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Propisi o rizicima ESG-a (ekološkim, društvenim i upravljačkim rizicima) za kriptoimovinu bave se pitanjem utjecaja na okoliš (npr. energetski intenzivno rudarenje), promicanja transparentnosti i osiguranja etičkih praksi upravljanja kako bi kripto industrija bila u skladu sa širim ciljevima održivosti i društvenim ciljevima. Ovi propisi potiču sukladnost sa standardima koji smanjuju rizike i potiču povjerenje u digitalnu imovinu.
Ime | Bitpanda Asset Management GmbH, Bitpanda GmbH |
Relevant legal entity identifier | 9845005X9B7N610K0093, 5493007WZ7IFULIL8G21 |
Name of the crypto-asset | Siacoin |
Consensus Mechanism | Siacoin employs a Proof of Work (PoW) consensus mechanism to secure its decentralized storage network. Core Components: Proof of Work (PoW): Miners validate transactions and secure the blockchain by solving computational puzzles using the PoW model. Blake2b Hashing Algorithm: The network relies on the Blake2b hashing algorithm, optimized for efficient and secure mining operations. ASIC Mining: Mining on the Siacoin network is most efficient using Application-Specific Integrated Circuits (ASICs) tailored for the Blake2b algorithm, promoting network stability through specialized hardware. |
Incentive Mechanisms and Applicable Fees | Siacoin incentivizes miners and storage hosts while ensuring low-cost, secure transactions and storage services. Incentive Mechanisms: Block Rewards: Miners earn SC as rewards for validating transactions and successfully adding new blocks to the blockchain, encouraging continuous participation in network security. Transaction Fees: Users pay transaction fees in SC for sending payments and interacting with the network. These fees compensate miners for processing transactions and maintaining network integrity. Host Collateral: Storage hosts must lock up SC as collateral when entering storage contracts. This ensures reliability and motivates hosts to fulfill their storage commitments, enhancing trust within the network. |
Beginning of the period | 2024-09-17 |
End of the period | 2025-09-17 |
Energy consumption | 87614336.89081 (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: Blake. 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 | 4.80079 (kWh) |
Scope 1 DLT GHG emissions - Controlled | 0.00000 (tCO2e/a) |
Scope 2 DLT GHG emissions - Purchased | 36096.76172 (tCO2e/a) |
GHG intensity | 1.97790 (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. |