USD1 (USD1) sustainability report

USD1 is present on the following networks: Aptos Coin, Binance Smart Chain, Ethereum, Plume, Solana, Tron.

The Aptos blockchain network leverages a robust Proof-of-Stake (PoS) framework, integrated with a Byzantine Fault Tolerant (BFT) consensus protocol, to achieve high transaction throughput, minimal latency, and fortified security. A foundational component of this architecture is Block-STM, a parallel execution engine that allows for the simultaneous processing of multiple transactions. This parallel processing capability is critical for enhancing the network's overall performance and scalability, enabling it to handle a significant volume of operations efficiently. The consensus mechanism operates on a leader-based BFT model, where a specific validator is elected to propose new blocks. Following the proposal, other validators on the network are responsible for validating and subsequently finalizing these transactions. This structured approach ensures that transactions are processed in an orderly yet highly efficient manner.

To further bolster decentralization and mitigate the risk of collusion, Aptos incorporates a dynamic validator rotation system. This mechanism regularly shuffles the set of active validators, preventing any single entity or small group from gaining undue influence over the network's consensus process. A key feature of the Aptos network is its commitment to instant finality. Once transactions are validated by the network's participants, they achieve immediate and irreversible finality. This means that confirmed transactions cannot be altered or reversed, providing users and applications with a high degree of certainty and reliability. The combination of PoS for economic security, BFT for fault tolerance, parallel execution for performance, dynamic rotation for decentralization, and instant finality for reliability positions Aptos as a high-performance and secure blockchain platform designed for scalable applications.

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 Ethereum blockchain network, following "The Merge" in 2022, operates on a Proof-of-Stake (PoS) consensus mechanism, a significant departure from its previous Proof of Work system. This transition replaced energy-intensive mining with validator staking, aiming to enhance energy efficiency, security, and scalability. In this model, participants willing to secure the network act as validators by staking a minimum of 32 units of the network's native asset (Ether). The network organizes its operations around a precise slot and epoch system. Every 12 seconds, a validator is randomly selected to propose a new block. Following this proposal, other validators on the network verify the integrity and validity of the block. Finalization of transactions, meaning they become irreversible, occurs after approximately two epochs, which translates to about 12.8 minutes, utilizing the Casper-FFG (Friendly Finality Gadget) protocol. The Beacon Chain plays a central role in coordinating the activities of these validators, while the LMD-GHOST (Latest Message Driven-Greedy Heaviest Observed SubTree) fork-choice rule is employed to ensure all network participants agree on the canonical chain, following the branch with the heaviest accumulated validator votes. Validators are economically incentivized for their honest participation in proposing and verifying blocks, but they also face severe penalties, known as slashing, for malicious actions or prolonged inactivity. This PoS framework is designed not only to reduce the network's environmental footprint but also to lay the groundwork for future upgrades, such as Proto-Danksharding, which are intended to further improve transaction efficiency and overall network throughput. The core components like validator selection, block production, and transaction finality are intrinsically tied to the amount of Ether staked, ensuring that participants have a vested interest in the network's security and stability.

The Plume blockchain network operates on an architecture built as an optimistic rollup within the Arbitrum Orbit framework, fundamentally deriving its security and finality from the underlying Ethereum blockchain. This means Plume does not maintain an independent consensus mechanism but rather relies on Ethereum's Proof-of-Stake (PoS) system for settlement and transaction finalization. Ethereum transitioned to PoS with "The Merge" in 2022, replacing energy-intensive mining with a validator staking model. Under this mechanism, individuals or entities wishing to become validators must stake a minimum of 32 ETH. Periodically, a validator is randomly chosen to propose the next block, which then undergoes verification by other participating validators to ensure its integrity before being added to the chain. The network functions based on a precise slot and epoch system, where a new block is consistently proposed every 12 seconds. Finality, or the irreversible confirmation of transactions, is achieved after approximately two epochs, equating to about 12.8 minutes, utilizing the Casper-FFG protocol. The Beacon Chain plays a central role in orchestrating validators, while the LMD-GHOST fork-choice rule is employed to guarantee that the network always follows the chain with the most accumulated validator votes. Validators are incentivized through rewards for successfully proposing and verifying blocks, but they also face significant penalties, known as slashing, for engaging in malicious activities or extended periods of inactivity. This PoS design not only aims to enhance the network's energy efficiency significantly compared to Proof-of-Work systems but also bolster its security and scalability, with planned future upgrades like Proto-Danksharding intended to further optimize transaction efficiency. Plume's integration with this robust framework ensures a secure and efficient operational environment.

The Solana blockchain architecture operates through a hybrid consensus model that integrates Proof of History (PoH) with Proof of Stake (PoS). This combination is designed to optimize transaction throughput and reduce network latency while maintaining a high degree of security. Proof of History functions as a decentralized clock, using a Verifiable Delay Function (VDF) to create a permanent, timestamped record of events. This cryptographic sequence allows the network to agree on the chronological order of transactions without requiring nodes to communicate extensively, thereby solving traditional synchronization bottlenecks found in other distributed ledgers. Parallel to PoH, the Proof of Stake component manages the selection of validators and the finalization of the ledger state. Validators are chosen to act as leaders for specific blocks based on the total quantity of the native network assets they have staked. Users who do not run their own hardware can participate in network security by delegating their assets to existing validators, sharing in the rewards generated by successful block production. The consensus process begins when transactions are broadcast and collected for validation. A designated leader then generates a PoH sequence to order these transactions within a block. Subsequently, other validators in the network verify the integrity of the PoH hashes and the validity of the transactions. Once a sufficient number of signatures are collected, the block is finalized and appended to the blockchain. This dual approach ensures that the network remains resilient against attacks; validators must provide collateral through staking, and any malicious activity, such as producing invalid blocks or double-signing, can result in the loss of staked assets through a process known as slashing. This economic deterrent ensures that participants remain aligned with the network's health and operational standards.

The Tron blockchain utilizes a Delegated Proof of Stake (DPoS) consensus mechanism, specifically engineered to enhance scalability, boost transaction speeds, and improve energy efficiency compared to alternative consensus models. This system's core relies on token holders actively participating in network governance and security. A primary component of DPoS on Tron involves token holders voting for a select group of delegates known as Super Representatives (SRs). These SRs are crucial for validating transactions and generating new blocks that are then added to the blockchain. The selection process dictates that token holders cast their votes based on their stake in the Tron network, with the top 27 SRs (or potentially more, depending on protocol updates) being chosen to actively engage in the block production cycle. Block production on the Tron network is highly efficient, with SRs taking turns to produce blocks in a rotational manner, ensuring a degree of decentralization and preventing undue influence by any single entity. This rotational system allows the Tron blockchain to achieve rapid block finality, with new blocks being produced approximately every 3 seconds. Such speed enables the network to process thousands of transactions per second, making it suitable for high-throughput applications. Beyond transaction validation, the DPoS framework empowers Tron token holders to participate in vital network decisions. Their voting power is directly proportional to the amount of TRX, Tron’s native token, they hold and choose to stake. This robust governance system allows the community to influence protocol upgrades and changes to the network's operational parameters, fostering an engaged and decentralized decision-making environment. The Super Representatives are fundamental to maintaining the Tron blockchain's security and stability, carrying the responsibility for validating transactions, proposing new blocks, and ensuring overall network functionality, for which they are compensated with block rewards and transaction fees.

USD1 is present on the following networks: Aptos Coin, Binance Smart Chain, Ethereum, Plume, Solana, Tron.

The Aptos blockchain network employs a comprehensive system of incentive mechanisms and applicable fees designed to ensure network security, encourage participation, and maintain operational efficiency. A core incentive for network participants is the validator reward system. Validators, who are crucial for validating transactions and producing new blocks, earn rewards in the network's native token. These rewards are distributed proportionally, reflecting both the amount of tokens staked by the validators themselves and the contributions from their delegators. This direct financial incentive encourages validators to maintain high uptime and act honestly in their network duties.

Further promoting broader participation, Aptos allows for delegator involvement. Token holders who prefer not to operate their own validator nodes can delegate their tokens to existing validators. By doing so, delegators earn a share of the staking rewards, effectively participating in network security and earning passive income without the technical overhead of running a node. To uphold accountability and safeguard network integrity, a slashing mechanism is in place. Validators who engage in malicious activities, such as double-signing transactions, or who experience prolonged inactivity, face penalties that involve the forfeiture of a portion of their staked tokens. This economic disincentive acts as a strong deterrent against dishonest behavior.

Regarding applicable fees, users engaging with the Aptos network are required to pay transaction fees in the native token for sending transactions and interacting with smart contracts. These fees are not static; Aptos features a dynamic fee adjustment system that recalibrates fees based on current network activity and resource utilization. This dynamic approach helps ensure cost efficiency for users and prevents network congestion during periods of high demand. The collected transaction fees are then distributed among validators and their delegators, providing an additional layer of economic incentive for their continued active participation and crucial role in securing the network.

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 Ethereum network's Proof-of-Stake (PoS) system is underpinned by a robust framework of incentive mechanisms and applicable fees, meticulously designed to secure transactions and encourage active, honest participation from validators. Validators, who are essential for the network's operation, commit at least 32 units of the network's native asset (Ether) to secure their role. Their primary incentives include rewards for successfully proposing new blocks, attesting to the validity of other blocks, and participating in sync committees, all of which contribute to the network's integrity and consensus. These rewards are distributed in newly issued Ether, alongside a portion of the transaction fees generated on the network. A key feature of Ethereum's fee structure is the implementation of EIP-1559, which divides transaction fees into two main components. The first is a base fee, which is automatically burned, effectively reducing the overall supply of Ether over time and potentially introducing a deflationary aspect, especially during periods of high network activity. The second is an optional priority fee, also known as a "tip," which users can choose to pay directly to validators to incentivize faster inclusion of their transactions into a block. This dual-fee structure aims to make transaction costs more predictable for users. To enforce honest behavior and prevent malicious activities, the network employs a strict system of economic penalties, including slashing. Validators who engage in dishonest acts or demonstrate extended periods of inactivity risk losing a portion of their staked Ether, providing a powerful deterrent against misconduct and ensuring the long-term security and reliability of the network. This comprehensive system aligns the economic interests of validators with the overall health and security of the Ethereum blockchain.

The Plume network, as an optimistic rollup built on the Ethereum blockchain, leverages Ethereum's established incentive mechanisms and fee structure to ensure transaction security and network integrity. In this Proof-of-Stake (PoS) system, validators play a crucial role by staking a minimum of 32 ETH. These validators are economically motivated through rewards, which are paid in newly issued ETH and a share of transaction fees, for their participation in proposing valid blocks, attesting to the correctness of others, and engaging in sync committees. Conversely, the system incorporates stringent economic penalties, such as slashing, for validators found to be acting maliciously or for prolonged periods of inactivity, thereby aligning their interests with the network's health and security. Transaction fees on the underlying Ethereum network, governed by the EIP-1559 standard, are structured to be more predictable and to introduce a deflationary mechanism during high network demand. This structure comprises a base fee, which is automatically burned to reduce the overall supply of ETH, and an optional priority fee (or "tip") that users can pay to validators to expedite their transaction processing. For Plume specifically, transaction fees serve as a fundamental economic mechanism vital for supporting its network operations and security. These fee flows are utilized to compensate various critical network roles, which may include sequencer-related functions responsible for ordering transactions, as well as validator-related functions inherent to the rollup architecture. Additionally, the native token, PLUME, is described as being utilized in connection with various incentive and participation mechanisms, including potential staking arrangements and interactions within decentralized finance (DeFi) applications. It is important to note that any yields, rewards, or economic benefits associated with PLUME are dynamic and dependent on protocol parameters, prevailing market conditions, and user behavior, subject to potential changes through governance or technical updates.

Incentives within the Solana blockchain network are structured to ensure high performance and decentralized security. The primary participants are validators and delegators, both of whom receive financial compensation for their roles in maintaining the ledger. Validators are rewarded for successfully producing and verifying blocks. These rewards are distributed in the network's native asset and are determined by the validator's overall stake and historical performance. Furthermore, validators receive a portion of the transaction fees associated with the data processed in their blocks, which encourages them to maximize efficiency and maintain uptime. Token holders who prefer not to operate complex infrastructure can delegate their stake to professional validators. This delegation model facilitates a more inclusive security environment, as delegators earn a percentage of the rewards proportional to their contribution, thereby decentralizing the control of the network. Security is further enforced through economic penalties. The network employs a slashing mechanism where a portion of a validator's staked assets is confiscated if they engage in dishonest behavior or fail to meet network requirements, such as remaining offline for extended periods. This introduces an opportunity cost for all participants, ensuring they remain committed to honest operations. Regarding the cost of using the network, the fee structure is designed to be highly competitive and predictable. Users pay transaction fees to compensate for the computational power and bandwidth consumed by nodes. These fees are notably low, facilitating high-volume usage. In addition to transaction costs, the network implements rent fees for data storage. This unique mechanism charges for the persistence of data on the blockchain, discouraging the inefficient use of state storage and prompting developers to prune unnecessary data. Finally, smart contract execution fees are calculated based on the specific resource intensity of the code, ensuring that participants pay a fair rate for the network resources they utilize.

The Tron blockchain implements a comprehensive set of incentive mechanisms, underpinned by its Delegated Proof of Stake (DPoS) consensus model, designed to ensure network security, encourage participation, and maintain operational efficiency. Central to this system are the Super Representatives (SRs), who are directly rewarded for their critical roles. SRs, elected by TRX token holders, receive block rewards in the form of newly minted TRX tokens for each block they successfully produce. Additionally, they are compensated with transaction fees for validating and incorporating transactions into these blocks, providing a continuous income stream that incentivizes efficient transaction processing. Further incentivizing network engagement, Tron encourages token holders to stake their TRX and vote for SRs. This delegation of voting power allows SRs to earn rewards, and in turn, delegators—those who stake their tokens and vote—can also receive a share of these block rewards and transaction fees. This shared reward structure fosters broad participation in network security and governance, as increased staking leads to greater voting power and potential rewards. SRs are also motivated by reputation and the necessity of consistent, efficient block production to maintain their elected status. Regarding applicable fees, users on the Tron network incur several types of charges, primarily paid in TRX tokens. Transaction fees are mandatory for processing transactions, and their cost fluctuates based on the transaction's complexity and the current network demand. These fees are distributed among the Super Representatives. Additionally, Tron charges storage fees for data stored on the blockchain, including smart contracts and tokens, requiring users to pay in TRX. The network also employs a resource model where staking TRX tokens grants users access to essential network resources like bandwidth and energy. This innovative resource system effectively manages network capacity and demand, optimizing performance and user experience by allowing resource acquisition through staking.

USD1 is present on the following networks: Aptos Coin, Binance Smart Chain, Ethereum, Plume, Solana, Tron.

The methodology for calculating the energy consumption of the Aptos network, like other digital ledger technologies (DLTs), employs a 'bottom-up' approach, focusing primarily on the energy consumption of individual nodes within the network. This comprehensive method aggregates energy usage across various components contributing to the network's operation. The core assumption underpinning this calculation is that the nodes represent the most significant factor in the network's overall energy footprint. These estimations are built upon empirical data gathered from diverse sources, including publicly available information sites, as well as both open-source and proprietary in-house crawlers.

A critical determinant in assessing hardware energy consumption involves identifying the specific hardware required to run the client software for the network. The energy consumption profiles of these hardware devices are meticulously measured in certified test laboratories, ensuring accuracy in the base data. When conducting these calculations, if available, the Functionally Fungible Group Digital Token Identifier (FFG DTI) is utilized to accurately identify all implementations of the crypto-asset within scope. These mappings are regularly updated, drawing data from the Digital Token Identifier Foundation, to maintain the most current and precise attribution.

Information concerning the specific hardware deployed across the network and the total number of participants is derived from assumptions. These assumptions are subjected to rigorous verification efforts using empirical data to ensure their best possible accuracy. A general principle guiding these assumptions is that network participants are largely economically rational actors. Furthermore, adopting a precautionary stance, in instances of doubt, conservative estimates are applied, meaning higher figures are used for potential adverse impacts to ensure a robust and responsible assessment of energy consumption.

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 calculating the Ethereum network's energy consumption primarily employs a "bottom-up" approach, which focuses on the energy demands of individual nodes that are central to the network's operation. These nodes are considered the fundamental factor driving the network's overall energy use. The assumptions underpinning these calculations are derived from empirical data gathered through a variety of sources, including public information sites, open-source crawlers, and proprietary in-house crawlers developed for this purpose. A critical step in this methodology involves determining the hardware used within the network, primarily by assessing the computational and other requirements necessary to run the client software. The energy consumption characteristics of these identified hardware devices are then rigorously measured in certified test laboratories to ensure accuracy. When quantifying the energy consumption for the network, the Functionally Fungible Group Digital Token Identifier (FFG DTI) is utilized, when available, to identify all implementations of the asset in scope, with mappings regularly updated based on data from the Digital Token Identifier Foundation. The information regarding the specific hardware deployed and the total number of participants in the network relies on assumptions that are diligently verified using empirical data whenever possible. Generally, participants are presumed to act in an economically rational manner. Furthermore, adhering to a precautionary principle, if there is any doubt in estimations, conservative assumptions are made, meaning higher estimates are used for potential adverse impacts to ensure a comprehensive and cautious assessment of energy consumption.

The Plume blockchain network's energy consumption is primarily determined by its operational architecture as an optimistic rollup anchored to the Ethereum mainnet. Consequently, Plume does not possess an independent energy consumption profile separate from its foundational Layer 1. The methodology for calculating its energy usage is thus intrinsically linked to the energy expenditure of the underlying Ethereum network. To ascertain Plume's specific energy consumption, the overall energy consumption of the Ethereum network is first computed. A fractional portion of this total Ethereum energy consumption is then attributed to Plume, proportional to its activity and footprint within the broader Ethereum ecosystem. This allocation is based on the specific usage and operations conducted on the Plume network. Furthermore, when calculating the energy consumption, the Functionally Fungible Group Digital Token Identifier (FFG DTI) is utilized, where available, to accurately identify all implementations of the crypto-asset within scope. The mappings associated with these identifiers are conscientiously updated on a regular basis, drawing data from the Digital Token Identifier Foundation, ensuring the most current and precise information. The underlying assumptions regarding the hardware employed across the network and the aggregate number of participants are rigorously verified through empirical data, reflecting a best-effort approach to accuracy. A core principle of this methodology assumes that participants generally act in an economically rational manner. Moreover, to maintain a cautious and responsible stance, a precautionary principle is applied, leading to conservative assumptions that typically result in higher estimates for potential adverse environmental impacts when there is any uncertainty. This comprehensive approach aims to provide a robust estimate of the network's energy footprint.

To calculate the energy consumption of the Solana blockchain network, a "bottom-up" methodology is utilized, placing the network nodes at the center of the analysis. This approach relies on identifying the number of active participants and the specific hardware requirements necessary to run the network's client software. Data collection involves a variety of sources, including open-source web crawlers, internal monitoring tools developed by the legal entities, and public information websites. By analyzing these data points, researchers can estimate the hardware profiles of the various nodes operating globally. To ensure accuracy, the energy consumption of typical hardware devices is measured within certified laboratory environments, providing a baseline for the power usage of each node. Furthermore, the methodology incorporates data from the Digital Token Identifier Foundation to map all implementations of the assets within the network's scope. When specific hardware data is not directly observable, assumptions are made based on the principle of economic rationality, assuming participants optimize their setups for cost-efficiency while meeting software specifications. In instances of uncertainty, a precautionary principle is applied, favoring conservative estimates that likely overstate the environmental impact rather than underestimating it. This ensures that the reported energy footprint represents a credible upper bound of actual consumption. The total network consumption is determined by aggregating the energy needs of all identified nodes, accounting for both the computational requirements of processing transactions and the energy consumed by hardware in an idle or supportive state. This rigorous framework allows for a comprehensive assessment of the network’s total power requirements over a defined reporting period, providing a transparent view of the operational costs associated with maintaining the distributed ledger's infrastructure.

The methodology for calculating the energy consumption of the Tron blockchain network primarily adopts a "bottom-up" approach, which aggregates energy usage across multiple individual components of the network. The central assumption underpinning this calculation is that the nodes constitute the predominant factor in the network's overall energy consumption. The estimations for hardware utilized within the network are derived from the operational requirements of the client software. These assumptions are meticulously formulated based on empirical findings gathered through the deployment of various data collection tools, including public information sites, open-source crawlers, and proprietary in-house developed crawlers. The energy consumption data for the specific hardware devices identified is obtained through measurements conducted in certified test laboratories, ensuring a high degree of accuracy and reliability. When determining the scope of assets for energy consumption calculations, the Functionally Fungible Group Digital Token Identifier (FFG DTI) is employed whenever available to identify all relevant implementations of the crypto-asset within the network. These FFG DTI mappings are routinely updated using data from the Digital Token Identifier Foundation, ensuring the methodology remains current and comprehensive. The information related to the hardware in use and the total number of network participants is based on assumptions that undergo rigorous verification through empirical data. A general principle assumes participants are largely economically rational. Furthermore, a precautionary principle is applied, favoring conservative estimates (i.e., higher estimates for potential adverse impacts) when any doubt or uncertainty exists in the data or assumptions. No direct external links were provided within the source material for this section's methodologies or data sources.

USD1 is present on the following networks: Aptos Coin, Binance Smart Chain, Ethereum, Plume, Solana, Tron.

The methodology for determining the key energy sources utilized by the Aptos network, and by extension the proportion of renewable energy, begins with precisely identifying the geographical locations of its operational nodes. This intricate process involves the systematic use of public information sources, coupled with advanced open-source and proprietary in-house crawlers, to pinpoint where these nodes are situated. In scenarios where direct geographical information for specific nodes is unavailable or insufficient, the methodology intelligently references comparable DLT networks. These reference networks are carefully chosen based on their similarities in incentivization structures and consensus mechanisms, providing a proxy for estimating energy source mixes.

Once the geographical data for the nodes is established, it is then meticulously integrated with publicly available energy mix data provided by 'Our World in Data.' This comprehensive dataset offers detailed insights into the energy generation profiles across various regions, allowing for an informed estimation of the renewable energy proportion powering the network's operations. The energy intensity metric, which quantifies the energy cost per additional transaction, is calculated as the marginal energy cost associated with processing just one more transaction. This provides a granular view of the energy efficiency.

The data sources for renewable energy usage, which are crucial for this assessment, include a range of reputable publications such as Ember (2025) and the Energy Institute's Statistical Review of World Energy (2024). These sources are heavily processed by Our World in Data to generate the 'Share of electricity generated by renewables' dataset, which is directly referenced. Users seeking to explore this data further can consult the original source: Share of electricity generated by renewables - Ember and Energy Institute. This robust approach ensures that energy consumption and renewable energy integration are assessed with the highest possible degree of transparency and accuracy.

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.

To ascertain the proportion of renewable energy utilized by the Ethereum network, a specific set of methodologies is applied. The initial step involves pinpointing the geographical locations of the network's nodes. This crucial geo-information is gathered through various means, including publicly available information sites, as well as both open-source and internally developed crawlers designed to scan the network. In instances where comprehensive geographical data for nodes is not directly accessible, the analysis resorts to leveraging "reference networks." These are comparable networks chosen for their similar incentivization structures and consensus mechanisms, providing a proxy for node distribution. Once the geo-information is established, it is then integrated and cross-referenced with public data obtained from "Our World in Data." This comprehensive dataset offers insights into the energy mixes and renewable energy penetration across different regions globally. The final calculation of energy intensity is defined as the marginal energy cost incurred for processing one additional transaction on the network. This approach allows for an estimation of the energy footprint associated with scaling the network's transactional volume. For detailed information and the underlying data sources on the share of electricity generated by renewables, relevant information can be found through sources such as Ember (2025) and the Energy Institute - Statistical Review of World Energy (2024), with further processing by Our World in Data, accessible via Share of electricity generated by renewables – Ember and Energy Institute.

For the Plume blockchain network, given its design as an optimistic rollup that relies on the Ethereum mainnet for finality and security, the key energy sources are inherently those of the underlying Ethereum infrastructure. Plume does not operate its own distinct energy generation or consumption facilities, but rather inherits the energy profile of the Ethereum network to which it is anchored. The methodology employed to determine the proportion of renewable energy usage within this broader context involves a meticulous process of identifying the geographical locations of network nodes. This is achieved through a combination of publicly available information sites, advanced open-source crawlers, and proprietary in-house developed crawling tools. Should there be insufficient data on the precise geographic distribution of nodes directly associated with Plume's underlying Layer 1, reference networks are strategically utilized. These reference networks are selected based on their comparability in terms of incentivization structures and consensus mechanisms, ensuring that the estimations remain relevant and accurate. The gathered geo-information is then systematically integrated with extensive public data provided by Our World in Data, which offers detailed insights into global energy statistics and renewable energy shares. This integration allows for a robust assessment of the renewable energy mix contributing to the network's operations. The calculation of energy intensity is defined as the marginal energy cost incurred with respect to one additional transaction processed on the network. This metric provides a crucial understanding of the energy efficiency of the blockchain's operational activities. The specific data sources leveraged for these analyses include Ember (2025) and the Energy Institute's Statistical Review of World Energy (2024), with substantial processing by Our World in Data. The primary source for "Share of electricity generated by renewables" data can be accessed via Share of electricity generated by renewables - Ember and Energy Institute. This comprehensive methodology aims to provide a transparent and accurate assessment of the network's energy characteristics.

The determination of energy sources for the Solana blockchain network involves a sophisticated geolocation mapping of the global node infrastructure. By utilizing internal and open-source crawlers, the physical locations of validator nodes are identified. Once the geographic distribution is established, this information is cross-referenced with regional energy data to calculate the percentage of renewable energy utilized by the network. For regions where specific node data is unavailable, researchers utilize reference networks that share similar consensus mechanisms and incentive structures as proxies to estimate the geographic spread of the infrastructure. The primary data source for these regional energy profiles is the Share of electricity generated by renewables dataset provided by Our World in Data, which incorporates research from Ember and the Energy Institute. This dataset provides yearly electricity data that allows for a granular assessment of how much of the network's power is derived from wind, solar, hydro, and other renewable sources. In addition to the total percentage of green energy, the methodology focuses on energy intensity, which is defined as the marginal energy cost required to process a single additional transaction on the network. This figure helps quantify the efficiency of the blockchain's resource usage relative to its utility. By integrating global energy statistics with real-time node distribution data, the network can report a more accurate picture of its sustainability, currently indicating that a significant portion of its operational energy comes from renewable sources, reflecting the broader global transition toward cleaner power grids.

To ascertain the proportion of renewable energy utilized by the Tron blockchain network, a comprehensive methodology is employed that primarily focuses on determining the geographical locations of its operational nodes. This critical geo-information is obtained through various data collection methods, including the use of public information sites, sophisticated open-source crawlers, and specialized crawlers developed in-house. These tools allow for a detailed mapping of where the network's infrastructure is physically situated. In instances where specific geographic distribution data for the nodes is unavailable, the methodology prudently relies on reference networks. These reference networks are carefully selected based on their comparability to Tron in terms of their incentivization structures and underlying consensus mechanisms, ensuring that the estimated renewable energy usage remains relevant and indicative despite data gaps. Once the geographical data is established, it is then meticulously merged with extensive public information derived from "Our World in Data." This integration allows for a robust assessment of the energy mix, including the share of renewable electricity, at the determined node locations. "Our World in Data" aggregates and processes data from reputable sources such as Ember's "Yearly Electricity Data Europe" and "Yearly Electricity Data," as well as the Energy Institute's "Statistical Review of World Energy." The energy intensity of the network is calculated as the marginal energy cost with respect to one additional transaction, providing a metric for the energy efficiency of each individual operation on the blockchain. The primary data source for the share of electricity generated by renewables, which informs this calculation, is available via Share of electricity generated by renewables – Ember and Energy Institute.

USD1 is present on the following networks: Aptos Coin, Binance Smart Chain, Ethereum, Plume, Solana, Tron.

The methodology for quantifying Greenhouse Gas (GHG) emissions attributable to the Aptos network relies on a detailed analysis that starts with identifying the geographical locations of its network nodes. Similar to energy source determination, this involves extensive data collection using public information sites, alongside specialized open-source and in-house developed crawlers. Should specific geographical data for nodes be incomplete, the approach mandates the use of reference networks that exhibit comparable incentivization frameworks and consensus mechanisms. This ensures that a reasonable estimation of the emission profile can still be achieved, even when direct information is scarce.

Upon obtaining the geographical distribution of nodes, this geo-information is subsequently integrated with publicly accessible data from 'Our World in Data.' This critical step allows for the correlation of node locations with regional carbon intensity of electricity generation, thereby enabling the calculation of associated GHG emissions. The overall GHG intensity is then calculated as the marginal emission generated by each additional transaction. This provides a precise measure of the environmental impact per unit of network activity, highlighting the efficiency of the network's operations from an emissions perspective.

The primary data sources underpinning these GHG emission calculations are contributions from Ember (2025) and the Energy Institute's Statistical Review of World Energy (2024), which undergo significant processing by Our World in Data. Specifically, the 'Carbon intensity of electricity generation' dataset is utilized for these estimations. Further details and the original data can be found at: Carbon intensity of electricity generation - Ember and Energy Institute. This information is licensed under CC BY 4.0, emphasizing its open and verifiable nature.

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 methodology for determining the Greenhouse Gas (GHG) emissions of the Ethereum network closely mirrors the approach used for energy consumption, focusing on identifying emission sources and their quantification. The initial and fundamental step involves precisely identifying the geographical locations of the network's operational nodes. This data collection is facilitated through a combination of publicly available information, as well as specialized open-source and proprietary crawlers designed to actively discover and map node distributions across the globe. Should there be an absence of specific geographic information for the nodes, the analysis intelligently defaults to utilizing "reference networks." These are carefully selected networks that exhibit comparable characteristics in terms of their incentivization structures and consensus mechanisms, providing a basis for estimating the geographic spread when direct data is unavailable. This collected geo-information is then meticulously integrated with publicly accessible data from "Our World in Data." This integration allows for the application of regional carbon intensity factors to the estimated energy consumption, thereby enabling the calculation of associated GHG emissions. The overall GHG intensity is quantified as the marginal emission generated per additional transaction processed on the network, offering a metric for the environmental impact of increased network activity. For detailed information and original data regarding the carbon intensity of electricity generation, sources include Ember (2025) and the Energy Institute - Statistical Review of World Energy (2024), processed by Our World in Data, available at Carbon intensity of electricity generation – Ember and Energy Institute. This resource is licensed under CC BY 4.0.

As an optimistic rollup network that secures its transactions and finality through the Ethereum mainnet, the Plume blockchain network's Greenhouse Gas (GHG) emissions are fundamentally tied to the operational footprint of the underlying Ethereum infrastructure. Plume does not generate direct GHG emissions from its own distinct energy sources; instead, its emission profile is a reflection of the electricity sources powering the Ethereum network. The methodology for assessing these GHG emissions involves a detailed process of pinpointing the geographical locations of network nodes. This geographical data is collected using a combination of publicly accessible information sites, sophisticated open-source crawlers, and specialized in-house developed crawling technologies. In instances where comprehensive information regarding the geographic distribution of these nodes is unavailable, the methodology resorts to employing reference networks. These reference networks are carefully chosen for their similarities in incentivization structures and consensus mechanisms to ensure the relevance and reliability of the emissions estimations. The geo-information thus acquired is meticulously merged with publicly available data sourced from Our World in Data, which provides comprehensive statistics on the carbon intensity of electricity generation globally. This integration facilitates an informed calculation of the GHG emissions associated with the network's electricity consumption. The GHG intensity is specifically quantified as the marginal emission generated with respect to processing one additional transaction on the network, offering a precise measure of its environmental impact per unit of activity. Key data sources underpinning these calculations include Ember (2025) and the Energy Institute’s Statistical Review of World Energy (2024), with significant analytical contributions from Our World in Data. Information regarding the "Carbon intensity of electricity generation" is available from Carbon intensity of electricity generation - Ember and Energy Institute, which is licensed under CC BY 4.0. This rigorous approach ensures a comprehensive and transparent accounting of the network's GHG footprint.

Quantifying the greenhouse gas (GHG) emissions of the Solana blockchain network requires a methodology focused on carbon intensity and the geographic footprint of its decentralized nodes. Similar to the energy source analysis, the process begins by locating active nodes using a combination of public data and specialized web crawling technology. This geographic information is critical because the carbon footprint of electricity varies significantly between different jurisdictions depending on their local power generation mix. For nodes that cannot be precisely located, the analysis uses data from comparable blockchain networks to ensure the estimation remains as complete as possible. The carbon intensity of the electricity used by these nodes is derived from the Carbon intensity of electricity generation dataset, accessible via Our World in Data. This dataset, which is licensed under CC BY 4.0, provides essential metrics on the amount of CO2 equivalent emitted per kilowatt-hour of electricity produced in various countries. By merging node locations with these carbon intensity values, the network can calculate its Scope 2 emissions, which represent the indirect emissions from the generation of purchased electricity. The methodology also focuses on GHG intensity, measuring the marginal emissions generated by one additional transaction on the blockchain. This allows for a performance-based assessment of the network's environmental impact. The results are typically reported in tonnes of CO2 equivalent (tCO2e), providing a standardized metric that allows for comparison with other industries and financial systems. This data-driven approach ensures that the network’s environmental disclosures are rooted in empirical global energy statistics and verifiable infrastructure data.

The methodology for determining Greenhouse Gas (GHG) Emissions associated with the Tron blockchain network mirrors the approach used for energy consumption, by first establishing the geographical locations of the network's operating nodes. This crucial geographical information is diligently identified using a combination of public information sites, various open-source crawlers, and proprietary in-house developed crawlers. In situations where precise geographic distribution data for the nodes is not directly accessible, the assessment methodology intelligently falls back on employing reference networks. These selected reference networks are chosen specifically for their comparable incentivization structures and consensus mechanisms to Tron, ensuring that the GHG emission estimates remain relevant and methodologically sound. Upon the successful determination of node locations, this geo-information is then integrated with publicly available data sourced from "Our World in Data." This comprehensive database, which processes contributions from entities such as Ember and the Energy Institute’s Statistical Review of World Energy, provides essential insights into the carbon intensity of electricity generation across different regions. The process facilitates a robust calculation of the network's overall GHG emissions. The intensity of these emissions is calculated as the marginal emission with respect to processing one additional transaction on the blockchain. This metric offers a nuanced understanding of the environmental footprint per unit of network activity. The key data source utilized for the carbon intensity of electricity generation is accessible through Carbon intensity of electricity generation – Ember and Energy Institute. This source is licensed under CC BY 4.0.