Sustainability Disclosure - ALGO

Quantitative information

S.1 Name
Digital Currency Services B.V.

S.2 Relevant legal entity identifier
724500QZRWKCU8D2L569

S.3 Name of the crypto-asset
Algorand [ALGO]

S.6 Beginning of the period to which the disclosure relates
2024-06-14

S.7 End of the period to which the disclosure relates
2025-06-14

S.8 Energy consumption
552500 kWh/a

S.10 Renewable energy consumption
17.20%

S.11 Energy intensity
0.00000 kWh

S.12 Scope 1 DLT GHG emission - Controlled
0.00000 tCO2e

S.13 Scope 2 DLT GHG emission - Purchased
184.94809 tCO2e

S.14 GHG intensity
0.00000 kgCO2e

Qualitative information

S.4 Consensus Mechanism

Algorand utilizes a unique Pure Proof-of-Stake (PPoS) consensus mechanism based on Byzantine Agreement, where security is tied to honest majority stake rather than computation. It achieves fast, fork-free transactions (under 3 seconds) by using Verifiable Random Functions (VRF) to privately and randomly select committee members for proposing and validating blocks, with selection probability proportional to each user’s staked Algo.

Key Aspects of Algorand Consensus (PPoS):

  • True Decentralization: Unlike standard Proof-of-Stake, PPoS does not require locking up tokens. Users keep their Algo in their wallets while participating, and any user with a balance can be randomly selected to participate in consensus.
  • Cryptographic Sortition (VRF): For every block, a new committee is chosen through a fast, secret, and random cryptographic process (VRF). Because participants are chosen privately and only revealed after they send a message, it is impossible for adversaries to know whom to target in an attack.
  • Two-Phase Process (Proposal & Vote):
    1. Propose Phase: A user is chosen to propose a new block.
    2. Soft Vote: A committee votes to narrow down the proposals to the "best" one.
    3. Certify Vote: A second committee certifies the block by voting to ensure it has no double-spending and conforms to the protocol.
  • High Performance: Because the committee selection is instantaneous and only a small committee is needed to validate, the network achieves finality in under 5 seconds.
  • No Forks: The consensus protocol ensures that only one block is certified in each round, meaning Algorand does not fork.

The system allows the network to remain secure and functional as long as a supermajority (over 2/3) of the stake is held by honest participants.

S.5 Incentive Mechanisms and Applicable Fees

I) Incentive mechanism

Algorand's incentive model has evolved towards rewarding active network participants (validators) rather than just passive holders.

  • Staking Rewards (Node Participation): As of late 2024, validators running participation nodes can earn rewards by securing the network.
  • Requirements: A minimum stake of 30,000 ALGO is generally required for a participating account to be eligible for these rewards, determined by governance vote.
  • Payouts: Validators receive 10 ALGO for each successfully proposed block, along with a portion of transaction fees, with rewards designed to decrease by 1% every millionth block.
  • Governance Rewards: Users can commit ALGO to three-month governance cycles to vote on protocol changes. This is separate from node staking but acts as an incentive for long-term holding and community participation.
  • Transaction Fee Redistribution: 50% of transaction fees are paid to the proposer of the block, incentivizing validators to include more transactions.
  • No Slashing: Unlike other Proof-of-Stake networks, Algorand does not slash (destroy) staked tokens for bad behavior, which encourages participation. Instead, inactive or malicious nodes are simply ignored by the network and lose out on rewards

II) Applicable Fees

Algorand is designed to be highly cost-effective, with fees that are predictable and low, regardless of network load.

  • Standard Transaction Fee: The minimum fee for a standard transaction is 0.001 ALGO.
  • Transaction Fee Structure: The fee is calculated based on transaction size in bytes, but the minimum fee remains fixed at 1,000 microAlgos (ALGO) under normal conditions.
  • Fee Sink: Collected fees are not directly rewarded to a central party but are generally sent to a "Fee Sink," which helps fund future incentive mechanisms, making the model self-sustaining

S.9 Energy consumption sources and methodologies

For the calculation of energy consumptions, the so called "bottom-up" approach is being used. The nodes are considered to be the central factor for the energy consumption of the network. These assumptions are made on the basis of empirical findings through the use of public information sites, open-source crawlers and crawlers developed in-house. The main determinants for estimating the hardware used within the network are the requirements for operating the client software. The energy consumption of the hardware devices was measured in certified test laboratories. When calculating the energy consumption, we used - if available - the Functionally Fungible Group Digital Token Identifier (FFG DTI) to determine all implementations of the asset of question in scope and we update the mappings regulary, based on data of the Digital Token Identifier Foundation.

S.15 Key energy sources and methodologies

To determine the proportion of renewable energy usage, the locations of the nodes are to be determined using public information sites, open-source crawlers and crawlers developed in-house. If no information is available on the geographic distribution of the nodes, reference networks are used which are comparable in terms of their incentivization structure and consensus mechanism. This geo-information is merged with public information from the European Environment Agency (EEA) and thus determined. The intensity is calculated as the marginal energy cost wrt. one more transaction.

S.16 Key GHG sources and methodologies

To determine the GHG Emissions, the locations of the nodes are to be determined using public information sites, open-source crawlers and crawlers developed in-house. If no information is available on the geographic distribution of the nodes, reference networks are used which are comparable in terms of their incentivization structure and consensus mechanism. This geo-information is merged with public information from Our World in Data, see citation. The intensity is calculated as the marginal emission wrt. one more transaction.