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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 | NKN |
Consensus Mechanism | NKN employs a unique consensus mechanism called Proof of Relay (PoR), which is specifically designed for decentralized data transmission. This mechanism incentivizes network nodes to relay data between participants. Each node in the NKN network contributes by transmitting data and participating in the consensus process. The PoR mechanism uses a public key as the node's identifier and applies an algorithm based on Verifiable Random Functions (VRF) to ensure that consensus is achieved efficiently and securely. The VRF process ensures that nodes are selected for participation in a fair and unpredictable manner, maintaining the network's decentralization. Unlike traditional Proof of Work or Proof of Stake systems, NKN’s PoR does not rely on mining or staking. Instead, it rewards nodes for their contribution to data transmission, ensuring the network scales naturally as usage increases. |
Incentive Mechanisms and Applicable Fees | The NKN token is used as the native currency for network transactions and incentivization. Users pay fees in NKN tokens for data transmission and other services within the network. Nodes earn these tokens as compensation for relaying data, with rewards proportional to the amount of data successfully transmitted. The economic model of NKN is designed to balance demand and supply dynamically. Nodes compete to provide the fastest and most reliable data transmission, creating a market-driven approach to bandwidth allocation. This competitive environment ensures network efficiency while maintaining low latency and high throughput. |
Beginning of the period | 2024-09-09 |
End of the period | 2025-09-09 |
Energy consumption | 591300.00000 (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 | 0.00810 (kWh) |
Scope 1 DLT GHG emissions - Controlled | 0.00000 (tCO2e/a) |
Scope 2 DLT GHG emissions - Purchased | 196.79237 (tCO2e/a) |
GHG intensity | 0.00270 (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. |