* 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: 54399.60000 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 | Pi Network |
| Consensus Mechanism | Pi Network uses a consensus mechanism derived from the Stellar Consensus Protocol (SCP), which is based on the Federated Byzantine Agreement (FBA). The network relies on a trust graph built from “Security Circles” created by individual users. Each participant adds trusted members to their Security Circle, and these interconnected trust relationships form the basis for validating transactions and securing the network. |
| Incentive Mechanisms and Applicable Fees | The Pi Network incentivizes user participation through a mobile-first mining system that distributes Pi tokens based on user engagement and contributions to network growth and security. Users, called Pioneers, earn tokens by logging into the app daily and actively participating in the creation of Security Circles, which help build the trust graph required for consensus. Transaction fees on the network are expected to remain low and accessible, and the governance of these fees will evolve as the network transitions fully to its open mainnet phase. |
| Beginning of the period | 2025-07-27 |
| End of the period | 2026-07-27 |
| Energy consumption | 54399.60000 (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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