* Ei reaaliaikaiset tiedot.
* Mikään Euroopan unionin jäsenvaltion toimivaltainen viranomainen ei ole hyväksynyt tätä kryptovaran kuvausta. Kryptovaran tarjoaja on yksin vastuussa tämän kryptovaran kuvauksen sisällöstä.
Energy consumption: 24966.00000 kWh/a | Renewable energy: 28.000000000%
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 Ltd |
| Relevant legal entity identifier | 2135881-0 |
| Name of the crypto-asset | PEAQ |
| Consensus Mechanism | The peaq blockchain operates on a modified version of the Nominated Proof of Stake (NPoS) consensus mechanism. In this system, validators are responsible for block production and transaction validation, while nominators support trustworthy validators by staking their tokens. The mechanism is designed to balance decentralization and performance, selecting validators based on their stake and reputation. It emphasizes security and scalability, ensuring resistance to malicious actors while maintaining high throughput. |
| Incentive Mechanisms and Applicable Fees | peaq’s economic model is tailored to support the machine economy through its native tokens, $PEAQ and $MINT. Validators and nominators earn rewards for their contributions to network security and operations, incentivizing active participation in the consensus process. The network also rewards users and devices participating in machine-driven ecosystems, fostering innovation and engagement. Transaction fees are designed to be minimal, enabling seamless microtransactions and accessibility for various use cases. |
| Beginning of the period | 2025-02-08 |
| End of the period | 2026-02-08 |
| Energy consumption | 24966.00000 (kWh/a) |
| Energy consumption resources 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. 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 | 28.000000000% |
| Energy intensity | 0 (kWh) |
| Scope 1 DLT GHG emissions - Controlled | 0.00000 (tCO2e/a) |
| Scope 2 DLT GHG emissions - Purchased | 0 (tCO2e/a) |
| GHG intensity | 0 (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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