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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 | Qubic |
Consensus Mechanism | The Qubic blockchain utilizes a Proof-of-Work algorithm called Quorum-Based Computation (QBC), designed to combine decentralization, efficiency, and adaptability for secure transaction and smart contract execution. Core Components: Quorum-Based Computation (QBC): Decisions require agreement from at least 451 out of the top 676 Computors, ensuring reliable and decentralized consensus for transaction validation and smart contract execution. Useful Proof-of-Work (uPoW): AI miners compete to solve computational tasks, determining the ranking of Computors for each epoch (one week). The top-performing Computors qualify to participate in consensus, fostering a dynamic and performance-driven network. Spectrum Ledger: Qubic's equivalent of a blockchain ledger, the Spectrum records validated transactions and smart contract outcomes, ensuring integrity and transparency. Dynamic and Adaptive Selection: Computors are continually reassessed and ranked based on their performance. Computors unable to meet the network’s speed or efficiency standards are replaced, maintaining high operational quality. Decentralization and Autonomy: All major decisions, including transaction validation and smart contract execution, are governed by the quorum of Computors, with no single entity having overriding authority. |
Incentive Mechanisms and Applicable Fees | The Qubic blockchain's incentive mechanism and fee structure are designed to reward performance and ensure efficient network operations while maintaining fairness and decentralization. Incentive Mechanism: Revenue for Computors: Qualified Computors earn revenue by validating transactions and executing smart contracts on the Spectrum ledger. Computors are rewarded based on their performance, including transaction processing speed and network compatibility. Useful Proof-of-Work (uPoW): AI miners earn recognition by solving computational tasks, ranking Computors for eligibility in the consensus process. While mining does not directly validate transactions, it incentivizes AI innovation and supports the Aigarth ecosystem. Performance-Based Rewards: Computors demonstrating consistent speed and compatibility are prioritized, ensuring that high-performance nodes are rewarded. Computors that fail to keep pace are replaced, maintaining network efficiency. Applicable Fees: Transaction Fees: Users pay transaction fees for executing transfers and smart contracts. These fees are distributed among the participating Computors, incentivizing their active involvement in network operations. Dynamic Fee Structure: Fees are adjusted based on network demand and transaction complexity, balancing user costs with network sustainability. |
Beginning of the period | 2024-09-14 |
End of the period | 2025-09-14 |
Energy consumption | 1909680.00000 (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 | 32.225548601 (%) |
Energy intensity | 0.01090 (kWh) |
Scope 1 DLT GHG emissions - Controlled | 0.00000 (tCO2e/a) |
Scope 2 DLT GHG emissions - Purchased | 635.56646 (tCO2e/a) |
GHG intensity | 0.00363 (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. |