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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 | Bitpanda Asset Management GmbH, Bitpanda GmbH |
Relevant legal entity identifier | 9845005X9B7N610K0093, 5493007WZ7IFULIL8G21 |
Name of the crypto-asset | Ravencoin |
Consensus Mechanism | Ravencoin employs a Proof of Work (PoW) consensus mechanism with an ASIC-resistant algorithm to promote decentralization and enable efficient mining. Core Components: Proof of Work (PoW): Ensures network security and transaction validation by requiring miners to perform computational work. KawPow Algorithm: Utilizes an ASIC-resistant algorithm designed to allow mining with GPUs, encouraging broader participation and maintaining decentralization. |
Incentive Mechanisms and Applicable Fees | Ravencoin incentivizes network security and transaction processing through block rewards and user fees. Incentive Mechanisms: Block Rewards: Miners are rewarded with newly minted RVN tokens for successfully mining blocks and securing the blockchain. Transaction Fees: Users pay minimal fees in RVN for transferring assets or executing operations, providing miners with additional compensation. Applicable Fees: Transaction Fees: Low fees in RVN are applied for all transactions, supporting cost-effective and efficient blockchain operations. |
Beginning of the period | 2024-09-13 |
End of the period | 2025-09-13 |
Energy consumption | 193473510.36554 (kWh/a) |
Energy consumption resources and methodologies | For the calculation of energy consumptions, the so called 'top-down' approach is being used, within which an economic calculation of the miners is assumed. Miners are persons or devices that actively participate in the proof-of-work consensus mechanism. The miners are considered to be the central factor for the energy consumption of the network. Hardware is pre-selected based on the consensus mechanism's hash algorithm: KawPow. A current profitability threshold is determined on the basis of the revenue and cost structure for mining operations. Only Hardware above the profitability threshold is considered for the network. The energy consumption of the network can be determined by taking into account the distribution for the hardware, the efficiency levels for operating the hardware and on-chain information regarding the miners' revenue opportunities. If significant use of merge mining is known, this is taken into account. 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 | 29.306425042 (%) |
Energy intensity | 6.31781 (kWh) |
Scope 1 DLT GHG emissions - Controlled | 0.00000 (tCO2e/a) |
Scope 2 DLT GHG emissions - Purchased | 79710.32424 (tCO2e/a) |
GHG intensity | 2.60291 (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. |