Bitpanda Asset Management GmbH, Bitpanda GmbH

9845005X9B7N610K0093, 5493007WZ7IFULIL8G21

Kaspa

The Kaspa blockchain uses a unique Proof-of-Work consensus mechanism called the GHOSTDAG (Greedy Heaviest Observed Subtree Directed Acyclic Graph) protocol. GHOSTDAG is designed to offer high throughput, low latency, and secure finality, addressing scalability and performance challenges typically faced by traditional blockchain systems. Key Features of Kaspa's Consensus Mechanism: 1. Directed Acyclic Graph (DAG) Structure: Kaspa operates on a DAG, allowing multiple blocks to be produced simultaneously and linked together in a way that eliminates the need for a single linear chain. This allows for parallel block production, significantly increasing the overall throughput of the network. Unlike traditional blockchains where only the longest chain is considered valid, Kaspa allows blocks to coexist, increasing transaction throughput and scalability. 2. GHOSTDAG Protocol: The GHOSTDAG protocol resolves the challenges that arise in DAG-based networks by ensuring a consistent ordering of blocks. It uses the concept of "growing" blocks from the heaviest observed subtree, meaning that new blocks are integrated into the DAG in a way that prioritizes the most secure and valid branches. This mechanism allows Kaspa to maintain finality and avoid forks while increasing the overall throughput of the system. 3. High Throughput and Low Latency: Kaspa's GHOSTDAG protocol enables high-speed block confirmation without sacrificing security, processing thousands of transactions per second with low latency. 4. Proof of Work (PoW): Kaspa utilizes Proof of Work (PoW) for block validation, where miners must solve cryptographic puzzles to add new blocks to the DAG. This ensures the integrity and security of the network while making the mining process decentralized and permissionless. The PoW ensures that no single miner or group can control the network, contributing to the decentralized nature of Kaspa. 5. Simultaneous Block Creation: In Kaspa, blocks are created in parallel by miners, and their validity is determined by the consensus protocol (GHOSTDAG), allowing for high scalability and fast block times (approximately one block every second). 6. Block Finality: Once a block is added to the DAG and supported by a sufficient number of subsequent blocks, it achieves finality, meaning it cannot be reverted or reorganized.

Kaspa employs a Proof-of-Work (PoW) consensus mechanism to secure its network and incentivize participants. Miners validate transactions and add new blocks to the DAG (Directed Acyclic Graph) structure, earning rewards for their efforts. Incentive Mechanism: 1. Mining Rewards: Block Rewards: Miners receive newly minted KAS tokens as rewards for successfully mining new blocks. The block reward decreases over time, following a predetermined schedule, to control the total supply of KAS tokens. Transaction Fees: In addition to block rewards, miners earn transaction fees from the transactions included in the blocks they mine. Users pay these fees to incentivize miners to prioritize their transactions. 2. Transaction Fees: Users pay transaction fees to have their transactions processed and included in the blockchain. These fees are determined by the size of the transaction and the current network conditions. Higher fees can expedite transaction inclusion, especially during periods of high network activity. Applicable Fees: 1. Transaction Fees: Transaction fees are calculated based on the size of the transaction, measured in bytes. The fee rate is dynamic and adjusts according to network congestion and demand. Users can estimate appropriate fee rates using tools like the Rusty Kaspa node's getFeeEstimate() RPC method, which provides real-time fee rate suggestions based on current network conditions. 2. Fee Rate and Quality of Service (QoS): The fee rate influences the priority of transactions. A higher fee rate increases the likelihood of a transaction being included in the next block, ensuring faster confirmation times. Kaspa's fee structure allows users to adjust their fee rates to balance cost and transaction speed according to their preferences.

2024-10-12

2025-10-12

2831162818.95504 (kWh/a)

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: KHeavyhash. 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.

29.306425042 (%)

3.52573 (kWh)

0.00000 (tCO2e/a)

1166427.93040 (tCO2e/a)

1.45259 (kgCO2e)

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.

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.