| Name |
Coinmotion Oy |
| Relevant legal entity identifier |
743700PZG5RRF7SA4Q58 |
| Name of the crypto-asset |
gunz |
| Consensus Mechanism |
gunz is present on the following networks: Avalanche, Gunz.
The Avalanche blockchain network employs a unique Proof-of-Stake consensus mechanism called Avalanche Consensus, which involves three interconnected protocols: Snowball, Snowflake, and Avalanche. Avalanche Consensus Process 1. Snowball Protocol: o Random Sampling: Each validator randomly samples a small, constant-sized subset of other validators. Repeated Polling: Validators repeatedly poll the sampled validators to determine the preferred transaction. Confidence Counters: Validators maintain confidence counters for each transaction, incrementing them each time a sampled validator supports their preferred transaction. Decision Threshold: Once the confidence counter exceeds a pre-defined threshold, the transaction is considered accepted. 2. Snowflake Protocol: Binary Decision: Enhances the Snowball protocol by incorporating a binary decision process. Validators decide between two conflicting transactions. Binary Confidence: Confidence counters are used to track the preferred binary decision. Finality: When a binary decision reaches a certain confidence level, it becomes final. 3. Avalanche Protocol: DAG Structure: Uses a Directed Acyclic Graph (DAG) structure to organize transactions, allowing for parallel processing and higher throughput. Transaction Ordering: Transactions are added to the DAG based on their dependencies, ensuring a consistent order. Consensus on DAG: While most Proof-of-Stake Protocols use a Byzantine Fault Tolerant (BFT) consensus, Avalanche uses the Avalanche Consensus, Validators reach consensus on the structure and contents of the DAG through repeated Snowball and Snowflake.
GUNZ is a Layer-1 blockchain specifically designed for gaming, operating as an Avalanche subnet and utilizing the Snowman++ consensus protocol. This setup enables sub-second transaction finality and supports over 4,500 transactions per second, ensuring a seamless gaming experience. The network employs a permissioned model for smart contract deployment, allowing only authorized addresses to implement code, which enhances security and stability. Validators are represented by Validator NFTs, which are required to participate in the network's consensus process. These NFTs come in various rarities, each offering different levels of rewards and responsibilities within the network. |
| Incentive Mechanisms and Applicable Fees |
gunz is present on the following networks: Avalanche, Gunz.
Avalanche uses a consensus mechanism known as Avalanche Consensus, which relies on a combination of validators, staking, and a novel approach to consensus to ensure the network's security and integrity. Validators: Staking: Validators on the Avalanche network are required to stake AVAX tokens. The amount staked influences their probability of being selected to propose or validate new blocks. Rewards: Validators earn rewards for their participation in the consensus process. These rewards are proportional to the amount of AVAX staked and their uptime and performance in validating transactions. Delegation: Validators can also accept delegations from other token holders. Delegators share in the rewards based on the amount they delegate, which incentivizes smaller holders to participate indirectly in securing the network. 2. Economic Incentives: Block Rewards: Validators receive block rewards for proposing and validating blocks. These rewards are distributed from the network’s inflationary issuance of AVAX tokens. Transaction Fees: Validators also earn a portion of the transaction fees paid by users. This includes fees for simple transactions, smart contract interactions, and the creation of new assets on the network. 3. Penalties: Slashing: Unlike some other PoS systems, Avalanche does not employ slashing (i.e., the confiscation of staked tokens) as a penalty for misbehavior. Instead, the network relies on the financial disincentive of lost future rewards for validators who are not consistently online or act maliciously. o Uptime Requirements: Validators must maintain a high level of uptime and correctly validate transactions to continue earning rewards. Poor performance or malicious actions result in missed rewards, providing a strong economic incentive to act honestly. Fees on the Avalanche Blockchain 1. Transaction Fees: Dynamic Fees: Transaction fees on Avalanche are dynamic, varying based on network demand and the complexity of the transactions. This ensures that fees remain fair and proportional to the network's usage. Fee Burning: A portion of the transaction fees is burned, permanently removing them from circulation. This deflationary mechanism helps to balance the inflation from block rewards and incentivizes token holders by potentially increasing the value of AVAX over time. 2. Smart Contract Fees: Execution Costs: Fees for deploying and interacting with smart contracts are determined by the computational resources required. These fees ensure that the network remains efficient and that resources are used responsibly. 3. Asset Creation Fees: New Asset Creation: There are fees associated with creating new assets (tokens) on the Avalanche network. These fees help to prevent spam and ensure that only serious projects use the network's resources.
The GUNZ ecosystem is powered by its native token, $GUN, which serves multiple functions including transaction fees, staking, and governance. Players use $GUN for various in-game transactions such as purchasing NFT items, customization options, and subscriptions. Validators earn $GUN rewards for processing transactions and decoding in-game items, with the amount of rewards influenced by the rarity of their Validator NFTs. Transaction fees within the network are paid in $GUN and are designed to be minimal to encourage user participation. |
| Beginning of the period |
2024-10-12 |
| End of the period |
2025-10-12 |
| Energy consumption |
19943.59007 (kWh/a) |
| Energy consumption resources and methodologies |
The energy consumption of this asset is aggregated across multiple components:
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. The information regarding the hardware used and the number of participants in the network is based on assumptions that are verified with best effort using empirical data. In general, participants are assumed to be largely economically rational. As a precautionary principle, we make assumptions on the conservative side when in doubt, i.e. making higher estimates for the adverse impacts.
To determine the energy consumption of a token, the energy consumption of the network(s) avalanche is calculated first. For the energy consumption of the token, a fraction of the energy consumption of the network is attributed to the token, which is determined based on the activity of the crypto-asset within the network. When calculating the energy consumption, the Functionally Fungible Group Digital Token Identifier (FFG DTI) is used - if available - to determine all implementations of the asset in scope. The mappings are updated regularly, based on data of the Digital Token Identifier Foundation. The information regarding the hardware used and the number of participants in the network is based on assumptions that are verified with best effort using empirical data. In general, participants are assumed to be largely economically rational. As a precautionary principle, we make assumptions on the conservative side when in doubt, i.e. making higher estimates for the adverse impacts. |
| Renewable energy consumption |
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| Energy intensity |
(kWh) |
| Scope 1 DLT GHG emissions - Controlled |
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| Scope 2 DLT GHG emissions - Purchased |
(tCO2e/a) |
| GHG intensity |
(kgCO2e) |
| Key energy sources and methodologies |
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| Key GHG sources and methodologies |
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