Regulamentada
Plataforma de corretagem de criptomoedas e valores mobiliários regulamentada na Áustria e na Europa
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As regulamentações ESG (Environmental, Social, and Governance) para criptoativos têm como objetivo reduzir o seu impacto ambiental (por exemplo, a mineração com elevado consumo de energia), promover a transparência e garantir práticas de governação ética, alinhando o setor das criptomoedas com metas mais amplas de sustentabilidade e responsabilidade social. Estas regulamentações promovem o cumprimento de normas que reduzem riscos e fortalecem a confiança nos ativos digitais.
Nome | Bitpanda Asset Management GmbH, Bitpanda GmbH |
Identificador relevante da entidade jurídica | 9845005X9B7N610K0093, 5493007WZ7IFULIL8G21 |
Nome do criptoativo | Flow |
Mecanismo de Consenso | Flow employs a Proof of Stake (PoS) model with a multi-role node architecture and the HotStuff Byzantine Fault Tolerant (BFT) protocol to achieve high throughput, scalability, and fast finality. Core Components of Flow’s Consensus: 1. Proof of Stake with Multi-Role Architecture: Specialized Node Roles: Flow’s PoS model features a multi-node architecture where node roles are divided among different types of specialized nodes, each responsible for specific tasks. This separation enhances scalability by allowing nodes to focus on particular operations, leading to efficient transaction processing and high throughput. 2. HotStuff Consensus Algorithm: Optimized for High Throughput and Fast Finality: Flow utilizes an optimized version of the HotStuff consensus protocol, which is designed to support high-speed, low-latency transactions essential for Flow’s performance-oriented blockchain. BFT Compliance: HotStuff is a BFT protocol, allowing it to tolerate up to one-third of nodes acting maliciously without compromising the network’s security. This resilience ensures the network remains secure and functional, even with potential faults or dishonest nodes. 3. Leader-Based Block Proposal: Leader and Replica Nodes: HotStuff operates with a leader-based approach where a designated leader node proposes new blocks, and other nodes (replicas) validate these blocks. This method simplifies the consensus process, reducing complexity and improving efficiency. Leader Rotation Mechanism: To prevent centralization and enhance fault tolerance, HotStuff incorporates a leader rotation system, replacing the leader if it becomes unresponsive or acts maliciously. This rotation ensures continuous network reliability and minimizes downtime. |
Mecanismos de Incentivo e Taxas Aplicáveis | Flow’s incentive model rewards validator nodes, supports ecosystem growth, and maintains affordable fees for developers and users. Incentive Mechanisms: 1. Staking Rewards for Specialized Nodes: Role-Based Rewards: Validators earn Flow tokens according to their specific roles and contributions within the multi-node architecture, aligning rewards with each node’s responsibilities to encourage balanced and effective network participation. 2. Transaction Fees: Stable and Consumer-Friendly Fees: Flow’s fee structure is designed for predictability, keeping transaction costs stable for both developers and users. Fees are based on transaction complexity and provide an ongoing income stream for validators. 3. Misbehavior Penalties: Penalties for Downtime or Malicious Behavior: To maintain network stability, Flow imposes penalties on validators for misbehavior or downtime. This incentivizes high-quality validator participation and ensures consistent performance. 4. Ecosystem and Developer Support: Dedicated Portion of Fees and Rewards: A portion of Flow’s transaction fees and rewards is allocated to developer initiatives, ecosystem growth, and community engagement. This investment fosters innovation, supports long-term network health, and aligns incentives for ecosystem development. |
Início do período | 2025-05-07 |
Fim do período | 2026-05-07 |
Consumo de energia | 526695.00000 (kWh/a) |
Recursos e metodologias de consumo de energia | 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. |
Consumo de energia renovável | 37.912410119 (%) |
Intensidade energética | 0.00029 (kWh) |
Emissões de GEE de DLT de Âmbito 1 - Controladas | 0.00000 (tCO2e/a) |
Emissões de GEE de DLT de Âmbito 2 - Compradas | 175.29098 (tCO2e/a) |
Intensidade de GEE | 0.00010 (kgCO2e) |
Principais fontes de energia e metodologias | 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. |
Principais fontes de GEE e metodologias | 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. |