built on Polygon and was founded by Raphaël Haupt in 2020.

Toucan team & investors own and govern the protocol. Toucan’s investors include USV, Earthshot Ventures, etc.

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Infrastructure

Toucan Protocol

# Toucan Protocol Active users (monthly) Methodology

## 1. Overview

The Toucan Protocol Monthly Active Users (MAU) metric measures the number of unique users who redeemed carbon credits within rolling 30-day periods. This metric provides insights into user engagement with Toucan's carbon credit infrastructure across Polygon and Celo blockchains. The calculation uses HyperLogLog sketches for efficient approximate distinct counting and provides breakdowns by chain, user type, and product dimensions.

## 2. Data sources

The metric is calculated from the following blockchain data sources:

- **Carbon credit redemption events**: Parsed `BCT_Redeemed` and `NCT_Redeemed` events from Toucan v1 protocol contracts on Polygon and Celo blockchains
- **Blockchain transaction data**: Block timestamps and transaction metadata from Polygon and Celo networks
- **Event parameters**: User account addresses extracted from redemption event parameters (`account` field for redeemers)

The data focuses specifically on carbon credit redemption activities, where users redeem Base Carbon Tonnes (BCT) and Nature Carbon Tonnes (NCT) tokens.

## 3. Step-by-step calculation

### Component A: User extraction and daily aggregation
- **Purpose**: Extract unique daily active users from carbon credit redemption events
- **Logic**: Parse `BCT_Redeemed` and `NCT_Redeemed` events from Toucan v1 contracts on Polygon and Celo blockchains. Extract the `account` parameter from each redemption event, which identifies the user performing the redemption. Truncate block timestamps to day granularity and identify unique user addresses per day. Classify all users as 'Redeemer' user type and categorize under 'infrastructure' market sector
- **Key fields**: `block_timestamp` truncated to day, `user_address` from event `account` parameter, `chain` identifier, `user_type` as 'Redeemer'

### Component B: HyperLogLog sketch creation
- **Purpose**: Create efficient data structures for approximate distinct counting across time windows
- **Logic**: Transform daily user data into HyperLogLog sketches with precision parameter 15. Group data by timestamp, chain, user type, and product dimensions. Initialize HLL sketches from the daily unique user addresses to enable efficient merging across time windows
- **Key fields**: `hll` sketches containing user addresses, grouped by `timestamp`, `chain_id`, `user_type`, `product_id`

### Component C: Rolling window construction
- **Purpose**: Create 30-day rolling windows for MAU calculation
- **Logic**: Generate a complete datespine covering all days from the minimum timestamp in the data to current date minus one day. For each date in the datespine, create a 30-day window by generating timestamps for the target date plus the previous 29 days. Join the window dates with the daily HLL sketches to collect all relevant user data for each 30-day period
- **Key fields**: `timestamp` as the end date of each 30-day window, `joinday` representing each day within the window

### Component D: Dimensional breakdowns
- **Purpose**: Calculate MAU across different analytical dimensions
- **Logic**: Aggregate HLL sketches within each 30-day window and break down results by four dimensions. Project-level aggregation merges all HLL sketches regardless of chain, user type, or product. Chain-level aggregation groups by `chain_id` only. User type aggregation groups by `user_type` only. Product aggregation groups by `product_id` only. For each breakdown, merge HLL sketches using `hll_count.merge_partial` and extract final counts using `hll_count.extract`
- **Key fields**: `user_mau` as the approximate distinct count, `dimensions` indicating breakdown level, dimension-specific fields set to null for unused breakdowns

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set with consistent schema structure
- **Time aggregation**: Apply 30-day rolling windows where each date represents users active in that date plus the previous 29 days
- **Final calculation**: The `user_mau` value represents the approximate count of unique users who redeemed carbon credits within each 30-day rolling window, calculated using HyperLogLog merge and extract operations
- **Schema standardization**: Apply finalization macro to ensure consistent metadata fields and fill missing time periods with zero values

*This methodology was automatically generated by Token Terminal's methodology analyzer.*

Unique revenue-generating users over a rolling 30-day window.

Toucan Protocol

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown