Yearn.Finance (YFI) sustainability report

yearn.finance is present on the following networks: Arbitrum, Avalanche, Base, Ethereum, Fantom, Harmony One, Huobi, Near Protocol, Optimism, Polygon, Gnosis Chain.

Arbitrum, an innovative Layer 2 scaling solution built on top of Ethereum, utilizes an Optimistic Rollup consensus mechanism to significantly enhance transaction scalability and reduce operational costs. This optimistic approach operates on the fundamental assumption that all transactions processed off-chain are valid by default. Consequently, transactions only undergo a rigorous verification process if their validity is explicitly challenged during a specific time window.

The core architecture of the Arbitrum network integrates several key components essential for its functionality. The Sequencer plays a pivotal role by efficiently ordering user transactions and aggregating them into batches, which are then processed off-chain. This mechanism is critical for achieving high transaction throughput and maintaining network efficiency. A Bridge facilitates secure and seamless transfers of assets between the Arbitrum Layer 2 environment and the underlying Ethereum Layer 1 mainnet, ensuring interoperability and leveraging Ethereum's robust security. Safeguarding the network from malicious activities are Fraud Proofs, an interactive verification system designed to detect and invalidate fraudulent transactions.

The transaction verification process unfolds as follows: users first submit their transactions to the Arbitrum Sequencer. The Sequencer orders these transactions, bundles them into batches, and subsequently submits these batches along with a cryptographic "state commitment" to the Ethereum mainnet. A crucial "challenge period" then commences, during which any network validator can initiate a fraud proof if they suspect an invalid state transition. Should a challenge be raised, an iterative dispute resolution protocol is activated to pinpoint the exact fraudulent step. If fraud is confirmed, the system rolls back the incorrect state, and the dishonest party is subjected to penalties. The final, validated state is then executed on the Ethereum blockchain, preserving the rollup's integrity. This combination of off-chain computation, batching, and on-chain fraud detection, as seen in networks built on the Arbitrum Nitro stack like Kinto, enables high transaction volumes at considerably lower fees.

The Avalanche blockchain network implements a sophisticated Proof-of-Stake (PoS) mechanism known as Avalanche Consensus, distinguishing itself from many other PoS protocols by incorporating a novel, subsampling-based approach rather than a traditional Byzantine Fault Tolerant (BFT) consensus. This unique consensus process is built upon three integrated protocols: Snowball, Snowflake, and Avalanche, all working in concert to achieve high throughput, rapid finality, and robust security. The process begins with the Snowball protocol, where each validator randomly samples a small, fixed-size group of other validators. Through repeated polling of these sampled validators, a preference for a particular transaction is established. Validators maintain confidence counters for each transaction, incrementing them as sampled validators express support for their chosen transaction. A transaction is deemed accepted once its confidence counter surpasses a predefined threshold. Building upon Snowball, the Snowflake protocol refines the process by introducing a binary decision system, compelling validators to choose between two conflicting transactions. Binary confidence counters track the preferred binary choice, and once a specific confidence level is attained, the decision becomes final and irreversible. The overarching Avalanche protocol organizes transactions using a Directed Acyclic Graph (DAG) structure. This DAG architecture is crucial for facilitating parallel transaction processing, which significantly enhances the network's overall throughput and efficiency. Transactions are added to the DAG based on their intrinsic dependencies, ensuring a consistent and logical order across the network. Ultimately, validators reach consensus on both the structure and content of this DAG through the iterative application of the Snowball and Snowflake protocols. The Avalanche X-Chain, a component of the broader Avalanche network, also utilizes this Avalanche consensus protocol, emphasizing repeated subsampling of validators to achieve agreement on transactions. Furthermore, networks like Flare integrate the Avalanche Consensus with a Federated Byzantine Agreement (FBA) model to further bolster scalability, security, and decentralization, leveraging a gossip protocol for rapid node communication and transaction confirmation.

Base operates as a Layer-2 (L2) scaling solution built on the Ethereum blockchain, having been developed by Coinbase using Optimism's OP Stack. Critically, Base L2 transactions do not possess an independent consensus mechanism. Instead, their validation is directly linked to and secured by the underlying Ethereum Layer-1 (L1) network. This is achieved through a specialized component known as a sequencer. The sequencer's role is to aggregate multiple L2 transactions into bundles, which are then regularly published to the Ethereum mainnet as a single L1 transaction.

Consequently, all transactions processed on the Base network are indirectly secured by Ethereum's robust Proof-of-Stake (PoS) consensus mechanism once they are recorded on L1. Ethereum's PoS system, established with "The Merge" in 2022, moves away from energy-intensive mining by requiring validators to stake at least 32 ETH. In this system, a validator is randomly selected every 12 seconds to propose a new block, while other validators on the network are responsible for verifying its integrity. The network employs a sophisticated slot and epoch system, with transaction finality typically occurring after two epochs, which translates to approximately 12.8 minutes, utilizing the Casper-FFG protocol. The Beacon Chain is central to coordinating validators, and the LMD-GHOST fork-choice rule ensures the chain adheres to the path with the most accumulated validator votes. Validators are incentivized with rewards for their participation in proposing and verifying blocks, but face stringent penalties, known as slashing, for any malicious actions or prolonged inactivity. This design choice by Ethereum aims to significantly enhance energy efficiency, security, and scalability, with ongoing and future upgrades, such as Proto-Danksharding, further targeting improvements in transaction processing efficiency, thereby benefiting Base as its foundational security layer. Base specifically leverages Optimistic Rollups as part of the OP Stack, meaning transactions are presumed valid unless challenged within a specified period via fault proofs.

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.

Fantom's operational foundation is built upon the Lachesis Protocol, an Asynchronous Byzantine Fault Tolerant (aBFT) consensus mechanism specifically engineered to deliver rapid, secure, and highly scalable transaction processing. This innovative protocol diverges from conventional linear blockchain structures by employing a Directed Acyclic Graph (DAG) architecture, which facilitates the parallel processing of multiple transactions across various nodes. This parallel execution significantly boosts network throughput, making Fantom exceptionally well-suited for decentralized applications (dApps) that demand swift and efficient transaction handling. A cornerstone of the Lachesis Protocol is its asynchronous and leaderless design. This means that individual nodes can achieve consensus independently, without needing to defer to a central leader. Such a design inherently enhances the network's decentralization and overall speed, minimizing potential bottlenecks. Transactions on Fantom are organized into "event blocks," which undergo validation asynchronously by a multitude of validators. Once a sufficient number of validators confirm an event block, it is integrated into the network's immutable history. A critical feature distinguishing Fantom is its instant finality, meaning that once transactions are confirmed, they are irreversible and cannot be altered. This property is particularly valuable for applications where immediate and unchangeable transaction settlement is paramount, offering a high degree of confidence and reliability to users and developers alike.

Harmony One's blockchain network employs an innovative consensus mechanism known as Effective Proof of Stake (EPoS), meticulously engineered to achieve a delicate balance between validator influence, network security, and transaction scalability. At its core, EPoS promotes a diverse and decentralized validator set. Unlike systems that might allow large stakeholders to dominate, EPoS actively limits the disproportionate influence of high-stake validators, thereby encouraging broader participation and safeguarding against the centralization of power. This design principle extends to its sharding architecture, where multiple validators engage in competition within each distinct shard, further distributing staking power across the network and significantly bolstering overall security. The network's architectural strength is further amplified by its sharding capabilities, combined with Practical Byzantine Fault Tolerance (PBFT) finality. Harmony One features four independent shards, each capable of processing transactions and executing smart contracts concurrently. This parallel processing capability is crucial for achieving high levels of scalability and throughput, allowing the network to handle a substantial volume of operations simultaneously. Within each of these shards, a modified PBFT model is utilized, which is instrumental in delivering immediate transaction finality. This means that once blocks are validated within a shard, their finality is assured almost instantly, contributing to remarkably high transaction speeds. The integration of EPoS with sharding and PBFT finality ensures that Harmony One can sustain a decentralized, secure, and highly performant environment for its users and applications, making it suitable for high-frequency decentralized applications that require both speed and robust security guarantees.

The Huobi Eco Chain (HECO) blockchain operates using a sophisticated Hybrid-Proof-of-Stake (HPoS) consensus mechanism, a design choice intended to significantly boost transaction efficiency and overall network scalability. This hybrid approach intelligently integrates core principles from traditional Proof-of-Stake (PoS) systems, allowing for a more streamlined and less resource-intensive operation compared to older blockchain models. At the heart of HECO's consensus is a carefully managed validator selection process. The network supports a maximum of 21 validators, a number chosen to balance decentralization with performance. These validators are selected based on the quantity of their staked assets within the network, meaning those with a greater economic commitment have a higher chance of being chosen to participate in the consensus. Once selected, these validators assume the crucial responsibility of processing transactions and securely appending new blocks to the blockchain. This process ensures the continuous and accurate recording of all network activities. A significant advantage of HECO's HPoS mechanism is its ability to achieve quick transaction finality. This rapid confirmation means that transactions are processed and confirmed almost immediately, enhancing the user experience for applications requiring fast and reliable settlements. Furthermore, a key benefit of adopting PoS elements within the HPoS framework is a substantial reduction in energy consumption. By moving away from energy-intensive Proof-of-Work (PoW) systems, HECO contributes to a more sustainable blockchain ecosystem. This focus on energy efficiency, combined with its robust transaction processing capabilities and quick finality, positions the Huobi Eco Chain as a network designed for both high performance and environmental responsibility, facilitating a broad range of decentralized applications and services.

The NEAR Protocol blockchain network operates on a distinctive consensus mechanism that synergistically combines the principles of Proof of Stake (PoS) with a proprietary innovation known as Doomslug, further enhanced by dynamic sharding through Nightshade. This multi-faceted approach is engineered to deliver high efficiency, rapid transaction finality, and robust security across the network. At its foundation, the system relies on Proof of Stake, where participants, termed validators, secure the network by staking their native NEAR tokens. The greater the stake, coupled with community trust, the higher their probability of being chosen to propose and validate blocks.Doomslug significantly accelerates transaction finality. Unlike single-stage block confirmations, Doomslug introduces a two-stage process. Initially, validators propose new blocks. Finality is achieved swiftly when two-thirds of the participating validators formally approve the proposed block, making confirmed transactions irreversible and preventing potential forks. This rapid finality is crucial for applications demanding near-instant confirmations. Complementing this, NEAR integrates Nightshade, a dynamic sharding technique. Nightshade segments the network into multiple shards, allowing for the parallel processing of transactions. Each shard handles a distinct subset of transactions concurrently, and their respective processing outcomes are then consolidated into a single "snapshot" block. This dynamic sharding is vital for scalability, enabling the network to efficiently manage increasing transaction volumes and user demand without compromising performance.The consensus process also emphasizes decentralization and fairness through epoch rotation. Validators are regularly reshuffled across distinct intervals called epochs. This rotation mechanism ensures a balanced distribution of block proposal opportunities and validation responsibilities among eligible validators, mitigating centralization risks and promoting sustained network resilience. By integrating PoS for economic security, Doomslug for fast finality, and Nightshade for scalable throughput, the NEAR Protocol establishes a high-performance and secure blockchain environment.

Optimism operates as a Layer 2 scaling solution for the Ethereum network, designed to boost transaction throughput and minimize costs by utilizing Optimistic Rollups while inheriting the robust security features of the underlying Ethereum main chain. The system is built upon several core components. At its heart are Optimistic Rollups, where transactions are batched into "rollup blocks" and processed off-chain. The resulting state commitments, which represent the collective outcome of these off-chain operations, are then periodically committed to the Ethereum main chain.

Key to Optimism's functionality are the "Sequencers." These entities are tasked with collecting and ordering transactions into batches. Following processing, sequencers update the Layer 2 state and transmit these updates to Ethereum. Specifically, they construct and execute Layer 2 blocks, which are subsequently posted as calldata on the Ethereum mainnet. This involves publishing a cryptographic hash of the state root and the associated transaction data. This aggregation method efficiently combines numerous Layer 2 transactions into a single Layer 1 transaction, significantly reducing the average cost per transaction.

A defining characteristic of Optimistic Rollups is its "Fraud Proof" mechanism. Transactions are initially presumed valid, facilitating rapid finality. However, a critical "challenge period" allows any network participant to submit a fraud proof if they detect an invalid transaction. If a challenge is initiated, an "interactive verification game" unfolds, meticulously breaking down the disputed transaction into granular steps to pinpoint any fraudulent activity. Should fraud be conclusively proven, the invalid state is reverted, and the dishonest sequencer or actor is penalized, typically by forfeiting their staked collateral. A batch achieves finality and its state updates become permanent only after the challenge period expires without any successful fraud proofs. This design ensures that Optimism leverages Ethereum's underlying Proof-of-Stake consensus, thereby securing all Layer 2 transactions once they are enshrined on the Layer 1 network.

The Polygon blockchain network, originally known as Matic Network, operates as a Layer 2 scaling solution for Ethereum, leveraging a sophisticated hybrid consensus mechanism to enhance scalability, ensure security, and maintain decentralization. The foundational elements of its consensus protocol are built upon a combination of Proof of Stake (PoS) and Plasma Chains. Within the PoS framework, validators are selected based on the number of MATIC tokens they have staked, with a larger stake increasing their probability of being chosen to validate transactions and produce new blocks. This system also allows MATIC token holders who prefer not to run their own validator nodes to delegate their tokens to trusted validators, thereby earning a share of the rewards and actively contributing to the network's overall security and decentralization.

Supplementing PoS, Polygon utilizes Plasma Chains, which serve as a framework for establishing child chains that run in parallel with the main Ethereum chain. These child chains facilitate off-chain transaction processing, significantly improving transaction throughput and reducing congestion on the Ethereum mainnet by committing only the final, aggregated state back to Ethereum. To uphold the integrity and security of these off-chain transactions, Plasma Chains incorporate a robust fraud-proof mechanism, enabling the challenging and potential reversion of any detected fraudulent activity.

The consensus process on Polygon begins with validators confirming the validity of transactions and subsequently integrating them into blocks. Validators then propose new blocks, with their staked tokens influencing their voting power, and engage in a collective voting process to reach consensus. A new block is officially added to the blockchain upon receiving a majority of votes. A critical security measure is the periodic checkpointing system, where snapshots of the Polygon sidechain's state are regularly submitted to the Ethereum main chain, thereby leveraging Ethereum's inherent security for the finality of Polygon's transactions. The Plasma framework further enables off-chain validation of transactions on child chains, with their final states eventually submitted to the Ethereum main chain, and fraud proofs ready to challenge any suspicious transactions within a specified period, collectively reinforcing Polygon's operational integrity and security.

The Gnosis Chain blockchain network employs an innovative dual-layer architecture explicitly designed to achieve a balanced blend of scalability and robust security, fundamentally leveraging a Proof of Stake (PoS) consensus mechanism for its core operations and transaction finality. At its foundational level, Layer 1 is embodied by the Gnosis Beacon Chain, which serves as the primary security and consensus backbone for the entire network. Validators on this layer are required to stake GNO tokens, a crucial prerequisite that enables their active participation in validating transactions and securing the network. This staking mechanism is engineered to ensure that validators maintain a significant economic interest in upholding the integrity and stability of the blockchain.

Complementing this, Layer 2, known as the Gnosis xDai Chain, is purpose-built to facilitate high-speed, low-cost transactions and interactions with decentralized applications (dApps). The transactional data originating from these Layer 2 operations is subsequently finalized on the more secure Gnosis Beacon Chain, establishing a seamless and integrated operational framework. This architectural separation ensures that Layer 1 delivers foundational security and ultimate finality, while Layer 2 substantially boosts the network's overall scalability and transaction processing efficiency. By effectively combining the rapid processing and cost-effectiveness intrinsic to Layer 2 solutions with the steadfast security assurances provided by a PoS-secured Layer 1, Gnosis Chain positions itself as a highly versatile platform. This makes it exceptionally well-suited for a broad spectrum of applications, ranging from those demanding very high transaction frequencies to those necessitating secure asset management. The economic commitment demonstrated by validators through their staked GNO tokens directly underpins the security for both the Beacon Chain (Layer 1) and the xDai Chain (Layer 2), fostering a cohesive and dependable ecosystem.

yearn.finance is present on the following networks: Arbitrum, Avalanche, Base, Ethereum, Fantom, Harmony One, Huobi, Near Protocol, Optimism, Polygon, Gnosis Chain.

Arbitrum One, serving as a Layer 2 scaling solution for Ethereum, incorporates a sophisticated array of incentive mechanisms to guarantee the ongoing security and integrity of its network. Central to this framework are the Validators and Sequencers. Sequencers are entrusted with the vital task of ordering user transactions and compiling them into batches for efficient off-chain processing, playing a critical role in optimizing network throughput and speed. Validators, conversely, actively monitor the Sequencers' activities, meticulously verifying state transitions and ensuring that only valid transactions are included in the batches. Both Sequencers and Validators are motivated through economic rewards, primarily derived from collected transaction fees and potentially other protocol-specific incentives, contingent on their honest and efficient performance.

Arbitrum’s security model is heavily reliant on its Fraud Proofs system. Transactions processed off-chain are initially given an "assumption of validity," which enables swift transaction finality and higher throughput. However, a predefined "challenge period" is established, during which any network participant can submit a fraud proof to contest the validity of a transaction. This acts as a powerful deterrent against malicious behavior. If a challenge is successfully brought forward, an interactive verification process is initiated to precisely identify and confirm any fraudulent activity. In instances where fraud is proven, the invalid transaction is reversed, and the dishonest actor faces economic penalties, which may include the slashing of staked tokens or other forms of financial disincentive. This balanced system of rewards for honest participation and strict penalties for malicious actions aligns participants' interests with the overall health and security of the Arbitrum network.

The Applicable Fees on the Arbitrum One blockchain are structured to be cost-effective. Users pay Layer 2 Fees for transactions executed on the Arbitrum network, which are typically significantly lower than those on the Ethereum mainnet due to reduced computational load. A specific "Arbitrum Transaction Fee" is applied to each transaction processed by the sequencer, covering the costs of processing and batch inclusion. Additionally, L1 Data Fees are incurred when batches of Layer 2 state updates are periodically posted as calldata to the Ethereum mainnet. This fee covers the requisite gas costs on Ethereum. A key economic benefit is "cost sharing," where the fixed expenses of submitting these state updates to Ethereum are distributed across multiple transactions within a batch, substantially lowering the per-transaction cost for users. For example, protocols leveraging the Arbitrum stack, such as Kinto, utilize ETH for transaction fee payments.

The Avalanche blockchain network employs a comprehensive system of incentive mechanisms and fees designed to ensure its security, integrity, and efficiency, primarily through its Avalanche Consensus mechanism. Validators, who are critical to the network's operation, are required to stake a certain amount of AVAX tokens. The quantity of staked tokens directly influences their likelihood of being chosen to propose or validate new blocks. In return for their active participation, validators receive rewards, which are calculated proportionally to the amount of AVAX they have staked, as well as their consistent uptime and overall performance in validating transactions. To further decentralize participation, validators can also accept delegations from other token holders. These delegators subsequently share in the earned rewards, thus incentivizing smaller token holders to contribute indirectly to the network's security. The economic incentives for validators extend beyond staking rewards to include block rewards, which are distributed from the inflationary issuance of new AVAX tokens for proposing and validating blocks. Additionally, validators earn a portion of the transaction fees paid by users across the network, covering simple transactions, complex smart contract interactions, and the creation of new assets. Crucially, Avalanche's penalty system differs from some other Proof-of-Stake systems by not employing 'slashing,' which involves the confiscation of staked tokens for misbehavior. Instead, the network relies on the economic disincentive of lost future rewards. Validators who fail to maintain consistent uptime or engage in malicious activities will simply miss out on potential earnings, providing a strong incentive for honest and reliable behavior. The network also imposes clear uptime requirements, where poor performance directly impacts a validator's ability to earn rewards. Fees on the Avalanche blockchain are structured to be dynamic, adjusting based on current network demand and the computational complexity of transactions. This ensures that fees remain equitable and reflect the actual network usage. A significant portion of these transaction fees is 'burned,' meaning they are permanently removed from circulation. This deflationary mechanism helps to offset the inflationary effects of block rewards and aims to enhance the long-term value of AVAX tokens. Fees for deploying and interacting with smart contracts are determined by the required computational resources, promoting efficient resource utilization. Similarly, fees are imposed for creating new assets on the network, a measure designed to deter spam and ensure that network resources are utilized by serious projects. On the Avalanche X-Chain, validator incentives are realized indirectly through the network's overall AVAX issuance, while its transaction fees are fixed and burned to combat spam and progressively reduce the total supply of AVAX.

The Base blockchain, as an Ethereum Layer-2 solution utilizing Optimistic Rollups from the OP Stack, implements incentive mechanisms primarily focused on optimizing transaction costs and ensuring secure asset transfers, leveraging the economic security of its underlying Ethereum L1. A core incentive to use Base is its efficiency in reducing transaction expenses. This is achieved by a sequencer that bundles numerous L2 transactions together, submitting them as a single, consolidated L1 transaction to Ethereum. This process significantly lowers the average transaction cost for individual L2 operations, as the collective L2 transactions share the cost of the single L1 transaction fee, thereby making Base a more economically attractive option compared to direct L1 usage.

For the secure movement of crypto-assets between Base and Ethereum, a specialized smart contract on the Ethereum network is employed. Since Base, as an L2, does not manage its own consensus for fund withdrawals, an additional mechanism is in place to guarantee that only legitimate funds can be moved off the L2. When a user initiates a withdrawal request on Ethereum's L1, a predetermined challenge period begins. During this window, any other network participant has the opportunity to submit a "fault proof" if they detect a fraudulent withdrawal attempt, triggering a dispute resolution process. This entire system is strategically designed with economic incentives to encourage honest behavior and deter malicious activities, although specific details of these economic incentives for fault proof submission are not explicitly outlined beyond the general principle.

Furthermore, Base inherits and benefits from the robust incentive structure of Ethereum’s Proof-of-Stake (PoS) system, which indirectly secures Base transactions. Ethereum validators, by staking a minimum of 32 ETH, are rewarded for proposing and attesting to valid blocks, as well as for participating in sync committees. These rewards are distributed through newly issued ETH and a portion of transaction fees. Under the EIP-1559 fee model, transaction fees comprise a base fee, which is algorithmically burned to manage supply, and an optional priority fee (or 'tip') paid directly to validators. To maintain network integrity, validators face economic penalties, known as slashing, if they engage in malicious conduct or fail to perform their duties. This comprehensive incentive framework ensures strong security alignment for Base by reinforcing reliable validator behavior on its underlying L1.

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.

Fantom's economic framework incorporates a robust incentive model crafted to bolster network security and foster broad participation among its users and validators. A primary mechanism involves staking rewards for validators, who are crucial to the consensus process. These validators earn rewards in FTM tokens, directly proportional to the amount they have staked, thereby creating a strong incentive to actively secure and maintain the network. To ensure a balanced reward structure and support long-term network security, Fantom employs a dynamic staking reward rate, which adjusts based on the total FTM staked across the network. Consequently, if the total staked amount increases, individual rewards may see a proportional decrease. Beyond active validators, the network also facilitates participation for token holders who do not wish to operate their own validator nodes through delegated staking. These users can delegate their FTM tokens to existing validators, and in return, they receive a share of the staking rewards. This delegation option is vital for encouraging wider community involvement in the network's security without requiring significant technical overhead. Regarding applicable fees, transactions on the Fantom network are subject to fees paid in FTM tokens. Thanks to the network's high throughput capabilities, largely attributed to its DAG structure, these transaction fees are kept remarkably low. This efficient fee model, combined with the network's inherent scalability, renders Fantom an exceptionally cost-effective platform for users and an attractive environment for developing and deploying high-volume decentralized applications.

The Harmony One blockchain network implements a comprehensive suite of incentive mechanisms and a transparent fee structure designed to foster active participation, maintain network security, and ensure efficient operations. Validators and delegators are primarily incentivized through staking rewards, which are disbursed in ONE tokens. Validators earn these tokens for their critical role in validating transactions and securing the network infrastructure. A significant portion of these earned rewards is then shared with delegators, who contribute to network security by staking their ONE tokens with chosen validators. This tiered reward system encourages a broad base of participation, allowing both active node operators and passive token holders to contribute to the network's robustness. A distinctive feature of Harmony One's incentive model is its decentralization penalty for high-stake validators. This mechanism is specifically designed to adjust and reduce rewards for validators that accumulate an excessive amount of delegated stake. By doing so, the network actively discourages the centralization of staking power, promoting a more equitable distribution among validators and reinforcing the network's decentralized ethos. This helps to prevent a scenario where a few dominant entities could exert undue influence over the network. In terms of applicable fees, Harmony One is structured to provide a cost-effective environment for its users. The network charges minimal transaction fees, which are denominated in ONE tokens. These low fees are particularly advantageous for high-frequency applications, enabling numerous interactions without incurring prohibitive costs. Furthermore, these transaction fees serve a dual purpose: they make the network accessible and efficient for users, while simultaneously providing an additional revenue stream for validators, further aligning their economic interests with the ongoing health and performance of the Harmony One network.

The Huobi Eco Chain (HECO) blockchain implements a robust incentive framework and fee structure, intrinsically linked to its Hybrid-Proof-of-Stake (HPoS) consensus mechanism, to ensure network security, encourage participation, and maintain operational efficiency. This system is designed to reward active participants for their contributions while establishing clear costs for network usage. A primary incentive mechanism centers on Validator Rewards. Validators, who are critical to the network's integrity, are selected based on their staked assets and are responsible for processing transactions and adding new blocks. In return for these essential services, they receive rewards predominantly in the form of transaction fees. This direct financial compensation incentivizes validators to perform their duties diligently, ensuring the blockchain remains secure, up-to-date, and consistently operational. Beyond active validation, the network promotes broader Staking Participation. Users who wish to contribute to network security but may not operate a full validator node can stake Huobi Token (HT) either to become a validator themselves or by delegating their tokens to existing, trusted validators. This delegation model is crucial as it allows a wider range of participants to secure the network. In return for their staking efforts, these participants receive a portion of the transaction fees, creating an inclusive economic model that rewards collective security. Regarding Applicable Fees, the HECO network utilizes a straightforward and transparent fee structure. Users are required to pay Transaction Fees, also known as Gas Fees, in HT tokens for executing transactions and interacting with smart contracts. These fees serve a dual purpose: they compensate validators for the computational resources and effort expended in processing and validating transactions, and they help prevent network spam by assigning a cost to each operation. Additionally, deploying and interacting with smart contracts on the HECO network incurs Smart Contract Execution Fees. These are supplementary fees, also paid in HT tokens, specifically designed to cover the computational resources demanded by complex contract code execution. This comprehensive incentive and fee system ensures that all network participants, from active validators to staking users, are economically aligned with the health and performance of the Huobi Eco Chain, fostering a self-sustaining and secure decentralized environment.

The NEAR Protocol blockchain network employs a comprehensive suite of economic mechanisms designed to ensure network security, incentivize active participation from its community, and manage resource allocation efficiently. A core incentive is the staking reward system, where validators and delegators are compensated for their role in securing transactions. Validators, selected based on their staked NEAR tokens and community trust, receive a share of newly minted tokens, constituting about 90% of the approximate 5% annual inflation. They earn these rewards for proposing and validating blocks. Similarly, token holders who choose not to operate a full validator node can delegate their NEAR tokens to active validators, thereby contributing to network security and earning rewards proportional to their delegated stake. This delegation model fosters broader participation and strengthens the network's overall decentralization.To uphold network integrity, NEAR Protocol implements a robust slashing mechanism. Validators engaging in malicious activities, such as incorrect validation or dishonest behavior, face economic penalties, including the deduction of a portion of their staked tokens. This serves as a powerful deterrent against harmful actions, ensuring validators operate in the network's best interest. Additionally, the network promotes fairness and prevents undue concentration of power through regular epoch rotations. During these predefined intervals, validators are periodically reshuffled, and new block proposers are selected, maintaining a healthy balance between network performance and decentralization.Regarding applicable fees, the NEAR blockchain charges users for transaction processing and data storage, paid in NEAR tokens. A unique aspect of its fee structure is the burning mechanism for transaction fees, which reduces the total circulating supply of NEAR tokens over time, potentially introducing a deflationary effect. While a portion of these fees is burned, the remaining part is distributed to validators as additional compensation, providing a continuous incentive for network maintenance. Furthermore, the protocol imposes storage fees based on the amount of blockchain space consumed by user accounts, smart contracts, and associated data. Users are required to hold NEAR tokens as a deposit commensurate with their storage usage, which encourages efficient resource management and helps prevent network spam. This dual system of incentives and fees creates a self-sustaining economic model for the NEAR Protocol.

Optimism, functioning as an Ethereum Layer 2 scaling solution, employs Optimistic Rollups to implement a sophisticated array of incentive mechanisms and fee structures. These are meticulously designed to guarantee network security, operational efficiency, and cost-effectiveness, with a primary objective to significantly increase transaction throughput and lower costs compared to the Ethereum mainnet, all while preserving decentralization and robust security.

Sequencers are central to this model, responsible for collecting, ordering, and batching transactions off-chain, thereby optimizing the processing flow. Their economic incentive stems directly from the transaction fees they accrue from users, which drives them to process transactions swiftly and accurately. This expedited processing is crucial for the network’s overall speed and responsiveness.

A pivotal incentive mechanism is embedded within the validator and "Fraud Proofs" system. Transactions on Optimism are optimistically assumed to be valid, which inherently allows for quicker confirmation times. To prevent and address potential malicious activities, a "challenge mechanism" is in place. During a predefined challenge window, any network participant, including designated validators, can submit a fraud proof if an invalid transaction is detected. Successful challengers are rewarded for their diligence in identifying and substantiating fraudulent transactions. This reward system economically encourages active and continuous network monitoring, thus bolstering the overall security posture of the rollup. Conversely, "Economic Penalties" serve as a powerful deterrent. If a sequencer includes an invalid transaction that is subsequently and successfully challenged, they face financial repercussions, such as the loss of a portion of their staked collateral. Similarly, any form of inactivity or misbehavior by sequencers or validators can lead to penalties and the forfeiture of potential rewards, aligning participant actions with the network's best interests.

Optimism’s fee structure encompasses several categories. "Layer 2 Transaction Fees," paid by users for transactions processed on the Layer 2 network, are notably lower than those on the Ethereum mainnet due to the reduced computational load. The bundling of multiple transactions into a single batch significantly enhances this cost efficiency. Additionally, "L1 Data Fees" are incurred when state updates from Layer 2 transactions are periodically posted to the Ethereum mainnet as calldata. This fee covers the underlying gas costs on Ethereum, but these expenses are distributed across numerous transactions within a batch, further reducing individual transaction burdens. Lastly, "Smart Contract Fees" apply to the deployment and interaction with smart contracts on Optimism, calculated based on the computational resources consumed, ensuring charges are proportional to resource usage.

The Polygon network employs a robust set of incentive mechanisms and a distinct fee structure, combining its Proof of Stake (PoS) consensus with the Plasma framework to ensure network security, encourage active participation, and maintain transaction integrity. Validators play a crucial role, securing the network by staking MATIC tokens. Their selection for validating transactions and producing new blocks is directly influenced by the quantity of tokens they have staked. In exchange for their services, validators receive rewards in the form of newly minted MATIC tokens and a portion of the transaction fees. They are responsible for proposing and voting on new blocks, with incentives structured to promote honest and efficient operation, while also deterring misconduct through potential penalties. A key security feature involves validators periodically submitting checkpoints of the Polygon sidechain to the Ethereum main chain, which leverages Ethereum's established robustness to guarantee the finality of Polygon's transactions.

Delegators, who are token holders opting not to operate their own validator nodes, can delegate their MATIC tokens to trusted validators. This delegation allows them to earn a share of the rewards distributed to their chosen validators, fostering broader community participation in securing the network and enhancing its decentralization. The economic security of Polygon is further reinforced by a slashing mechanism, which penalizes validators for malicious actions, such as double-signing transactions or extended periods of inactivity. Slashing entails the forfeiture of a portion of their staked tokens, serving as a powerful deterrent against dishonest behavior. Additionally, validators are required to bond a substantial amount of MATIC, ensuring they have a significant financial interest in upholding the network's integrity.

Regarding the fee structure, one of Polygon's significant advantages is its remarkably low transaction fees compared to the Ethereum main chain. These fees, paid in MATIC tokens, are designed to be affordable, thereby encouraging high transaction throughput and widespread user adoption. While fees on Polygon can exhibit dynamic variations based on network congestion and transaction complexity, they consistently remain considerably lower than those on Ethereum, making Polygon an attractive option for users and developers. Deploying and interacting with smart contracts on Polygon also incurs fees, which are determined by the computational resources required. These smart contract fees are also paid in MATIC and are substantially lower than on Ethereum, offering a cost-effective environment for developing and maintaining decentralized applications (dApps). Furthermore, the Plasma framework facilitates off-chain processing for state transfers and withdrawals, with associated fees also paid in MATIC, collectively contributing to a reduced overall cost of utilizing the network.

The Gnosis Chain implements a sophisticated incentive and fee framework that actively encourages broad validator participation and ensures the network's accessibility for all users, primarily facilitated by its distinct dual-token system. This system is pivotal in concurrently achieving remarkably low transaction costs and highly effective staking rewards. The incentive mechanisms are meticulously structured to promote deep involvement in network security. Validators are generously rewarded with GNO tokens in exchange for their critical contributions to the consensus process and their role in fortifying the network's overall security. Beyond direct validation, the network integrates a flexible delegation model, which allows GNO token holders who prefer not to operate their own validator nodes to delegate their tokens to established validators. This delegation mechanism permits them to share in the staking rewards, thereby significantly widening participation in network security and decentralization.

The dual-token strategy clearly delineates functional roles: GNO tokens are principally allocated for staking, governance, and the distribution of validator rewards, thereby aligning the long-term security imperatives of the network with the economic interests of its token holders. Conversely, xDai functions as the stable primary currency for all network transactions, providing stable and predictable costs that are insulated from the inherent volatility often seen in other cryptocurrencies. This financial stability is particularly advantageous for both users and developers, guaranteeing consistent pricing for network operations. Regarding applicable fees, all transaction fees on the Gnosis Chain are paid in xDai, the stable fee token. This deliberate choice ensures that transaction costs remain affordable and highly predictable, a key benefit for high-frequency applications and decentralized applications (dApps) that demand consistent and minimal transaction overheads. Crucially, these xDai transaction fees are not merely burned; they are judiciously redistributed to validators as a core component of their compensation. This direct correlation between network activity and validator rewards further aligns their interests with the healthy operation and expansion of the Gnosis Chain. Moreover, through the delegated staking system, GNO holders also earn a portion of these staking rewards, robustly fostering user engagement in maintaining the network's security without requiring their direct involvement in complex consensus operations.

yearn.finance is present on the following networks: Arbitrum, Avalanche, Base, Ethereum, Fantom, Huobi, Near Protocol, Optimism, Polygon, Gnosis Chain.

The methodology employed for calculating the energy consumption attributed to the Arbitrum network adopts a "bottom-up" approach, systematically assessing individual operational components to arrive at an aggregate consumption figure. Within this framework, network nodes are identified as the central and most significant contributors to the network's overall energy footprint. The foundational assumptions underpinning these calculations are derived from empirical findings, which are compiled through the extensive use of publicly available information sites, proprietary in-house crawlers developed by the assessors, and various open-source data collection tools.

A crucial step in estimating energy consumption involves accurately determining the specific hardware devices utilized within the network. This determination is made by evaluating the technical requirements necessary for operating the client software pertinent to the Arbitrum network. Once these hardware profiles are established, their corresponding energy consumption rates are precisely measured under controlled conditions in certified test laboratories, ensuring a high degree of accuracy and reliability for the baseline data. To ensure a comprehensive and accurate scope, particularly when accounting for diverse implementations of crypto-assets across different networks, the Functionally Fungible Group Digital Token Identifier (FFG DTI) is employed whenever such an identifier is available. This tool assists in clearly delineating all relevant instances of an asset, with these mappings consistently updated based on data provided by the Digital Token Identifier Foundation.

Furthermore, the methodology relies on specific assumptions regarding the type of hardware deployed and the estimated number of active participants within the network. These assumptions are subjected to continuous validation using best-effort empirical data. A general guiding principle in these estimations is the presumption that network participants act in a largely economically rational manner. In accordance with a precautionary principle, conservative estimates are applied whenever there is uncertainty, typically resulting in higher assessments of potential adverse environmental impacts. When quantifying the energy consumption for a particular crypto-asset operating on Arbitrum, a proportionate fraction of the overall network's energy consumption is allocated to that asset, based on its observed activity within the Arbitrum ecosystem. The source documents do not provide any direct external links related to this methodology.

The methodology for assessing the Avalanche network's energy consumption is founded on a 'bottom-up' approach, where individual nodes are identified as the primary contributors to the network's overall energy footprint. This comprehensive calculation aggregates energy usage across various interconnected components of the network. The assumptions underpinning these calculations are derived from extensive empirical findings, utilizing a combination of publicly available information sites, sophisticated open-source crawlers, and proprietary in-house developed crawlers. A key aspect of this methodology involves estimating the hardware deployed within the network. This estimation is primarily driven by the technical specifications and operational requirements for running the client software, which dictates the type and performance of necessary hardware devices. The energy consumption profiles of these identified hardware devices are meticulously measured in certified test laboratories to ensure accuracy. To ensure a broad and precise scope, the Functionally Fungible Group Digital Token Identifier (FFG DTI) is leveraged, whenever available, to pinpoint all relevant implementations of the crypto-asset under consideration. These mappings are regularly updated based on current data provided by the Digital Token Identifier Foundation. The data regarding specific hardware usage and the total number of network participants is based on empirically verified assumptions, consistently updated with best-effort validation. A foundational assumption in this model is that network participants generally behave in an economically rational manner. Furthermore, adhering to a precautionary principle, any uncertainties or doubts during the estimation process lead to conservative assumptions, specifically by making higher estimates for potential adverse environmental impacts. When determining the energy consumption attributable to a specific token within the Avalanche ecosystem, the energy consumption of the entire Avalanche network (including subnets like Avalanche X-Chain) is calculated first. Subsequently, a fraction of this total network energy is allocated to the token, proportional to its activity and footprint within the network. This detailed, multi-layered approach aims to provide a robust and conservative estimate of the energy consumption associated with the Avalanche blockchain.

The energy consumption calculation for the Base blockchain network is meticulously performed using a "bottom-up" approach, where individual nodes are identified as the primary contributors to the network's overall energy footprint. This methodology is based on empirical data collected from a variety of sources, including publicly available information sites, dedicated open-source crawlers, and proprietary in-house crawling tools. The fundamental aspect of estimating hardware usage within the network involves determining the minimum requirements necessary to operate the client software. The energy consumption profiles of the specific hardware devices identified are obtained from measurements conducted in certified test laboratories, ensuring a high degree of accuracy in these foundational figures.

In the process of calculating network energy consumption, the Functionally Fungible Group Digital Token Identifier (FFG DTI) is utilized when available, serving to identify and encompass all relevant implementations of a crypto-asset within the scope of analysis. These mappings are regularly updated, drawing on data provided by the Digital Token Identifier Foundation. However, the source documents do not provide specific URLs for the public information sites, open-source crawlers, or the Digital Token Identifier Foundation, preventing direct external linking within this summary.

The methodology also incorporates assumptions regarding the hardware deployed and the number of participants operating within the network. These assumptions are rigorously verified with "best effort" against empirical data to ensure their realism and accuracy. A key underlying principle is the assumption that network participants generally act in a "largely economically rational" manner. Furthermore, to adhere to a precautionary principle, conservative estimates are applied in situations of uncertainty, leading to higher projected impacts to mitigate underestimation risks. For a specific token on Base, a fraction of the network’s total energy consumption is attributed, based on the token's 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 methodology employed for calculating the Fantom network's energy consumption utilizes a "bottom-up" approach, which identifies individual nodes as the primary contributors to the network's overall energy footprint. This calculation is grounded in a combination of empirical findings derived from various public information sources, proprietary in-house crawlers, and publicly available open-source crawlers. The core factor in estimating the hardware deployed across the network is the technical specifications and operational demands required to run the client software. To ensure accuracy, the energy consumption associated with these specific hardware devices is meticulously measured in certified test laboratories. When determining the full scope of crypto-asset implementations for energy calculation purposes, the Functionally Fungible Group Digital Token Identifier (FFG DTI) is utilized whenever available. This allows for comprehensive mapping of the asset in question, with these mappings being updated regularly based on data provided by the Digital Token Identifier Foundation. The underlying data regarding the types of hardware in use and the total number of participants in the network relies on carefully constructed assumptions. These assumptions are rigorously verified through a best-effort approach, cross-referencing against available empirical data. A general principle guiding these estimations is the assumption that participants are largely economically rational actors. Furthermore, in instances of uncertainty, a precautionary principle is applied, leading to conservative estimates that lean towards higher assessments of potential adverse environmental impacts. The detailed approach aims to provide a comprehensive, albeit assumption-based, quantification of energy usage. No specific external document links are available within the provided context for this section.

The methodology for calculating the energy consumption of assets operating on the Huobi network, alongside others, employs a "bottom-up" approach. This comprehensive strategy identifies individual energy-consuming components within the network to aggregate an overall consumption figure. At its core, the methodology considers the nodes as the central drivers of the network's energy footprint. These assumptions are grounded in extensive empirical findings, gathered through the continuous use of public information sites, sophisticated open-source crawlers, and proprietary crawlers developed in-house. A critical step in this process involves accurately estimating the hardware utilized across the network. This estimation is primarily determined by the specific hardware requirements necessary for operating the client software that facilitates participation in the Huobi network. To ensure accuracy, the energy consumption of various hardware devices is meticulously measured in certified test laboratories, providing verifiable data for the calculations. When attributing energy consumption to specific crypto-assets, such as those present on Huobi, a fraction of the total network energy is allocated. This attribution is directly proportional to the activity of the particular crypto-asset within the network, ensuring that energy impact is tied to actual usage. The Functionally Fungible Group Digital Token Identifier (FFG DTI) is leveraged, where available, to precisely identify all implementations of an asset in scope, with mappings regularly updated using data from the Digital Token Identifier Foundation. The information regarding hardware usage and the number of network participants relies on assumptions that are rigorously verified using empirical data. Generally, participants are presumed to act rationally in economic terms. Furthermore, a precautionary principle is applied, favoring conservative estimates (i.e., higher estimations for adverse impacts) when any doubt exists, thus aiming for a robust and safe assessment of energy consumption.

The methodology for assessing the energy consumption of the NEAR Protocol network relies on a "bottom-up" approach, meticulously aggregating data across its various operational components. This method primarily considers the network's nodes as the central contributors to overall energy usage. The fundamental assumptions underpinning these calculations are derived from empirical findings obtained through a combination of public information sources, proprietary in-house crawlers, and publicly available open-source crawlers. These tools are instrumental in gathering essential data about the network's operational footprint.A critical determinant in estimating the hardware deployed within the network is the specific computational requirements necessary to run the client software. Based on these identified requirements, the energy consumption of the corresponding hardware devices is rigorously measured in certified test laboratories. This ensures accuracy and consistency in energy attribution. Furthermore, when calculating energy consumption, the Functionally Fungible Group Digital Token Identifier (FFG DTI) is utilized, where available, to precisely identify and encompass all relevant implementations of any crypto-asset within the network's scope. These mappings are regularly updated, leveraging data from the Digital Token Identifier Foundation, to maintain current and accurate representations.The information pertaining to the hardware used and the total number of participants in the network is built upon assumptions, which are diligently verified using the best available empirical data. A general underlying premise is that network participants are largely economically rational actors. In adherence to a precautionary principle, conservative estimations are favored when uncertainties arise, leading to higher projected adverse impacts to ensure a robust and responsible assessment of energy consumption. This comprehensive methodology allows for a detailed and conservative estimation of the NEAR Protocol network's energy footprint.

The energy consumption profile of the Optimism blockchain network, being a Layer 2 scaling solution for Ethereum, is not isolated but rather intricately integrated with and aggregated within the broader Ethereum ecosystem. Its energy usage also includes the demands of its own specialized operational components. The general approach for calculating the energy consumption of such networks, including Optimism, typically involves a "bottom-up" methodology. This method primarily identifies network nodes—which, in Optimism’s context, encompass sequencers and any participants involved in the fraud proof mechanisms—as the principal contributors to the network's energy footprint.

Energy consumption estimations are built upon empirical data gathered from diverse sources, including publicly available information, open-source crawling tools, and internal proprietary crawlers. A key determinant in these calculations is the hardware used across the network, with particular emphasis on the specific requirements for running the client software on participating nodes. The energy consumption of these hardware devices is precisely measured in certified testing laboratories. To ensure a comprehensive assessment, the Functionally Fungible Group Digital Token Identifier (FFG DTI) is utilized, where applicable, to identify all implementations of a given asset across various networks, with these mappings consistently updated using data from the Digital Token Identifier Foundation.

Moreover, data pertaining to the deployed hardware configurations and the number of active participants in the network relies on certain assumptions. These assumptions undergo rigorous verification through empirical data whenever possible. A general tenet guiding these assumptions is the presumption of economically rational behavior among network participants. As a precautionary measure, especially in instances of data ambiguity or incompleteness, estimates for adverse impacts, such as energy consumption, are deliberately made on the conservative side, meaning higher values are chosen to account for potential underestimations. For any specific crypto-asset operating on Optimism, its allocated energy consumption is determined as a fraction of the network’s total energy, proportional to that asset's activity within the network, thereby providing a robust, albeit estimated, understanding of its energy demands.

The methodology for assessing the Polygon network's energy consumption is primarily based on a comprehensive "bottom-up" approach, which identifies the various nodes as the fundamental contributors to the network's overall energy footprint. This detailed calculation relies on empirical data collected from diverse sources, including publicly available information platforms, open-source crawlers, and proprietary in-house developed crawlers. The key factors for estimating the hardware utilized across the network are determined by the specific requirements for operating the client software. To ensure the accuracy of these estimations, the energy consumption of the identified hardware devices is precisely measured in certified test laboratories.

An integral part of this energy accounting involves the use of the Functionally Fungible Group Digital Token Identifier (FFG DTI). This identifier is employed to accurately determine and encompass all implementations of the crypto-asset relevant to the scope of analysis. The mappings derived from the FFG DTI are regularly updated, drawing upon data from the Digital Token Identifier Foundation to maintain their currency and reliability. Information concerning the specific hardware deployed and the total number of participants within the network is based on assumptions that undergo rigorous, best-effort verification using available empirical data. It is generally assumed that participants in the network behave in a largely economically rational manner. Adhering to a precautionary principle, in situations where uncertainties exist, estimates for potential adverse impacts are conservatively adjusted upwards, ensuring a robust and cautious assessment.

Crucially, due to Polygon's architectural design as a Layer 2 scaling solution for Ethereum, its energy consumption calculation incorporates a shared security model. Consequently, a proportional share of the Ethereum network's energy consumption is also attributed to Polygon, acknowledging Ethereum's foundational role in providing security to the Layer 2 solution. This specific proportion of Ethereum's energy usage is quantitatively determined based on the gas consumption on the Ethereum network. While the documents mention reliance on "public information sites" and the "Digital Token Identifier Foundation" for data, they do not provide specific URLs for these external resources.

The methodology for assessing the energy consumption related to crypto-assets on the Gnosis Chain involves a comprehensive aggregation across multiple components that reflect the asset’s presence and operational activity on the blockchain. Initially, the overall energy consumption of the underlying network, Gnosis Chain, is meticulously calculated. Subsequently, a specific portion of this network-level energy consumption is precisely attributed to the individual crypto-asset. This attribution is directly proportional to the crypto-asset's activity within the Gnosis Chain, thereby ensuring that the reported energy impact accurately corresponds to its usage footprint.

To ensure the inclusion of all relevant implementations of a crypto-asset within the scope of calculation, the Functionally Fungible Group Digital Token Identifier (FFG DTI) is employed whenever available. The mappings that connect crypto-assets to their respective network implementations are subject to continuous and regular updates, drawing upon authenticated data provided by the Digital Token Identifier Foundation, which guarantees the application of the most current and accurate information. The foundational data concerning the hardware utilized by network participants and the total number of participants active within the Gnosis Chain network is established through a set of carefully formulated assumptions. These assumptions undergo a rigorous verification process, utilizing best-effort empirical data to achieve the highest possible accuracy. A fundamental principle guiding these estimations is the presumption that network participants are largely economically rational, meaning their actions are primarily motivated by self-interest and operational efficiency. Furthermore, in situations where data uncertainty or ambiguity exists, a precautionary principle is consistently applied, leading to conservative, or higher, estimates for any potential adverse environmental impacts. This diligent approach ensures that the reported energy consumption figures are robust and deliberately err on the side of caution when precise data points are less clear.

yearn.finance is present on the following networks: Avalanche, Ethereum, Fantom, Huobi, Near Protocol, Optimism, Polygon.

The methodology for determining the key energy sources and the proportion of renewable energy utilized by the Avalanche blockchain network relies on a multi-pronged approach that integrates geographical data with energy mix statistics. To ascertain the percentage of renewable energy consumption, the initial step involves accurately identifying the geographical locations of the network's nodes. This crucial data is gathered through a combination of public information sites, advanced open-source crawlers, and proprietary in-house crawlers developed specifically for this purpose. In instances where comprehensive geographical distribution information for the nodes is not readily available, the methodology pivots to utilizing 'reference networks.' These reference networks are carefully selected for their comparability to Avalanche in terms of their incentivization structures and underlying consensus mechanisms, ensuring that the estimated renewable energy mix remains relevant and reflective of similar blockchain operations. Once the geographical data for the nodes (either directly identified or inferred from reference networks) is compiled, this geo-information is meticulously merged with comprehensive public data sets on electricity generation. A primary source for this integration is the data provided by Our World in Data, which offers detailed insights into the global energy landscape. The energy intensity of the network is then calculated as the marginal energy cost incurred for processing one additional transaction. This granular measurement provides a precise understanding of the energy overhead per unit of network activity. The specific datasets and sources referenced for this methodology include: Ember (2025) and the Energy Institute - Statistical Review of World Energy (2024), both of which undergo significant processing by Our World in Data. The dataset titled “Share of electricity generated by renewables – Ember and Energy Institute” is a key input, comprising original data from Ember’s “Yearly Electricity Data Europe” and “Yearly Electricity Data,” alongside the Energy Institute’s “Statistical Review of World Energy.” This information is publicly accessible at Share of electricity generated by renewables – Ember and Energy Institute.

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.

Regarding the key energy sources and methodologies for the Fantom network, the provided documentation primarily details the methodology for calculating energy consumption rather than specifying the direct energy sources (e.g., electricity grid mix, renewable percentages) powering the network's operations. The approach to quantifying energy usage is described as a "bottom-up" methodology, where individual operational nodes are identified as the central elements contributing to the network's energy demand. This calculation process is informed by empirical data gathered from public information sites, as well as both open-source and internally developed crawlers. Hardware specifications necessary for running the client software serve as the main criteria for estimating the equipment used across the network. The energy consumption of these hardware components is quantified through measurements conducted in certified test laboratories. For a comprehensive assessment, the Functionally Fungible Group Digital Token Identifier (FFG DTI) is used to identify all relevant implementations of the asset, with regular updates to these mappings from the Digital Token Identifier Foundation. Assumptions regarding hardware deployment and participant numbers are carefully vetted with empirical data, operating under the premise of economic rationality and a conservative estimation approach in cases of doubt. Therefore, while the methodology for measuring consumption is thoroughly outlined, explicit details on the specific types of energy sources remain undetailed within the given context. No specific external document links are available within the provided context for this section.

The methodology for determining the proportion of renewable energy utilized by the Huobi blockchain network focuses on geo-locating the operational nodes and integrating this data with publicly available energy statistics. To achieve this, sophisticated tools are employed, including public information sites, specialized open-source crawlers, and advanced in-house developed crawlers. These tools work in concert to identify the precise geographical distribution of the network's nodes, which are the fundamental computational units contributing to its operation. In instances where specific information regarding the geographic location of nodes is not readily available or cannot be precisely determined, a pragmatic approach is adopted. The methodology then relies on 'reference networks,' which are other blockchain networks deemed comparable to Huobi in terms of their incentivization structure and their underlying consensus mechanism. This comparability ensures that the chosen reference networks offer a realistic proxy for energy consumption and renewable energy integration patterns. The gathered geo-information, whether directly from Huobi nodes or through reference networks, is subsequently merged with comprehensive public data sets sourced from 'Our World in Data.' This integration allows for a detailed analysis of the electricity generation mix in those specific regions, including the share of renewables. The energy intensity of the network is then calculated, defining it as the marginal energy cost incurred with respect to one additional transaction. This provides a granular measure of energy efficiency. For a deeper understanding of the renewable energy data sources, reference is made to: Ember (2025); Energy Institute - Statistical Review of World Energy (2024) – with major processing by Our World in Data. This methodology ensures a data-driven and transparent assessment of the renewable energy footprint associated with the Huobi network's operations.

To accurately determine the proportion of renewable energy utilized by the NEAR Protocol network, a systematic methodology is employed focusing on the geographic distribution of its operational nodes. The initial step involves identifying the precise locations of these nodes using a combination of public information sites, advanced open-source crawlers, and internally developed specialized crawlers. This comprehensive data collection ensures a broad and accurate understanding of the physical presence of the network's infrastructure.In instances where precise geographic information for certain nodes might be unavailable, the methodology incorporates a fallback mechanism. It leverages data from reference networks that are deemed comparable to NEAR Protocol in terms of their incentivization structures and underlying consensus mechanisms. This comparative analysis helps to fill data gaps and provide a reasonable proxy for renewable energy usage in such cases.Once the geographical data for the nodes is established, this geo-information is meticulously integrated with publicly available datasets from reputable sources, notably "Our World in Data." This integration allows for the correlation of node locations with regional energy grid compositions and the prevalence of renewable energy sources in those areas. The final aspect of this methodology involves calculating the energy intensity of the network. This is defined as the marginal energy cost incurred with respect to processing one additional transaction on the NEAR Protocol blockchain. This metric provides a granular view of the energy efficiency per unit of network activity. For detailed information regarding the underlying energy data, the following sources are utilized: Share of electricity generated by renewables – Ember and Energy Institute. This rigorous approach ensures a transparent and verifiable assessment of renewable energy integration within the network's operations.

For the Optimism blockchain network, the notion of "key energy sources" primarily refers to the specific operational components that draw electrical power, consistent with its design as a Layer 2 solution built atop Ethereum. Optimism's energy requirements are inherently linked to the power needed to operate the hardware and infrastructure that support its sequencers. These sequencers are critical for processing and batching transactions off-chain. Additionally, the network of participants, including validators and challengers involved in the fraud proof mechanism, also contribute to the energy expenditure. Since Optimism derives its ultimate security from the Ethereum main chain, the energy consumption associated with Ethereum's underlying Proof-of-Stake validators also indirectly feeds into Optimism's overall energy footprint. While the provided documents do not specify the precise geographical locations or the specific types of power grids (e.g., renewable versus fossil fuel sources) that supply energy to these components, the functional "sources" of energy consumption are fundamentally the computational resources and networking equipment that constitute these operational nodes.

The methodology for quantifying this energy consumption adheres to a meticulous "bottom-up" approach. This process begins by identifying the exact hardware components, such as servers, processors, and associated networking gear, necessary to run the Optimism network's client software. The power draw of these individual hardware devices is typically ascertained through precise measurements conducted in certified test laboratories. The total estimated energy expenditure is then derived by multiplying the measured power consumption of these devices by their estimated operational duration and the assumed number of active participating nodes or sequencers. The Functionally Fungible Group Digital Token Identifier (FFG DTI) is employed to ensure that all relevant instances and implementations of crypto-assets within the network are accurately identified for a comprehensive measurement. Data concerning hardware utilization and the number of network participants are based on empirically verified assumptions, generally assuming economically rational behavior among participants. In situations where data is uncertain, a conservative estimation approach is applied, resulting in higher reported energy impact figures to mitigate any potential underestimations. This framework systematically accounts for the energy consumed by Optimism's operational infrastructure and its interaction with Ethereum's Layer 1, even in the absence of granular details about specific energy grid mixes.

The available documentation details the methodologies for calculating the Polygon network's energy consumption, but it does not explicitly identify the key energy sources (e.g., renewable vs. non-renewable electricity, specific grid mixes) that power its underlying infrastructure. Instead, the focus is on the methodology of consumption assessment. The energy calculation employs a "bottom-up" approach, which considers individual nodes as the primary units of energy consumption within the network. This methodology draws on empirical findings from various data points, including public information sites, open-source crawlers, and proprietary in-house developed crawlers, to estimate the hardware utilized across the network.

The primary determinants for estimating the hardware's energy usage are the computational requirements for running the client software. The energy consumption of these specific hardware devices is meticulously measured and verified in certified test laboratories to ensure precise data collection. To accurately scope all relevant implementations of the crypto-asset for energy calculation, the Functionally Fungible Group Digital Token Identifier (FFG DTI) is utilized, with its mappings regularly updated through data from the Digital Token Identifier Foundation. Assumptions regarding the hardware in operation and the total count of network participants are diligently verified against empirical data, operating under the premise that participants are largely economically rational. In line with a precautionary principle, any uncertainties default to conservative estimates, leaning towards higher figures for potential adverse impacts.

Significantly, as Polygon functions as a Layer 2 scaling solution for Ethereum, its energy consumption calculation also integrates a portion of the Ethereum network's energy usage. This inclusion acknowledges Ethereum's fundamental role in providing security to Polygon. The specific proportion attributed is determined by the gas consumption on the Ethereum network, ensuring a comprehensive view of Polygon's energy demand, considering its reliance on the main Layer 1 chain. While these methodologies provide a clear framework for quantifying energy use, specific details regarding the actual sources of this energy are not elaborated upon in the provided documents, nor are any direct links to external documents specifying these sources or methodologies furnished.

yearn.finance is present on the following networks: Avalanche, Ethereum, Huobi, Near Protocol, Polygon.

The methodology employed to determine the Greenhouse Gas (GHG) emissions associated with the Avalanche blockchain network involves a detailed process of locating network infrastructure and integrating this geographical data with carbon intensity statistics. The initial step is to precisely identify the locations of the network's nodes, a task accomplished through the diligent use of public information sites, sophisticated open-source crawlers, and specialized in-house crawlers. This geographical mapping is fundamental to understanding the specific energy grids from which the nodes draw their power. In situations where direct geographical information on node distribution is insufficient, the methodology relies on 'reference networks.' These are selected based on their structural similarities to Avalanche, particularly concerning their incentivization mechanisms and consensus protocols, ensuring that the estimates are as representative as possible. The collected geo-information, whether direct or inferred, is then carefully integrated with public data regarding the carbon intensity of electricity generation. A significant source for this critical data is Our World in Data, which provides comprehensive global information on electricity generation’s carbon footprint. The GHG intensity of the network is quantified as the marginal emission generated per additional transaction processed. This metric allows for a precise evaluation of the environmental impact as network activity scales. The foundational data and citations for this methodology include: Ember (2025) and the Energy Institute - Statistical Review of World Energy (2024), which have been extensively processed by Our World in Data. The specific dataset used is titled “Carbon intensity of electricity generation – Ember and Energy Institute,” drawing original data from Ember’s “Yearly Electricity Data Europe” and “Yearly Electricity Data,” as well as the Energy Institute’s “Statistical Review of World Energy.” This crucial resource for carbon intensity data is available under a CC BY 4.0 license at Carbon intensity of electricity generation – Ember and Energy Institute.

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.

The methodology for assessing the Greenhouse Gas (GHG) Emissions attributable to the Huobi blockchain network is designed to provide a comprehensive understanding of its environmental impact. This process begins with the critical step of identifying the geographical locations of the network's operational nodes, as the energy mix and associated emissions vary significantly by region. To achieve this, a combination of data collection techniques is deployed, including leveraging information from public sources, utilizing advanced open-source crawlers, and employing sophisticated crawlers developed internally. These tools help pinpoint where the computational activities supporting the Huobi network are physically situated. If precise geographical distribution data for the Huobi network's nodes is unavailable, the methodology incorporates a fallback mechanism. In such scenarios, the assessment relies on 'reference networks.' These are carefully selected blockchain networks that are structurally and operationally similar to Huobi, particularly concerning their incentivization mechanisms and consensus protocols. This ensures that any extrapolated data remains relevant and comparable. The geographical information obtained, whether directly or through reference networks, is then meticulously integrated with extensive public data from 'Our World in Data.' This robust external data source provides critical details on the carbon intensity of electricity generation across various global regions, allowing for the calculation of associated GHG emissions. The overall GHG intensity of the network is calculated as the marginal emission produced per additional transaction, offering a precise metric of the environmental impact for each unit of network activity. For detailed information on the carbon intensity data used, readers can refer to: Ember (2025); Energy Institute - Statistical Review of World Energy (2024) – with major processing by Our World in Data. This rigorous approach ensures that the GHG emissions of the Huobi network are estimated based on verifiable data and established scientific principles, contributing to transparency in its sustainability reporting.

The assessment of Greenhouse Gas (GHG) Emissions for the NEAR Protocol network follows a structured methodology that prioritizes the precise geographical identification of its operational nodes. This process begins by actively determining the locations of all network nodes, utilizing a combination of publicly accessible information sites, sophisticated open-source crawling tools, and specialized crawlers developed in-house. This multi-pronged data acquisition strategy aims to gather comprehensive location data for the network's infrastructure.Should specific geographic distribution data for certain nodes prove unobtainable, the methodology incorporates the use of reference networks. These are carefully selected based on their similarity to the NEAR Protocol in terms of their incentive structures and consensus mechanisms, allowing for an informed estimation of GHG emissions in the absence of direct data. This comparative approach ensures that even with limited direct information, a credible assessment can still be made.The collected geo-information is subsequently integrated with extensive public datasets, prominently featuring data from "Our World in Data." This integration enables the cross-referencing of node locations with regional carbon intensity data of electricity generation, providing a basis for calculating the associated GHG emissions. A crucial metric derived from this methodology is the GHG intensity, which quantifies the marginal emission attributable to processing one additional transaction on the NEAR Protocol network. This metric offers insights into the environmental footprint per unit of network activity. For detailed data on carbon intensity, the following resource is referenced: Carbon intensity of electricity generation – Ember and Energy Institute. This rigorous and transparent methodology underpins the network's efforts to measure and report its environmental impact.

The provided documents offer comprehensive details regarding the methodologies for calculating the energy consumption of the Polygon blockchain network, which are predicated on a "bottom-up" approach focusing on node energy demand, hardware requirements, and the integration of a proportion of Ethereum's energy consumption due to Polygon's Layer 2 architecture. This framework is robust for quantifying electrical energy usage. However, when addressing the topic of key Greenhouse Gas (GHG) sources and their associated methodologies, the provided information is notably insufficient. The documents do not contain any specific data or discussions pertaining to the direct or indirect GHG emissions generated by the Polygon network's operations.

Crucially, there is no mention of the types of emissions (e.g., Scope 1 for direct emissions, Scope 2 for indirect emissions from purchased electricity, or Scope 3 for other indirect emissions within the value chain), nor any dedicated methodologies for calculating, monitoring, or reporting these GHG emissions. The absence of information on the energy mix that powers the network's validators and underlying infrastructure – whether it is predominantly from renewable sources, fossil fuels, or a specific national grid mix – makes it impossible to determine the carbon intensity of the energy consumed. Without such details, a comprehensive assessment of GHG sources cannot be made.

While the methodology for energy consumption includes a "precautionary principle" to make higher estimates for "adverse impacts," these impacts are not explicitly defined or quantified in terms of GHG emissions. There is no information provided on specific conversion factors used to translate energy consumption into carbon dioxide equivalents or other greenhouse gases. The documents do not offer any external links or references to dedicated environmental impact assessments or GHG reporting standards followed by the Polygon network. Consequently, based solely on the provided information, it is not possible to identify the key GHG sources or the specific methodologies employed for their quantification within the Polygon ecosystem.