| Name |
Coinmotion Ltd |
| Relevant legal entity identifier |
2135881-0 |
| Name of the crypto-asset |
Berachain BERA |
| Consensus Mechanism |
Berachain utilizes a unique consensus mechanism called Proof-of-Liquidity (PoL). In this system, validators secure the network by staking the native gas token, $BERA. The likelihood of a validator being selected to propose a block is proportional to the amount of $BERA they have staked. Upon successfully proposing a block, validators receive rewards in the form of $BGT (Bera Governance Token). The amount of $BGT awarded is influenced by the level of $BGT delegation they have received from other participants. This mechanism aligns the incentives of validators, protocols, and users, contributing to the overall long-term health of the chain. |
| Incentive Mechanisms and Applicable Fees |
In Berachain's economic model, validators and delegators are incentivized through a combination of staking rewards and protocol-provided incentives. Validators earn $BGT rewards for block production, with the reward size determined by their boost, which is a percentage calculated from the validator's $BGT boost out of the total $BGT boosted to all validators. Validators can direct their $BGT emissions to whitelisted Reward Vaults of their choosing, and in exchange, they receive protocol-provided incentives from these Reward Vaults. Delegators, by staking their $BGT with validators, can influence the validator's boost and share in the rewards. Transaction fees on the network are paid in $BERA and are burned, removing them from the circulating supply. This structure ensures that all participants are motivated to contribute to the network's security and efficiency. |
| Beginning of the period |
2025-07-27 |
| End of the period |
2026-07-27 |
| Energy consumption |
244404.00000 (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 |
28.000000000% |
| Energy intensity |
0 (kWh) |
| Scope 1 DLT GHG emissions - Controlled |
0.00000 (tCO2e/a) |
| Scope 2 DLT GHG emissions - Purchased |
0 (tCO2e/a) |
| GHG intensity |
0 (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. |