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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 | Conflux |
Consensus Mechanism | Conflux operates on a unique Tree-Graph consensus mechanism that combines Optimized Proof of Work (PoW) with Proof of Stake (PoS), allowing high transaction throughput, security, and scalability. Core Components of Conflux’s Consensus: 1. Tree-Graph Structure: Concurrent Block Production: Conflux’s Tree-Graph model enables blocks to be produced in parallel, rather than sequentially in a single chain. This structure significantly increases transaction throughput and efficiency compared to traditional blockchains. Hierarchy for Fork Reduction: Unlike typical PoW blockchains where forks are common, Conflux’s Tree-Graph organizes blocks hierarchically, allowing multiple chains to coexist without causing divergences. This minimizes the need for forks, ensuring stability and continuity in block production. 2. Optimized Proof of Work (PoW): Security and Decentralization: Conflux uses an optimized PoW model to maintain security and decentralization, offering similar security guarantees to traditional PoW systems but with enhanced efficiency, allowing high-performance block processing. 3. Proof of Stake (PoS) Integration: PoS for Finality: PoS nodes in Conflux are selected based on the amount of staked CFX (Conflux’s native token). These nodes sign pivot blocks to finalize them, reducing the probability of forks and ensuring rapid finality. Balance Between PoW and PoS: By combining PoW and PoS, Conflux achieves a balanced, secure consensus system that leverages PoW’s security while incorporating PoS for faster finality. |
Incentive Mechanisms and Applicable Fees | Conflux incentivizes network participation and security through block rewards, transaction fees, and staking rewards, along with unique ecosystem support and storage fee structures. Incentive Mechanisms: 1. Block Rewards and Transaction Fees for Miners: Continuous Incentive for Miners: Miners receive CFX rewards not only for mining blocks but also for securing the network. These rewards, including transaction fees, create an ongoing incentive for miners to participate actively and uphold network stability. 2. Staking Rewards for PoS Nodes: Rewards for Finalization Participation: PoS nodes, responsible for signing and finalizing pivot blocks, earn staking rewards based on their staked CFX amount. This reward structure encourages reliable PoS participation, enhancing network security and finality. 3. Dynamic Gas Fee Model: Ethereum-Like Gas Model: Conflux uses a gas model similar to Ethereum’s, where fees are calculated based on the computational resources required (measured in gas) and the current gas price, which adjusts based on network demand. Dynamic Adjustment: During high network demand, gas fees increase to help manage congestion, while fees decrease in low-demand periods to promote network activity. 4. Ecosystem Fund Allocation: Supporting Long-Term Development: A portion of transaction fees is allocated to the Conflux ecosystem fund, which supports long-term network development, community initiatives, and ecosystem growth. This fund helps sustain the network and fosters innovation within the ecosystem. 5. Storage Fee Model: Reducing Blockchain Bloat: Conflux incorporates a storage fee to discourage unnecessary data storage on the blockchain. This model supports long-term sustainability by reducing blockchain bloat, helping to maintain efficient network performance over time. |
Beginning of the period | 2024-09-09 |
End of the period | 2025-09-09 |
Energy consumption | 1837140.73200 (kWh/a) |
Energy consumption resources and methodologies | The energy consumption of this asset is aggregated across multiple components: 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. To determine the energy consumption of a token, the energy consumption of the network(s) conflux is calculated first. For the energy consumption of the token, a fraction of the energy consumption of the network is attributed to the token, which is determined based on the activity of the crypto-asset within the network. When calculating the energy consumption, the Functionally Fungible Group Digital Token Identifier (FFG DTI) is used - if available - to determine all implementations of the asset in scope. The mappings are updated regularly, 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 | 0.00973 (kWh) |
Scope 1 DLT GHG emissions - Controlled | 0.00000 (tCO2e/a) |
Scope 2 DLT GHG emissions - Purchased | 754.94600 (tCO2e/a) |
GHG intensity | 0.00401 (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. |