Stable (STABLE) sustainability report

Stable is present on the following networks: Binance Smart Chain, Hyperliquid.

The Binance Smart Chain (BSC) network utilizes a hybrid consensus mechanism known as Proof of Staked Authority (PoSA). This innovative approach integrates key elements from both Delegated Proof of Stake (DPoS) and Proof of Authority (PoA) to achieve a balance of high transaction speeds, cost-efficiency, and network security, while striving to maintain a reasonable level of decentralization. The core participants in the PoSA mechanism include Validators, referred to as "Cabinet Members," Delegators, and Candidates.

Validators play a critical role, being responsible for creating new blocks, verifying transactions, and upholding the overall security of the network. To qualify as a validator, an entity must stake a substantial quantity of BNB, which serves as collateral to ensure honest conduct. These validators are selected through a dynamic process that considers both the amount of BNB they have staked and the votes they receive from token holders. At any given time, there are 21 active validators, whose rotation aims to enhance decentralization and security. Delegators are token holders who opt not to operate a validator node themselves but can contribute to network security by delegating their BNB tokens to chosen validators. This delegation bolsters a validator's total stake, increasing their likelihood of being selected for block production. In return, delegators receive a share of the rewards earned by their chosen validators, fostering broader participation in network governance and security. Candidates represent potential validators who have met the minimum BNB staking requirements and are awaiting election into the active validator set through community voting. Their presence ensures a continuous pool of ready-to-serve nodes, contributing to the network's resilience and decentralization.

During the consensus process, validators are chosen based on their accumulated BNB stake and delegator votes. The higher these metrics, the greater the chance of selection for validating transactions and producing new blocks. Once selected, these validators take turns in a PoA-like fashion to produce blocks rapidly and efficiently, validating transactions, adding them to blocks, and broadcasting them across the network. BSC boasts fast block times, typically around 3 seconds, leading to quick transaction finality. This rapid finality is a direct benefit of the efficient PoSA mechanism, which allows validators to reach consensus swiftly. To further ensure network integrity, validators face economic incentives such as slashing, where a portion of their staked BNB can be forfeited if they engage in malicious activities. This mechanism aligns validators' interests with the network's well-being, complementing the rewards they receive for their honest participation.

The Hyperliquid blockchain network, known as Hyperliquid L1, operates on a proprietary consensus mechanism called HyperBFT. This mechanism is specifically engineered to support the demanding requirements of a decentralized perpetual exchange (DEX), emphasizing high-frequency trading, robust security, and consistent transactional integrity across its ecosystem. HyperBFT draws its inspiration from the HotStuff protocol, a well-regarded Byzantine Fault Tolerant (BFT) consensus algorithm known for its efficiency and resilience in distributed systems. A core characteristic of BFT protocols like HotStuff is their ability to maintain operational integrity and reach agreement even when a certain proportion of network participants (up to one-third) act maliciously or are unresponsive. This fault tolerance is crucial for financial applications where transaction finality and security are paramount.The HotStuff protocol, and by extension HyperBFT, operates on a leader-based model. In this setup, a designated validator node is responsible for proposing new blocks of transactions to the network. Once a block is proposed, other validator nodes, often referred to as replicas, engage in a verification and validation process. This structured approach simplifies the consensus process compared to some more complex models, contributing to high-speed and low-latency transaction processing. To counteract potential centralization risks associated with a leader-based system and to enhance overall fault tolerance, HyperBFT likely incorporates a dynamic leader rotation mechanism. This ensures that the responsibility of proposing blocks is regularly shuffled among eligible validators, preventing any single entity from gaining undue control and maintaining continuous network reliability. The integration of such an efficient BFT consensus mechanism allows Hyperliquid L1 to deliver rapid transaction finality and high throughput, which are essential for a high-performance trading platform, while simultaneously ensuring strong security guarantees against various forms of network attacks or dishonest behavior.

Stable is present on the following networks: Binance Smart Chain, Hyperliquid.

The Binance Smart Chain (BSC) network employs a robust system of incentive mechanisms and applicable fees, primarily built around its Proof of Staked Authority (PoSA) consensus, designed to secure the network, encourage participation, and maintain operational efficiency. This system ensures that validators, delegators, and other participants are economically motivated to act in the network's best interest.

Validators on BSC, often referred to as "Cabinet Members," are critical to the network's operation. They are incentivized through staking rewards, which include a combination of transaction fees and newly generated block rewards. To become a validator, a significant amount of BNB must be staked. Their selection for block production is determined by the total BNB staked, encompassing both their own stake and delegated tokens, as well as the votes received from delegators. This competitive selection process motivates validators to attract delegators and maintain high performance. Delegators, in turn, are crucial for supporting network decentralization and security. By delegating their BNB to validators, they increase the validators' total stake, enhancing their chances of selection. In exchange, delegators receive a share of the rewards earned by their chosen validators, fostering active community involvement. The system also includes a pool of Candidates, nodes that have staked BNB and are ready to become active validators, ensuring a robust and resilient network of potential participants. Economic security is further reinforced through slashing mechanisms, where validators found engaging in malicious behavior or failing to perform their duties face penalties, including the forfeiture of a portion of their staked BNB. The opportunity cost of locking up BNB also provides a strong economic incentive for all participants to act honestly.

BSC is known for its low transaction fees, which are paid in BNB. These fees are vital for network maintenance and compensate validators for processing transactions. The fee structure is dynamic, adjusting based on network congestion and transaction complexity, though it is designed to remain significantly lower than on some other major blockchain networks, such as the Ethereum mainnet. In addition to transaction fees, validators receive block rewards, further incentivizing their role in maintaining and processing network activity. BSC also supports cross-chain compatibility, enabling asset transfers between Binance Chain and Binance Smart Chain, which incur minimal fees to facilitate a seamless user experience. Furthermore, interacting with and deploying smart contracts on BSC involves fees based on the computational resources required. These smart contract fees are also paid in BNB and are structured to be cost-effective, encouraging developers to build and innovate on the BSC platform.

The Hyperliquid blockchain network employs a comprehensive system of incentive mechanisms and a dynamic fee structure to ensure network security, encourage participation, and support its ongoing growth and stability. At the core of its incentive model is the native token, HYPE. Validators, who are crucial for the network's operation, earn rewards in HYPE for their diligent efforts in securing the network. This includes their role in validating transactions, participating in the HyperBFT consensus process, and generally maintaining the integrity of the blockchain. By compensating validators, Hyperliquid ensures a robust and reliable network infrastructure.Beyond validators, the network also incentivizes delegators. Token holders who may not have the technical expertise or resources to run a validator node can delegate their HYPE tokens to existing validators. In return for supporting these validators and contributing to the network's pooled security, delegators also earn a share of the HYPE rewards. This mechanism broadens participation in network security and promotes decentralization by allowing a wider range of token holders to have a vested interest in the network's health. Furthermore, Hyperliquid extends incentives to other active users within its ecosystem. Participants can earn HYPE through various activities, such as staking their tokens, providing essential liquidity to the decentralized exchange, and engaging in other contributions that foster the functionality and vibrancy of the platform. This multi-faceted incentive approach, often referred to as a dual-token system (though only HYPE is explicitly mentioned as the native token for rewards in this context), is designed to foster active engagement and align the economic interests of all participants with the long-term success of the network.Regarding applicable fees, Hyperliquid utilizes a dynamic fee model for transactions. These fees are not fixed but rather adjust based on two primary factors: the current level of network activity and the inherent complexity of the transaction being processed. This dynamic adjustment mechanism helps the network manage congestion efficiently and ensures that resource allocation is priced appropriately according to demand. Users conducting transactions on the Hyperliquid L1 blockchain are responsible for paying these fees. The fees serve a dual purpose: they cover the operational costs associated with processing transactions, including the computational resources required, and they act as a crucial component of the incentive structure for validators. By compensating validators through a portion of these transaction fees, Hyperliquid ensures that there is a continuous economic impetus for them to process transactions accurately, maintain high network uptime, and contribute to the overall security of the platform.

Stable is present on the following networks: Binance Smart Chain, Hyperliquid.

The methodology for calculating the energy consumption of the Binance Smart Chain (BSC) network, which then serves as a basis for attributing a fraction of energy to tokens operating on it, primarily utilizes a "bottom-up" approach. This method focuses on the individual components of the network to aggregate a comprehensive energy profile. The central factor in this calculation is identified as the network nodes themselves.

Assumptions regarding the hardware used within the BSC network are derived from extensive empirical findings. These findings are gathered through a combination of public information sites, sophisticated open-source crawlers, and proprietary in-house developed crawlers. The primary determinants for estimating the specific hardware deployed are the technical requirements necessary to operate the client software of the network. To ensure accuracy, the energy consumption of these identified hardware devices is rigorously measured in certified test laboratories. This precise measurement allows for a detailed understanding of the power demands of the operational infrastructure.

For the comprehensive identification of all implementations of an asset within scope, the Functionally Fungible Group Digital Token Identifier (FFG DTI) is employed, where available. The mappings associated with the FFG DTI are regularly updated based on data provided by the Digital Token Identifier Foundation. The information regarding both the hardware in use and the total number of participants active within the network is based on assumptions that undergo best-effort verification using empirical data. Generally, participants are presumed to be largely economically rational in their decision-making. As a precautionary principle, in situations of uncertainty, assumptions tend to err on the conservative side, meaning higher estimates are made for potential adverse impacts. When determining the energy consumption for a specific token that operates on BSC, the initial step involves calculating the energy consumption of the entire Binance Smart Chain network. Following this, a fraction of the total network energy consumption is attributed to the particular crypto-asset, a fraction determined by the asset's specific activity within the network.

The methodology for assessing the energy consumption of the Hyperliquid blockchain network, and by extension, any crypto-asset operating on it, follows a standardized approach that aggregates data across various components. The initial step involves calculating the total energy consumption of the underlying network itself. For networks such as Hyperliquid, which host multiple crypto-assets, this overarching network consumption is determined first. Subsequently, to ascertain the energy footprint specifically attributable to a particular crypto-asset on the Hyperliquid network, a proportional fraction of the network's total energy consumption is allocated. This attribution is meticulously determined based on the observed activity levels of that specific crypto-asset within the Hyperliquid ecosystem.To ensure accuracy and comprehensive coverage, if available, the Functionally Fungible Group Digital Token Identifier (FFG DTI) is utilized to identify and scope all implementations of the crypto-asset in question. The mappings for these identifiers are regularly updated, drawing on data provided by the Digital Token Identifier Foundation, ensuring that the assessment remains current and reflects any changes in asset deployment.The underlying assumptions regarding the hardware utilized by network participants and the overall number of active participants are critical to these calculations. These assumptions are not arbitrary but are rigorously verified using empirical data and a 'best effort' approach. A general guiding principle is that participants within the network are presumed to behave largely as economically rational actors. Furthermore, in adherence to a precautionary principle, conservative assumptions are applied when there is any uncertainty, meaning that estimates for potential adverse impacts, such as energy consumption, are biased towards higher figures to ensure a robust and cautious assessment.

Stable is present on the following networks: Binance Smart Chain, Hyperliquid.

To ascertain the proportion of renewable energy utilized by the Binance Smart Chain (BSC) network, a detailed methodology focuses on identifying the geographical distribution of its operational nodes. This process begins with leveraging a variety of data sources, including public information websites, general open-source crawlers, and specialized in-house developed crawlers. These tools collectively help pinpoint the physical locations where the network's nodes are hosted. The precise geographic distribution of these nodes is a crucial piece of information for accurately assessing renewable energy integration.

In instances where comprehensive information regarding the geographic distribution of nodes is unavailable or insufficient, the methodology incorporates a fallback mechanism. This involves using reference networks that exhibit comparable characteristics in terms of their incentivization structures and underlying consensus mechanisms. By analyzing the renewable energy usage patterns of these similar networks, an informed estimate can be made for BSC. Once geographical data for the nodes (either direct or inferred from reference networks) is established, this geo-information is meticulously merged with publicly accessible data from Our World in Data. This external dataset provides crucial insights into the share of electricity generated by renewables globally, drawing from sources like Ember (2025) and the Energy Institute’s Statistical Review of World Energy (2024). The integration of this data allows for a granular understanding of the renewable energy mix at the node locations.

Furthermore, the energy intensity of the network is calculated as the marginal energy cost with respect to one additional transaction. This metric quantifies the energy expenditure incurred for each incremental transaction processed on the network, providing a measure of its operational efficiency from an energy perspective. The consistent use of reputable public data sources and a robust methodology ensures transparency and accuracy in reporting the renewable energy profile of the Binance Smart Chain network.

The methodology for determining the key energy sources and the proportion of renewable energy utilized by the Hyperliquid blockchain network involves a multi-pronged approach focused on identifying the geographical distribution of its operational nodes. The primary method for establishing node locations leverages a combination of publicly available information sites, advanced open-source crawlers, and specialized in-house developed crawling technologies. These tools collectively work to pinpoint where Hyperliquid's validator and other network nodes are physically situated.In instances where precise geographic distribution data for the nodes is insufficient or unavailable, a pragmatic approach is adopted: reference networks are employed. These reference networks are carefully selected based on their demonstrable comparability to Hyperliquid in terms of both their incentivization structures and their underlying consensus mechanisms. By analyzing comparable networks, an informed estimation of the geographical energy mix can still be made.Once the geographical information pertaining to the nodes is gathered, it is systematically integrated with comprehensive public data sets sourced from Our World in Data. Specifically, this involves utilizing datasets like the "Share of electricity generated by renewables - Ember and Energy Institute" to understand the renewable energy penetration in those regions. This merger allows for a detailed assessment of the renewable energy proportion in Hyperliquid's operational energy consumption. The energy intensity of the network is then quantified as the marginal energy cost associated with the execution of one additional transaction.This rigorous methodology provides a robust framework for evaluating the network's renewable energy profile and its overall energy efficiency. Further details on the data sources can be found at Our World in Data.

Stable is present on the following networks: Binance Smart Chain, Hyperliquid.

The methodology for determining the Greenhouse Gas (GHG) Emissions associated with the Binance Smart Chain (BSC) network, much like the energy consumption assessment, places a strong emphasis on geographically situating its operational nodes. The initial step involves identifying the physical locations of these nodes, which is achieved through a combination of public information sites, open-source crawlers, and specialized in-house developed crawlers. Accurately mapping these locations is fundamental, as regional electricity mixes and their associated carbon footprints vary significantly.

In situations where detailed geographical information for all nodes is not readily available, the methodology incorporates a pragmatic approach. This involves utilizing reference networks that share similar characteristics, specifically in their incentivization structures and consensus mechanisms. By studying these comparable networks, reasonable inferences can be made about the likely geographic distribution and, consequently, the emissions profile of BSC's nodes. Once the geographic data is gathered or estimated, it is then meticulously integrated with publicly available information from Our World in Data. This authoritative dataset provides critical data on the carbon intensity of electricity generation across various regions, compiling information from sources such as Ember (2025) and the Energy Institute’s Statistical Review of World Energy (2024).

This integration allows for the calculation of GHG emissions based on the electricity consumption at specific node locations and the carbon intensity of those regional grids. The intensity of GHG emissions for the network is specifically calculated as the marginal emission with respect to one additional transaction. This metric quantifies the increase in GHG emissions for each incremental transaction processed on the network, offering a direct measure of its environmental impact per unit of activity. The entire process adheres to a principle of transparency, utilizing established external data sources and a consistent approach to ensure the reported GHG emissions are as accurate and comprehensive as possible, always acknowledging that the data from Our World in Data is licensed under CC BY 4.0.

The methodologies employed to ascertain the key Greenhouse Gas (GHG) sources and their associated emissions for the Hyperliquid blockchain network are directly linked to its operational infrastructure. The initial and critical step involves precisely determining the geographical locations of the network's nodes. This process relies on a combination of publicly accessible information sites, sophisticated open-source crawlers, and proprietary crawling systems developed specifically for this purpose. These tools are instrumental in identifying the physical whereabouts of Hyperliquid's various network participants, including validator nodes, which are central to its operation.Should there be a lack of concrete data concerning the geographic distribution of Hyperliquid's nodes, the methodology provides for the use of reference networks. These are carefully chosen based on their structural similarities to Hyperliquid, specifically in terms of their incentive frameworks and consensus mechanisms. This comparative analysis allows for an informed estimation of the GHG impact even when direct geographical data is sparse.Once the geographical data for the nodes is successfully compiled, it is then meticulously combined with public information obtained from Our World in Data. This integration specifically utilizes datasets such as the "Carbon intensity of electricity generation - Ember and Energy Institute." By merging the location data with region-specific carbon intensity figures, a comprehensive picture of the GHG emissions attributed to the electricity consumption of the Hyperliquid network can be constructed.The overall GHG intensity of the network is subsequently calculated as the marginal emission associated with the processing of one additional transaction. This metric offers insight into the incremental environmental impact of network activity. For more detailed information on the data sources and their licensing, please refer to Our World in Data. This resource is licensed under CC BY 4.0, ensuring transparency and accessibility of the underlying environmental data.