* Ei reaaliaikaiset tiedot.
* Mikään Euroopan unionin jäsenvaltion toimivaltainen viranomainen ei ole hyväksynyt tätä kryptovaran kuvausta. Kryptovaran tarjoaja on yksin vastuussa tämän kryptovaran kuvauksen sisällöstä.
Energiankulutus: 375278.40000 kWh/a | Uusiutuva energia: 26.538687083
ESG (Environmental, Social, and Governance) regulations for crypto assets aim to address their environmental impact (e.g., energy-intensive mining), promote transparency, and ensure ethical governance practices to align the crypto industry with broader sustainability and societal goals. These regulations encourage compliance with standards that mitigate risks and foster trust in digital assets.
| Name | Coinmotion Oy |
| Relevant legal entity identifier | 743700PZG5RRF7SA4Q58 |
| Name of the crypto-asset | Sui |
| Consensus Mechanism | The Sui blockchain utilizes a Byzantine Fault Tolerant (BFT) consensus mechanism optimized for high throughput and low latency. Core Components 1. Mysten Consensus Protocol: The Sui consensus is based on Mysten Labs' Byzantine Fault Tolerance (BFT) protocol, which builds on principles of Practical Byzantine Fault Tolerance (pBFT) but introduces key optimizations for performance. Leaderless Design: Unlike traditional BFT models, Sui does not rely on a single leader to propose blocks. Validators can propose blocks simultaneously, increasing efficiency and reducing the risks associated with leader failure or attacks. Parallel Processing: Transactions can be processed in parallel, maximizing network throughput by utilizing multiple cores and threads. This allows for faster confirmation of transactions and high scalability. 2. Transaction Validation: Validators are responsible for receiving transaction requests from clients and processing them. Each transaction includes digital signatures and must meet the network’s rules to be considered valid. Validators can propose transactions simultaneously, unlike many other networks that require a sequential, leader-driven process. 3. Optimistic Execution: Optimistic Consensus: Sui allows validators to process certain non-contentious, independent transactions without waiting for full consensus. This is known as optimistic execution and helps reduce transaction latency for many use cases, allowing for fast finality in most cases. 4. Finality and Latency: The system only requires three rounds of communication between validators to finalize a transaction. This results in low-latency consensus and rapid transaction confirmation times, achieving scalability while maintaining security. Fault Tolerance: The system can tolerate up to one-third of validators being faulty or malicious without compromising the integrity of the consensus process. |
| Incentive Mechanisms and Applicable Fees | Security and Economic Incentives: 1. Validators: Validators stake SUI tokens to participate in the consensus process. They earn rewards for validating transactions and securing the network. Slashing: Validators can be penalized (slashed) for malicious behavior, such as double-signing or failing to properly validate transactions. This helps maintain network security and incentivizes honest behavior. 2. Delegation: Token holders can delegate their SUI tokens to trusted validators. In return, they share in the rewards earned by validators. This encourages widespread participation in securing the network. Fees on the SUI Blockchain 1. Transaction Fees: Users pay transaction fees to validators for processing and confirming transactions. These fees are calculated based on the computational resources required to process the transaction. Fees are paid in SUI tokens, which is the native cryptocurrency of the Sui blockchain. 2. Dynamic Fee Model: The transaction fees on Sui are dynamic, meaning they adjust based on network demand and the complexity of the transactions being processed. |
| Beginning of the period | 2024-06-09 |
| End of the period | 2025-06-09 |
| Energy consumption | 375278.40000 (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. To determine the energy consumption of a token, the energy consumption of the network(s) sui 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. |
| Renewable energy consumption | 26.538687083 |
| Energy intensity | 0.00001 (kWh) |
| Scope 1 DLT GHG emissions - Controlled | 0.00000 (tCO2e/a) |
| Scope 2 DLT GHG emissions - Purchased | 124.89756 (tCO2e/a) |
| GHG intensity | 0.00000 (kgCO2e) |
| 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 Our World in Data, see citation. The intensity is calculated as the marginal energy cost wrt. one more transaction. Ember (2025); Energy Institute - Statistical Review of World Energy (2024) – with major processing by Our World in Data. “Share of electricity generated by renewables – Ember and Energy Institute” [dataset]. Ember, “Yearly Electricity Data Europe”; Ember, “Yearly Electricity Data”; Energy Institute, “Statistical Review of World Energy” [original data]. Retrieved from https://ourworldindata.org/grapher/share-electricity-renewables |
| 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. Ember (2025); Energy Institute - Statistical Review of World Energy (2024) – with major processing by Our World in Data. “Carbon intensity of electricity generation – Ember and Energy Institute” [dataset]. Ember, “Yearly Electricity Data Europe”; Ember, “Yearly Electricity Data”; Energy Institute, “Statistical Review of World Energy” [original data]. Retrieved from https://ourworldindata.org/grapher/carbon-intensity-electricity Licenced under CC BY 4.0 |
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