Vnx Euro (VEUR) sustainability report

VNX EURO is present on the following networks: Arbitrum, Avalanche, Base, Celo, Internet Computer, Polygon, Solana, Stellar, Tezos, Ripple.

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 Celo blockchain network operates on a Proof of Stake (PoS) consensus mechanism, a foundational element supporting its decentralized architecture, robust network security, and a governance model that is strongly driven by its community. Central to this mechanism are the validators, who bear the significant responsibilities of proposing and creating new blocks, meticulously validating transactions to ensure their legitimacy, and continuously upholding the overall security and integrity of the network. These validators are not chosen arbitrarily; their selection is critically dependent on the quantity of tokens they hold and commit to stake. This economic commitment serves as a powerful incentive for honest participation and contributes substantially to the network's reliability and resilience against potential attacks. The PoS design inherently positions Celo as a significantly more energy-efficient alternative when compared to energy-intensive Proof of Work systems, aligning with broader sustainability goals in the blockchain space. Further enhancing its decentralized nature, Celo incorporates a unique decentralized governance structure. This empowers its token holders to actively engage in the network's strategic direction by voting on various proposals and proposed modifications to the protocol. This community-driven approach ensures that the network's evolution is reflective of its user base's collective interests, promoting adaptability and responsiveness. The continuous validation and proposal of blocks by a rotating set of staked validators, whose economic interest is aligned with the network's success, creates a self-sustaining and secure environment. Through this system, transaction finality is achieved efficiently, and the network can scale its operations while maintaining high levels of security and user participation, which are critical for its mission of financial inclusion.

The Internet Computer Protocol (ICP) utilizes a distinctive consensus mechanism that synergizes Threshold Relay with Chain Key Technology, ensuring decentralized, scalable, and secure operations for its underlying network. Unlike traditional Proof-of-Work or Proof-of-Stake systems, Threshold Relay operates by having a designated group of nodes, referred to as "the committee," collaboratively generate a random beacon. This beacon is pivotal for the impartial selection of the subsequent block producer. This protocol is specifically engineered to deliver scalability and high-speed performance while rigorously upholding decentralization, allowing any node to seamlessly integrate into the consensus process. A core facet of Threshold Relay involves a threshold signature scheme, which mandates cooperation among a subset of nodes to forge a valid signature. This design intrinsically guarantees that consensus is attained even when confronted with faulty or malicious nodes, providing robust Byzantine Fault Tolerance (BFT) and safeguarding network integrity.

Complementing this, Chain Key Technology is instrumental in managing the Internet Computer's state, enabling the network to scale effectively across a vast number of nodes while simultaneously providing rapid and secure transaction finality. This innovative technology facilitates the creation and management of numerous independent blockchains, known as subnet blockchains, each operating with its own specialized set of validators. Chain Key Technology empowers the Internet Computer to efficiently support billions of smart contracts without sacrificing speed, primarily by streamlining quick communication across these subnets and fostering comprehensive cross-chain interoperability. Furthermore, the network integrates Canister Smart Contracts, a decentralized model where the computational tasks of these contracts, which encapsulate application logic, are distributed across various network nodes. These canisters are designed to operate autonomously and scale dynamically with the network’s expansion. The combined consensus mechanisms guarantee transaction finality, meaning that once a block is validated and added, it becomes irreversible, providing the high level of security essential for demanding, high-stakes applications.

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

Stellar operates on a distinctive consensus mechanism known as the Stellar Consensus Protocol (SCP), which is fundamentally built upon the principles of Federated Byzantine Agreement (FBA). This design enables decentralized and leaderless consensus, eliminating the requirement for a closed, pre-defined group of trusted participants often found in traditional Byzantine Fault Tolerant (BFT) systems. Instead, SCP empowers each node within the Stellar network to independently select a specific set of other nodes it trusts, referred to as its "quorum slice." Consensus on the transaction state is achieved when these individual quorum slices sufficiently overlap and collectively agree on the proposed ledger modifications. The consensus process on Stellar involves several structured phases. Initially, transactions are submitted to the network, where nodes validate them against established rules such as sufficient balances and valid digital signatures. This is followed by a "Nomination Phase," where nodes propose values (representing potential transactions or ledger updates) they believe should be included in the upcoming ledger. Nodes actively communicate these nominations to their respective quorum slices. Through continuous voting and a federated agreement process among these slices, a set of candidate values emerges, with this phase persisting until a unified set of values is agreed upon. Subsequently, these agreed-upon values advance to the "Ballot Protocol," involving multiple rounds of voting where nodes either accept or reject the proposed values. Within their quorum slices, nodes exchange votes, and a value progresses to the next stage if it garners sufficient support across intersecting slices. Final acceptance and "externalization" of a value as the next state of the ledger occur once it successfully navigates through various stages, including preparation and confirmation. Following this, the ledger is updated, with all participating nodes reflecting the new, agreed-upon state. The inherent flexibility for nodes to choose their quorum slices fosters decentralization and resilience, allowing the network to maintain consensus even if certain nodes become faulty or malicious. This protocol is also noted for its efficiency, avoiding the energy-intensive mining processes characteristic of many blockchain systems, making it suitable for high-throughput applications.

The Tezos blockchain network operates on a Liquid Proof of Stake (LPoS) consensus mechanism, a sophisticated design that integrates flexible staking participation with an innovative on-chain governance model. This core mechanism allows XTZ token holders to contribute to network security by either directly staking their tokens or delegating them to a validator, commonly known as a baker, without transferring ownership of their assets. This delegation feature significantly broadens participation, making network security more accessible. Bakers are central to the network's operations, responsible for creating new blocks (baking) and validating other blocks through endorsement. Their selection is directly proportional to the amount of XTZ staked or delegated to them; a higher stake increases their probability of being chosen for these critical tasks. To bolster network security further, endorsers are randomly selected from the pool of active bakers to validate and approve blocks proposed by other bakers. A distinctive characteristic of Tezos is its self-amendment protocol, which underpins its adaptive on-chain governance. This system empowers XTZ token holders to propose, vote on, and implement network upgrades directly on the blockchain, bypassing the need for disruptive hard forks. This capacity for self-evolution ensures that the Tezos network can continuously adapt and enhance its functionalities based on community and developer input, fostering a highly flexible and resilient blockchain environment that maintains decentralization while enabling consistent improvement.

The Ripple blockchain, notably the XRP Ledger (XRPL), operates using a distinct consensus mechanism known as the Ripple Protocol Consensus Algorithm (RPCA). This system fundamentally diverges from energy-intensive Proof of Work (PoW) and capital-intensive Proof of Stake (PoS) models, as it does not involve mining or staking. Instead, RPCA relies on a Federated Byzantine Agreement (FBA) model, emphasizing the role of trusted validators to achieve network consensus efficiently. Core to this mechanism are 'Validators' and their 'Unique Node Lists' (UNL). Validators are designated as trustworthy nodes responsible for validating transactions and proposing updates to the ledger. Each individual node within the network maintains its own Unique Node List, comprising a selection of other trusted validators. Consensus is reached when a supermajority of 80% of validators listed in a node's UNL collectively agree on the legitimacy of a transaction or a proposed block. This agreement threshold is critical for upholding high levels of security and ensuring the network's decentralized nature. The consensus process begins with a 'Proposal Phase,' where validators submit new transactions for inclusion in the ledger. This is followed by a 'Validation Phase,' during which validators cast votes on these proposed transactions by comparing them against their respective UNLs. Once the necessary 80% agreement is secured, the transactions proceed to 'Finalization.' In this conclusive stage, the agreed-upon transactions are permanently recorded into a new ledger, rendering them irreversible. The XRPL's design prioritizes rapid transaction ordering and validation, ensuring that transactions broadcast to the network are confirmed swiftly once the 80% validator agreement is met. This streamlined approach allows the network to process transactions efficiently, contributing to its reputation for speed and scalability without the environmental impact associated with traditional blockchain mining.

VNX EURO is present on the following networks: Arbitrum, Avalanche, Base, Celo, Internet Computer, Polygon, Solana, Stellar, Tezos, Ripple.

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 Celo blockchain network employs an incentive model designed to both reward network participants and ensure exceptional accessibility, particularly by maintaining minimal transaction fees for crucial use cases like cross-border payments. This strategy fosters a flexible and user-friendly ecosystem. At the core of its incentive mechanisms, validators receive remuneration from a dual-source system: a portion of transaction fees collected across the network, alongside newly minted tokens. This comprehensive reward structure provides a continuous and strong financial incentive for validators to maintain honest operations, diligently validate transactions, and secure the integrity of the network, thereby ensuring its ongoing reliability. Furthermore, Celo prioritizes user experience through flexible transaction parameters. Users can specify a maximum gas limit for their transactions, acting as a safeguard against excessive charges, especially if a transaction encounters an unexpected failure. They also have the option to adjust the gas price, allowing them to prioritize their transactions for faster processing by offering higher fees if urgency is required. A standout feature of Celo is its innovative payment flexibility, enabling transaction fees to be paid not only in its native asset but also in various ERC-20 tokens. This multi-currency payment option significantly enhances accessibility, especially benefiting individuals who may lack traditional banking services or face hurdles in acquiring specific native blockchain tokens. This approach aligns directly with Celo’s mission to extend blockchain technology to underserved global communities. The network's fee structure is intentionally designed to be minimal, making it an ideal platform for low-cost transactions, particularly those involving international transfers. This emphasis on affordability and flexibility underscores Celo's commitment to creating an inclusive and accessible financial infrastructure.

The Internet Computer Protocol (ICP) employs a comprehensive suite of incentive mechanisms and fee structures designed to engage and reward various network participants, including validators, node operators, and canister developers, thereby maintaining the network’s security, integrity, and operational efficiency.

Validators are compensated for their crucial role in upholding network integrity and security. They stake ICP tokens, receiving rewards for validating blocks, participating in consensus, and ensuring the decentralized network's performance. Similarly, node operators, who are responsible for the physical infrastructure and computational resources supporting the Threshold Relay consensus, are also rewarded for their contributions. Canister developers are incentivized not only through the creation of decentralized applications (dApps) but also by potentially sharing in transaction fees generated from the usage of their dApps and the deployment of smart contracts. Users of these dApps or canisters contribute through usage fees, typically paid in ICP tokens, with developers receiving a share based on application activity.

Governance on the Internet Computer is facilitated by the Network Nervous System (NNS), where ICP token holders can stake their tokens to participate in critical protocol decisions, such as network upgrades, incentive adjustments, and fund allocation. This active participation is rewarded, granting token holders influence over the network's future direction. Staking ICP tokens in the NNS also provides token holders with passive income, further securing the network and aligning their economic interests with its long-term stability.

Regarding applicable fees, every interaction with a canister, or smart contract, on the Internet Computer incurs a transaction fee, generally paid in ICP tokens. These fees are scaled based on the complexity and computational resources consumed by the canister call or network operation. Additionally, developers and users deploying applications are subject to storage fees for canister data, ensuring efficient resource utilization and preventing wasteful storage. Participation in NNS governance, such as submitting proposals or voting, may also involve minor fees to prevent spam and ensure equitable governance. Lastly, node operators, while incurring costs for hardware and operations, have these expenses partially offset by the rewards they receive for providing essential network resources.

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.

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

The Stellar blockchain network utilizes a unique approach to incentive mechanisms and applicable fees, primarily underpinned by its Stellar Consensus Protocol (SCP), which is based on the Federated Byzantine Agreement (FBA) model. Diverging from traditional Proof of Work (PoW) or Proof of Stake (PoS) systems, Stellar deliberately does not rely on direct economic incentives such as mining rewards or staking rewards for validators. Instead, the network secures transactions and maintains integrity through intrinsic network mechanisms and a specific fee structure. The primary incentive for nodes to participate and act honestly stems from the inherent value derived from maintaining a secure, efficient, and reliable payment network. Organizations and individuals operating nodes benefit directly from the network's core functionality and its capacity to facilitate rapid and low-cost transactions. This model encourages active participation by aligning the interests of nodes with the overall health and utility of the Stellar network. Furthermore, the FBA model, characterized by "quorum slices" where each node selects trusted peers, promotes decentralization. This flexibility in node selection reduces the risk of single points of failure and enhances the network's resilience against attacks, thereby providing a robust platform whose value incentivizes its upkeep. Regarding applicable fees, Stellar employs a flat fee structure designed for predictability and efficiency. Each transaction on the Stellar network incurs a minimal base fee of 0.00001 XLM. This exceedingly low and consistent fee makes Stellar particularly well-suited for high-volume transactions and micropayments. A crucial function of this transaction fee is spam prevention; by requiring a small cost for every transaction, the network deters frivolous or malicious activities that could otherwise overwhelm its resources, ensuring efficient operation. These minimal fees are also intended to cover the basic operational costs of the network, supporting its self-sustainability without imposing a significant financial burden on users. Beyond transaction fees, Stellar implements reserve requirements to further protect network integrity and manage resource usage. For instance, creating a new account necessitates a minimum balance of 1 XLM. Additional reserves are required for establishing "trustlines" and "offers" on the Stellar decentralized exchange (DEX), which collectively safeguard against spam and resource abuse while maintaining network efficiency.

The Tezos network is designed with a comprehensive set of incentive mechanisms and fee structures aimed at promoting active participation, ensuring robust security, and supporting the network's long-term sustainability. Key among these incentives are the rewards provided for baking and endorsing. Bakers, who perform the essential function of creating new blocks, receive XTZ tokens as compensation for their efforts. Similarly, endorsers, tasked with validating and approving blocks proposed by others, are also rewarded in XTZ. This dual reward system encourages consistent and honest engagement from all network participants. To further enhance inclusivity, Tezos offers delegation incentives, allowing XTZ holders who prefer not to run a full validator node to delegate their tokens to an active baker. In return, these delegators earn a share of the baker’s rewards, democratizing access to network participation and strengthening overall security. To safeguard network integrity, bakers are required to post a security deposit, or bond, in XTZ. This collateral is subject to forfeiture if a baker engages in malicious activities, thereby creating a strong financial deterrent against dishonest behavior and aligning bakers' interests with the health of the network. Regarding fees, users initiating transactions, such as transferring funds or interacting with smart contracts, pay transaction fees in XTZ. These fees are then distributed to bakers and endorsers, providing additional economic motivation for their critical validation and security services. The network also employs an inflationary reward model, periodically creating and distributing new XTZ tokens to bakers and endorsers. This model fosters continuous participation and network security while managing token availability over time through a gradual increase in supply.

The Ripple blockchain, specifically the XRP Ledger (XRPL), implements a unique incentive structure that markedly contrasts with traditional Proof of Work (PoW) and Proof of Stake (PoS) systems, which typically reward participants with newly minted tokens or a share of transaction fees. Instead, the XRPL's Ripple Protocol Consensus Algorithm (RPCA) operates without direct monetary compensation for its validators. Validators on the Ripple network are not incentivized through block rewards or staking rewards, as there is no mining or direct staking mechanism in place. Their primary incentive stems from the inherent utility and stability of the network itself. For instance, financial institutions acting as validators benefit significantly from the network's efficiency in facilitating fast, reliable, and low-cost cross-border payments, aligning their interests with the network's operational integrity and performance. The absence of mining also means the network avoids energy-intensive computations, which contributes to its fast transaction speeds and overall scalability. Regarding applicable fees, the Ripple blockchain charges minimal transaction fees, typically measured in fractions of an XRP, often referred to as 'drops,' for each operation. The fundamental purpose of these fees is not to reward validators but rather to act as a crucial anti-spam and anti-overload mechanism, safeguarding the network's stability and preventing malicious actors from saturating it with frivolous transactions. Furthermore, a distinctive 'burn mechanism' is integrated into the fee structure: a portion of every transaction fee is permanently removed from circulation. This deflationary process gradually reduces the total supply of XRP over time, which, in turn, can contribute to the long-term value stability and scarcity of the underlying digital asset. This holistic approach ensures network security and efficiency through intrinsic motivations and a unique fee model, rather than direct financial incentives for validators.

VNX EURO is present on the following networks: Arbitrum, Avalanche, Base, Celo, Internet Computer, Polygon, Solana, Stellar, Tezos, Ripple.

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 Celo blockchain network's energy consumption primarily utilizes a "bottom-up" approach. This detailed methodology considers network nodes as the central and most significant factor contributing to the overall energy footprint. The underlying assumptions of this calculation are derived from extensive empirical findings, gathered through a combination of publicly available information sites, advanced open-source crawlers, and proprietary in-house developed crawling tools. A key determinant in estimating the hardware deployed within the network is the specific computational requirements necessary to operate the client software. To ensure accuracy, the energy consumption of these various hardware devices is meticulously measured in certified test laboratories. In this calculation framework, the Functionally Fungible Group Digital Token Identifier (FFG DTI) is employed, whenever available, to comprehensively identify all relevant implementations of the crypto-asset within scope. These mappings are consistently updated to reflect the latest data provided by the Digital Token Identifier Foundation, ensuring the most current and accurate representation. Information pertaining to the types of hardware used and the total number of participants in the network relies on assumptions. These assumptions are rigorously verified through best-effort empirical data analysis. Generally, network participants are presumed to act in an economically rational manner. Adhering to a precautionary principle, in situations of uncertainty, estimations for potential adverse impacts are always biased towards higher, more conservative figures. While specific token energy consumption may aggregate data from multiple networks where the token is active, the core methodology for determining a network's energy consumption remains consistent with this node-centric, bottom-up framework.

The Internet Computer network's energy consumption is determined through a meticulous 'bottom-up' approach, which considers nodes as the primary contributors to the network's overall energy footprint. This methodology relies on empirical findings derived from an array of public information sites, alongside both open-source and proprietary in-house crawlers, to gather essential data. A crucial step involves estimating the hardware utilized within the network, with the main determinants being the specific requirements for running the client software. The energy consumption profiles of these identified hardware devices are precisely measured in certified test laboratories to ensure accuracy.

To standardize and comprehensively identify all relevant implementations of a blockchain asset within scope, the Functionally Fungible Group Digital Token Identifier (FFG DTI) is employed whenever available. The mappings for this identifier are regularly updated, drawing on data provided by the Digital Token Identifier Foundation. The underlying assumptions regarding the hardware deployed across the network and the total number of participants are rigorously verified using empirical data, with a general presumption that participants act largely as economically rational actors. In scenarios where uncertainty arises, a precautionary principle is applied, leading to conservative (higher) estimates for potential adverse environmental impacts. When calculating the energy consumption attributed to a specific asset or function on the Internet Computer, the overall energy consumption of the entire network is first established. Subsequently, a proportional fraction of this network energy is attributed based on the activity level of the particular crypto-asset or application within the Internet Computer blockchain network.

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.

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

The methodology for calculating the Stellar blockchain network's energy consumption adopts a "bottom-up" approach, where individual nodes are identified as the central determinant of the network's overall energy footprint. This comprehensive assessment begins by quantifying the energy usage of the network as a whole. Subsequently, for specific crypto-assets operating on Stellar, a proportional fraction of this total network energy consumption is attributed, based on the asset's activity within the network. This ensures that energy allocation is reflective of actual usage patterns. The foundational data for these calculations is derived from empirical findings, which are systematically gathered through a combination of publicly available information sites, sophisticated open-source crawlers, and proprietary in-house developed crawlers. These tools enable a thorough collection of data pertinent to the operational characteristics of the network. A critical component of this methodology involves estimating the hardware deployed across the network. These estimations are primarily guided by the technical specifications and requirements necessary for running the Stellar client software. The energy consumption profiles of these identified hardware devices are precisely measured in certified test laboratories, ensuring accuracy and reliability in the underlying energy data. Furthermore, to ensure a comprehensive scope, the Functionally Fungible Group Digital Token Identifier (FFG DTI) is utilized, where available, to pinpoint all implementations of crypto-assets relevant to the Stellar network. These mappings are subject to regular updates, drawing data from the Digital Token Identifier Foundation, to reflect any changes in the ecosystem. The information pertaining to the specific hardware used and the total number of participants active within the network is built upon a set of assumptions. These assumptions are meticulously verified through best efforts, leveraging empirical data to ensure their robustness. Generally, participants are presumed to behave in an economically rational manner. In instances of uncertainty, a precautionary principle is applied, leading to conservative assumptions that typically result in higher estimates for potential adverse impacts, thus providing a robust and cautious assessment of energy consumption.

The energy consumption of the Tezos blockchain network is quantified through a comprehensive "bottom-up" methodology that aggregates energy usage across its various operational components. This approach identifies individual nodes as the primary contributors to the network's overall energy footprint. The foundational assumptions for these calculations are derived from empirical data, which is gathered using a combination of public information sources, open-source crawlers, and specialized in-house crawling technologies. A critical step in estimating the hardware used within the network involves determining the technical specifications required to operate the Tezos client software. The energy consumption profiles of these identified hardware devices are then precisely measured in certified test laboratories to ensure accuracy. To achieve a holistic scope, the Functionally Fungible Group Digital Token Identifier (FFG DTI) is utilized, when available, to identify all relevant implementations of the crypto-asset. These mappings are consistently updated based on data from the Digital Token Identifier Foundation, reflecting the dynamic evolution of the network. Information concerning the hardware deployed and the number of participants in the network is based on assumptions rigorously verified with empirical data. Participants are generally presumed to act with economic rationality. As a precautionary principle, in situations of uncertainty, estimates for potential adverse impacts are conservatively adjusted upwards. When determining the energy consumption of a token present on networks like Tezos, the energy consumption of each relevant network is calculated first. A fraction of this network energy is then attributed to the specific token based on its activity within that network. One of the sources utilized for these calculations is tzStats.

The methodology for assessing the Ripple blockchain network's energy consumption, applicable to any crypto-asset operating on it, is founded on a 'bottom-up' approach. This method identifies the network's nodes as the primary contributors to its overall energy usage. The assumptions underpinning these calculations are derived from empirical data gathered through public information sources, open-source crawling tools, and proprietary in-house crawlers. A key factor in estimating the hardware deployed across the network is the minimum system requirements needed to run the client software. The energy consumption profiles of the specific hardware devices are meticulously measured in certified test laboratories to ensure accuracy. When calculating consumption, if available, the Functionally Fungible Group Digital Token Identifier (FFG DTI) is utilized to accurately identify and scope all implementations of the asset being evaluated. The mappings provided by the Digital Token Identifier Foundation are updated regularly to maintain data currency. The information concerning the types of hardware used and the number of participants within the network is based on verifiable assumptions, which are diligently checked against empirical data. A general presumption of economic rationality among participants guides these estimations. Adhering to a precautionary principle, conservative estimates are consistently applied when there is any uncertainty, deliberately opting for higher projections to account for potential adverse impacts. To determine the energy footprint attributable to a specific crypto-asset on the Ripple network, the total energy consumption of the Ripple network is calculated first. Subsequently, a fraction of this network-wide consumption is apportioned to the individual crypto-asset, based on its measurable activity within the network.

VNX EURO is present on the following networks: Avalanche, Celo, Internet Computer, Polygon, Solana, Stellar, Tezos, Ripple.

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.

The determination of key energy sources and the proportion of renewable energy utilized by the Celo blockchain network involves a structured and multi-faceted methodology. The initial critical step is to accurately identify the geographical locations of the network's operational nodes. This identification process leverages a variety of data sources, including readily available public information sites, sophisticated open-source crawlers, and proprietary in-house developed crawlers, all working in concert to pinpoint the physical distribution of the network infrastructure. In scenarios where precise geographical information regarding the nodes is not sufficiently available, the methodology resorts to using reference networks. These chosen reference networks are carefully selected based on their structural comparability, specifically in terms of their incentive mechanisms and underlying consensus protocols, ensuring that the energy profile is as relevant as possible. Once geographical data is established, whether directly or through reference, this information is then meticulously merged with comprehensive public data sets provided by Our World in Data. This integration allows for a contextual understanding of the energy mix and renewable energy penetration in the regions where Celo's nodes are operational. A crucial metric derived from this analysis is the energy intensity, which is precisely calculated as the marginal energy cost associated with processing one additional transaction on the network. This provides a granular insight into the energy efficiency per unit of activity. For further details on the underlying data sources concerning renewable electricity generation, interested parties can refer to the comprehensive datasets compiled by Ember and the Energy Institute, accessible via Share of electricity generated by renewables - Ember and Energy Institute. This meticulous approach ensures a transparent and empirically grounded assessment of the network's energy profile.

To accurately ascertain the proportion of renewable energy utilized by the Internet Computer network, a comprehensive methodology is employed, beginning with the geographical identification of network nodes. This process involves leveraging a diverse range of data sources, including publicly accessible information sites, alongside both open-source and internally developed crawlers, to pinpoint the physical locations where nodes operate. In instances where specific geographic data for nodes is unavailable, the methodology incorporates a fallback mechanism: reference networks are selected and analyzed. These reference networks are chosen based on their comparability in terms of incentivization structures and consensus mechanisms, providing a proxy for energy source assessment.

Once the geographic information is compiled, it is meticulously integrated with extensive public data sourced from Our World in Data, a prominent resource for global statistics and research. This integration allows for a robust estimation of the renewable energy mix at the identified node locations or inferred locations. Furthermore, the energy intensity of the network is quantified, which is defined as the marginal energy cost incurred for processing one additional transaction. This metric offers insight into the efficiency and energy footprint associated with individual network operations. The public information from Our World in Data, which plays a critical role in this calculation, draws upon data from sources such as Ember (2025) and the Energy Institute's Statistical Review of World Energy (2024). This dataset, specifically the “Share of electricity generated by renewables – Ember and Energy Institute,” is accessible via Our World in Data.

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.

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

The methodology for determining the proportion of renewable energy usage within the Stellar blockchain network involves a detailed process that identifies and analyzes the geographic distribution of its operational nodes. The initial step focuses on ascertaining the physical locations of these nodes. This is accomplished through diligent research utilizing public information sites, alongside the deployment of both open-source and internally developed crawlers designed to collect accurate geographical data. Should precise geographic information for all nodes prove unavailable, a pragmatic approach is adopted: reference networks are selected. These reference networks are carefully chosen based on their comparability to Stellar in terms of incentive structures and underlying consensus mechanisms. This ensures that the energy profiles and renewable energy penetration rates of these proxy networks provide a relevant and informed basis for estimation. Once geographical data, either direct or inferred from reference networks, is compiled, it is then meticulously integrated with extensive public information datasets. Specifically, data from "Our World in Data" is utilized, particularly their "Share of electricity generated by renewables - Ember and Energy Institute" dataset. This external data provides a global and regional overview of renewable energy generation, allowing for the calculation of the proportion of renewable energy consumed by the network's operations. The energy intensity of the Stellar network is precisely quantified as the marginal energy cost associated with processing one additional transaction. This metric offers a granular understanding of the energy footprint per unit of activity, providing insight into the efficiency of the network's operations. This robust methodology, which combines location-based energy mix data with empirically verified operational parameters, aims to provide a transparent and defensible assessment of the network's energy sources and their renewable penetration. The data sources for renewable electricity share are primarily Ember (2025) and the Energy Institute's Statistical Review of World Energy (2024), with substantial processing by Our World in Data, accessible via Share of electricity generated by renewables - Ember and Energy Institute.

The methodology for assessing the key energy sources and the proportion of renewable energy contributing to the Tezos network's operation involves a multi-faceted data collection and analytical process. To determine the extent of renewable energy utilization, the geographical locations of the network's nodes are first pinpointed. This identification relies on an analysis of public information sources, alongside the application of both open-source and proprietary in-house crawlers. In scenarios where direct geographical distribution data for nodes is not available, the methodology employs reference networks. These reference networks are carefully chosen for their structural similarities to Tezos, especially in terms of their incentivization frameworks and consensus mechanisms, ensuring that their energy consumption characteristics serve as a comparable proxy. The gathered geographical information is then integrated with extensive public datasets provided by "Our World in Data," a recognized source for global statistical and environmental information. This data integration facilitates a thorough evaluation of the energy mix powering the nodes. Furthermore, the energy intensity of the Tezos network is quantified, calculated as the marginal energy cost associated with processing each additional transaction. The foundational data for this analysis is drawn from reports such as Ember (2025) and the Energy Institute's Statistical Review of World Energy (2024), with significant data processing contributed by Our World in Data. Specifically, the "Share of electricity generated by renewables" dataset from these sources is instrumental in assessing Tezos's reliance on sustainable energy options. More details can be found via Share of electricity generated by renewables - Ember and Energy Institute.

To ascertain the proportion of renewable energy utilized by the Ripple blockchain network, a multi-faceted methodology is employed. The initial step involves pinpointing the geographical locations of the network's nodes. This crucial geo-information is acquired through a combination of public information sites, sophisticated open-source crawlers, and advanced in-house developed crawling technologies. In instances where comprehensive geographical data for node distribution is not readily available, the methodology resorts to leveraging 'reference networks.' These reference networks are carefully chosen based on their comparability in terms of incentivization structures and consensus mechanisms to the Ripple network, ensuring that the estimates remain relevant and robust. Once the geo-information is established, it is then meticulously integrated with publicly accessible data from Our World in Data. This integration provides a comprehensive understanding of the energy mix at the identified node locations, allowing for an accurate assessment of renewable energy penetration. The calculation for 'energy intensity' is defined as the marginal energy cost incurred for processing a single additional transaction on the network. This metric provides insight into the energy efficiency of the network's operations on a per-transaction basis. The data sources underpinning this assessment of renewable energy include: Ember (2025); Energy Institute - Statistical Review of World Energy (2024) - with major processing by Our World in Data. “Share of electricity generated by renewables - Ember and Energy Institute”. This comprehensive approach ensures that the analysis of renewable energy consumption is as accurate and transparent as possible, considering the dynamic nature of blockchain networks and global energy landscapes.

VNX EURO is present on the following networks: Avalanche, Celo, Internet Computer, Polygon, Solana, Stellar, Tezos, Ripple.

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 assessing the key Greenhouse Gas (GHG) sources and calculating emissions for the Celo blockchain network mirrors the rigorous approach applied to energy consumption analysis. A fundamental step involves precisely identifying the geographical locations of the network's nodes. This process relies on a combination of publicly accessible information sites, advanced open-source crawling tools, and specialized in-house developed crawlers designed to map the physical footprint of the network. Should direct geographical data for all nodes be unavailable or insufficient, the methodology wisely employs a strategy of leveraging reference networks. These alternative networks are carefully chosen for their strong comparability in terms of both their incentive structures and their core consensus mechanisms, ensuring that the environmental impact assessment remains pertinent. Following the collection of geographical data, whether directly or through comparative analysis, this information is integrated with extensive public data available from Our World in Data. This integration is essential for contextualizing the carbon footprint in relation to the energy sources used in the identified locations. The GHG intensity of the network is then determined, calculated as the marginal emission produced for each additional transaction processed. This metric offers valuable insight into the environmental impact per unit of network activity. For detailed information regarding the carbon intensity of electricity generation, a primary source for this data, stakeholders can consult the relevant datasets published by Ember and the Energy Institute, which are available through Carbon intensity of electricity generation - Ember and Energy Institute. This methodology ensures a comprehensive and transparent evaluation of the network's environmental performance concerning GHG emissions.

The methodology for determining the Greenhouse Gas (GHG) emissions attributable to the Internet Computer network is closely aligned with the approach used for energy consumption, focusing on identifying node locations and integrating broad energy data. The initial step involves locating the geographic distribution of the network's nodes, a task accomplished through the diligent use of public information sites, complemented by both open-source and proprietary in-house crawlers. This geographical mapping is crucial for assessing the localized energy mix that powers the network’s operations. Should direct geographical data for the nodes be unattainable, the methodology prescribes the use of reference networks. These networks are carefully selected for their structural similarities, particularly in their incentivization frameworks and consensus mechanisms, allowing for a comparative analysis of their emissions profiles.

After gathering and inferring node locations, this geo-information is subsequently merged with comprehensive public data from Our World in Data. This integration provides the necessary context to estimate the carbon intensity of the electricity consumed by the network's infrastructure at these locations. A key performance indicator calculated from this data is the GHG intensity, which quantifies the marginal emissions associated with processing a single additional transaction on the network. This metric provides a granular view of the environmental impact per operation. The data from Our World in Data, essential for these calculations, is drawn from esteemed sources such as Ember (2025) and the Energy Institute's Statistical Review of World Energy (2024). The specific dataset utilized is “Carbon intensity of electricity generation – Ember and Energy Institute,” which is made available under a CC BY 4.0 license and can be accessed through Our World in Data.

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.

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

The methodology for assessing the Greenhouse Gas (GHG) emissions associated with the Stellar blockchain network is systematically designed to pinpoint key emission sources and quantify their impact. A fundamental aspect of this assessment involves accurately determining the geographical locations of the network's operating nodes. This crucial data is gathered through a multi-faceted approach, including thorough searches of public information sites and the deployment of both open-source and proprietary in-house crawlers specifically developed to identify node distributions. In scenarios where comprehensive geographic information regarding node distribution is not readily available, the methodology incorporates a well-defined fallback procedure. In such cases, reference networks are employed. These reference networks are carefully chosen for their strong similarities to Stellar, particularly in their incentive structures and consensus mechanisms, ensuring that any derived estimates are as representative as possible. The geographical data, whether directly observed or inferred from comparable networks, is then meticulously integrated with publicly accessible data. A primary source for this integration is "Our World in Data," specifically leveraging their "Carbon intensity of electricity generation - Ember and Energy Institute" dataset. This dataset provides vital information on the carbon footprint of electricity generation across various regions, allowing for a precise calculation of the GHG emissions linked to the network's electricity consumption. The GHG intensity of the Stellar network is calculated as the marginal emission generated by processing one additional transaction. This metric is instrumental in understanding the environmental impact per unit of activity, offering insights into the efficiency of the network's operational processes from an emissions perspective. This comprehensive methodology, by combining detailed geographical data on electricity grids with empirically validated operational characteristics, strives to provide a transparent and accurate quantification of the network's GHG emissions. The cited data sources for carbon intensity of electricity generation include Ember (2025) and the Energy Institute's Statistical Review of World Energy (2024), processed by Our World in Data, available at Carbon intensity of electricity generation - Ember and Energy Institute. This information is licensed under CC BY 4.0.

The quantification of Greenhouse Gas (GHG) emissions attributable to the Tezos network employs a systematic methodology, akin to the approach for energy consumption analysis. This process begins by identifying the geographical locations of the operational nodes within the Tezos network. This crucial geographical intelligence is compiled through diligent scrutiny of public information, supplemented by the deployment of open-source and internally developed crawlers designed to gather precise location data. In situations where specific geographical distribution data for nodes cannot be obtained, the methodology resorts to a comparative analysis, substituting the missing information with data from carefully selected reference networks. These reference networks are chosen based on their structural and operational similarities to Tezos, particularly in their incentive frameworks and consensus mechanisms, ensuring the relevance of their environmental impact profiles. The collected geographical insights are then thoroughly integrated with publicly accessible environmental data from "Our World in Data." This integration is vital for correlating node locations with regional electricity generation characteristics, which directly influence GHG emission calculations. A key metric in this assessment is the GHG intensity, which is defined as the marginal emission produced per additional transaction processed on the Tezos blockchain, offering insight into the environmental impact on a per-transaction basis. The primary data sources underpinning these calculations are significant reports from Ember (2025) and the Energy Institute's Statistical Review of World Energy (2024), with extensive data processing conducted by "Our World in Data." Notably, the "Carbon intensity of electricity generation" dataset from these sources is pivotal for determining the emissions factor associated with the electricity consumed by the network, and it is licensed under CC BY 4.0. Further information is available through Carbon intensity of electricity generation - Ember and Energy Institute.

The methodology for determining the Greenhouse Gas (GHG) Emissions associated with the Ripple blockchain network mirrors the rigorous approach used for energy consumption. It commences with the precise identification of the geographical locations of the network's nodes. This critical data is accumulated using a combination of public information sites, sophisticated open-source crawlers, and specialized in-house developed crawlers. Should direct geographical distribution data for the nodes be unavailable, the methodology strategically employs 'reference networks.' These reference networks are selected based on their operational similarities to the Ripple network, specifically in their incentivization structures and consensus mechanisms, to ensure the validity and relevance of the emission estimates. Upon acquisition, this geo-information is then integrated with extensive public data provided by Our World in Data, facilitating a detailed analysis of the carbon intensity of the electricity consumed at each node location. The 'GHG intensity' metric is calculated as the marginal emission generated by processing one additional transaction on the network. This metric offers a precise measure of the environmental impact per transaction, reflecting the network's carbon footprint. The primary data source supporting the calculation of GHG emissions is: Ember (2025); Energy Institute - Statistical Review of World Energy (2024) - with major processing by Our World in Data. “Carbon intensity of electricity generation - Ember and Energy Institute”. This source is licensed under CC BY 4.0, ensuring transparency and accessibility of the underlying data. This systematic methodology aims to provide a robust and transparent assessment of the Ripple blockchain network's environmental impact in terms of GHG emissions.