VNX Gold (VNXAU) sustainability report

VNX Gold is present on the following networks: Base, Ethereum, Polygon, Solana, Tezos.

Base is a Layer 2 network that executes transactions away from the Ethereum chain and settles them on it. It runs no consensus protocol of its own and has no validator set of its own. Agreement about which Base transactions occurred, and in what order, is ultimately established by the data and the state commitments the network publishes to Ethereum, which are secured by Ethereum's proof-of-stake consensus.

Ordering and execution on the Layer 2 are carried out by a single sequencer, operated by the company that launched the network. It receives transactions, places them into blocks at a fixed cadence and returns a result to the user straight away; those blocks are then compressed and posted to Ethereum in batches, alongside commitments to the state they produce. Once a batch sits inside a finalized Ethereum block, the ordering it encodes is as hard to reverse as Ethereum itself. Users are not wholly dependent on the sequencer for access: a transaction can instead be submitted through a contract on Ethereum, and the rules by which the Layer 2 chain is derived oblige it to be included, which bounds how far the sequencer can censor.

Base is an optimistic rollup, built on the shared OP Stack codebase and part of the Superchain group of networks that use it. State commitments are accepted as correct unless disputed. Anyone may propose one and anyone may challenge one within a dispute window, by playing an interactive game on Ethereum that narrows the disagreement down to a single step of execution, which an Ethereum contract then settles by running that step itself. Both sides post bonds, so an untrue claim and a frivolous challenge are each expensive. Permissionless fault proofs have run on the main network since late 2024, and a multi-party security council with a supermajority threshold governs changes to the contracts; together these place the network at the intermediate tier of the rollup maturity scale commonly used to compare such systems. A withdrawal to Ethereum cannot complete until the dispute window for the relevant commitment has elapsed. Decentralizing the sequencer itself remains outstanding work.

Ethereum reaches agreement through proof of stake, adopted in September 2022 when the original mining-based chain was retired in favor of a validator-driven consensus layer. The protocol family is usually referred to as Gasper. A fork-choice rule named LMD-GHOST selects the head of the chain by following the branch carrying the greatest accumulated weight of validator votes, while a separate finality gadget, Casper FFG, periodically justifies and then finalizes checkpoints, so that reversing them would require destroying an enormous quantity of bonded value.

Time is divided into slots of twelve seconds, and thirty-two slots form an epoch. For each slot the protocol pseudo-randomly designates one active validator to assemble and publish a block, and assigns the rest to committees that vote on what they believe is the correct head and the correct checkpoints. Under healthy conditions a checkpoint becomes final two epochs after it is proposed, a little under thirteen minutes, after which everything beneath it is treated as settled.

Joining the validator set requires a deposit of no fewer than 32 units of the native asset. Since the protocol upgrade of May 2025 a single validator may hold a far larger balance, up to 2,048 units, and earn on the whole of it, which lets an operator running many minimum-sized validators consolidate them into fewer; the activation floor itself did not change. Entry and exit are rate-limited by a queue measured in staked weight rather than in validator headcount, which bounds how fast the composition of the set can turn over.

Security rests on voting power being bonded. A validator that signs contradictory messages can be proved to have done so and is penalized, and the size of that penalty scales with how much other stake was penalized at the same time, so a coordinated attack is punished far more severely than an isolated fault. Should the chain stop finalizing altogether, a separate mechanism gradually erodes the balances of validators that are not participating until the remainder again represents a large enough majority to finalize. Upgrades during 2024 and 2025 changed how large data payloads are distributed and sampled between nodes, without altering this underlying agreement process.

Polygon PoS is an EVM-compatible proof-of-stake network that runs its own validator set and anchors itself to Ethereum by posting periodic checkpoints there. It should not be confused with the other chains that have carried the Polygon name: the zero-knowledge rollup operated under that brand was shut down in 2026, and chains built with Polygon's development kit are independent networks with their own validators. Polygon PoS executes transactions and holds its own transaction data, so it is a sidechain or commit-chain rather than a rollup inheriting Ethereum's execution and data-availability guarantees.

The architecture splits into two node layers that every validator runs together. The execution layer, derived from Go Ethereum, assembles transactions into blocks. The consensus layer coordinates the validator set, tracks staking and finalizes checkpoints; it was rebuilt in 2025 on the Cosmos SDK and CometBFT, which brought checkpoint-based finality down from a wait of one to two minutes to a matter of seconds and capped how deeply the chain may reorganize. At intervals the consensus layer gathers the blocks produced since the last checkpoint into a Merkle tree and submits the root to contracts on Ethereum, where it becomes the reference point for bridge withdrawals.

Staking itself lives on Ethereum. Validators bond the network's native asset, POL, which replaced MATIC in the migration that began in 2024 and now serves as both the staking asset and the gas asset, into contracts on Ethereum mainnet; holders delegate through share-based pools in the same contracts. The active set is capped, so entry requires displacing an incumbent by stake.

Block production changed materially with the Rio upgrade in late 2025. Rather than rotating producers by stake-weighted draw over short intervals, validators now vote, with voting power weighted by stake, to elect the producer or producers for a span. Because a single elected producer builds the span, competing chain tips largely disappear and reorganizations are eliminated. The same upgrade introduced witness-based verification, letting a validator check a block against a supplied witness instead of holding full state, which lowers the storage burden of participating.

Solana runs a proof-of-stake network in which the right to produce a block is allocated in proportion to the quantity of the native asset staked to each validator. What sets the design apart is that ordering is established before agreement is sought. A designated leader runs a sequential hash chain, each output feeding the next input, so the chain cannot be computed faster than a fixed number of steps and a transaction's position within it is evidence of when that transaction was received. This construction, called proof of history, spares validators from negotiating timestamps with one another and lets the rest of the protocol treat the order of events as already settled.

Leadership is not auctioned block by block. At the start of each epoch, which runs for roughly two days, a schedule is derived deterministically from the active stake distribution and assigns every short slot in the epoch to a named validator. Slots follow one another a few hundred milliseconds apart. The scheduled leader gathers transactions, executes them and streams the resulting block to the rest of the set in small fragments relayed through a tree structure rather than pushed to every peer at once. Receiving validators replay the block independently and publish votes for the fork they consider canonical.

Fork choice weights those votes by stake, and each vote commits a validator to its chosen fork for a period that doubles with every further confirmation, so abandoning a block grows steadily more costly. A block that a supermajority of stake has voted on is treated as confirmed within about a second, and it is locked in permanently once enough additional confirmations accumulate, which takes on the order of ten seconds. Safety rests on the assumption that participants acting dishonestly control less than a third of staked value. A revision of the voting layer, approved in a stake-weighted validator vote, is being activated on the main network in stages; it retains stake-weighted validation and the existing block distribution scheme while replacing the incremental lockout rule with direct voting that settles in one or two rounds.

Tezos runs a liquid proof-of-stake network on top of a classical Byzantine fault-tolerant agreement algorithm called Tenderbake, adapted from the Tendermint family. Validators, known on this network as bakers, are allocated the right to propose blocks and to attest them in proportion to their baking power, which is computed once per cycle from the funds they have frozen themselves, the funds other holders have frozen alongside them, and the balances merely delegated to them, with frozen funds weighted more heavily than delegated ones. Within a block's round a designated proposer publishes a candidate and a committee of attesters votes on it; agreement requires more than two thirds of the committee's weight. Finality is deterministic rather than probabilistic, arriving within a couple of blocks, and block intervals were shortened to six seconds by the amendment adopted in January 2026.

The liquid part of the design is delegation that does not move custody. A holder can point the weight of a balance at a baker without transferring the coins, without freezing them and without surrendering the ability to spend them; the baker gains voting weight but never control. A distinct and stronger commitment, staking, freezes funds alongside the baker's own and shares the baker's exposure to penalties, while plain delegation carries no such exposure. Bakers may accept external frozen stake up to a multiple of their own.

What most distinguishes the network is that it amends itself on chain. A proposed protocol change moves through five automatically scheduled voting periods, and if it carries it activates at the end of the last one without a hard fork, coordinated release or chain split; the code the network runs is replaced by the network itself. More than twenty amendments have taken this route. Recent ones reshaped consensus and its economics: the January 2025 amendment bounded issuance against a target staked ratio and raised the external stake limit, the May 2025 amendment shortened cycles to roughly a day and tied part of baker reward to data availability participation, the September 2025 amendment introduced aggregated attestation signatures, and the amendment active since June 2026 widened data availability bandwidth and made attestation lag dynamic.

VNX Gold is present on the following networks: Base, Ethereum, Polygon, Solana, Tezos.

Base has no native protocol asset, no staking and no issuance. Nothing is minted to reward participation and there is no validator or delegation system on the Layer 2. Fees are denominated and paid in ether, the same asset used on the settlement layer.

What a user pays has two parts, and they behave quite differently. The first is the cost of executing the transaction on the Layer 2, metered in gas exactly as on Ethereum and priced by an equivalent algorithmic base fee that moves with how full recent Layer 2 blocks have been, plus an optional tip. Because Layer 2 block space is plentiful, this component is usually very small and fairly stable. The second is a charge for the cost of publishing that transaction's data to Ethereum. It is assessed per transaction from the compressed byte size of the transaction and the prevailing price of settlement-layer data space, and it is collected when the transaction is processed even though the actual posting happens later, in a batch shared with many others. This second component typically dominates the total and is why Layer 2 costs track conditions on Ethereum.

Since Ethereum opened a dedicated market for rollup data in 2024, the network posts its batches into that market rather than as ordinary transaction data. Those data fees are priced independently of execution and are destroyed rather than paid to anyone, which cut this component sharply. A December 2025 change on the settlement layer raised the available data capacity while introducing a floor that ties the minimum data price to ordinary execution costs, so the charge no longer falls to almost nothing whenever demand for data space is light.

Fees collected on the Layer 2 accrue to the entity operating the sequencer, funding the cost of running it and of settling to Ethereum, with a portion shared with the collective that stewards the shared codebase. The other economic mechanism at work is the dispute system: participants who propose or challenge a state commitment post bonds that are forfeited if they are shown to be wrong, which funds honest challenges and makes dishonest claims costly.

Payment inside the protocol flows to validators, the only participants the consensus layer compensates directly. A validator earns newly issued units of the network's native asset for voting promptly and correctly on the head of the chain and on the checkpoints being justified, for serving its turn in the committee that signs headers for light clients, and, when selected to propose, for the block itself. The proposer additionally keeps the priority portion of the fees in that block, together with whatever it receives from the separate market through which many proposers outsource block assembly. There is no delegation inside the consensus rules: stake is either operated directly or entrusted to an operator through arrangements that sit outside the protocol.

Users pay for execution in gas, metered per operation, with writes to persistent state priced far above arithmetic. Every transaction carries a base fee per unit of gas that the protocol sets algorithmically from how full recent blocks have been, and that amount is destroyed rather than paid to anyone, so sustained demand withdraws native asset from circulation. On top of it a user adds a voluntary tip, which goes to the proposer and governs how quickly the transaction is picked up. Data posted on behalf of Layer 2 networks is priced in a second, independent market whose fee is likewise destroyed; a December 2025 upgrade tied the floor of that market to ordinary execution costs so it cannot collapse to a negligible level, and capped the gas any one transaction may consume.

Penalties mirror the rewards. Failing to vote, or voting late or incorrectly, costs a validator roughly what correct behavior would have earned it. Provable equivocation is treated far more harshly: the offender is scheduled for ejection, forfeits part of its balance immediately, and later incurs an additional correlated penalty computed from how much other stake was penalized nearby in time. Prolonged absence while the chain is failing to finalize drains balances until finality can resume. Stakers may take out accumulated rewards without leaving the set, and since 2025 may also trigger a full exit from the execution layer rather than only from the consensus client.

Validators on Polygon PoS are paid for two distinct jobs: producing and executing blocks on the chain itself, and signing the checkpoints submitted to Ethereum. Rewards are distributed per checkpoint, funded by protocol issuance of the native asset together with an allocation of transaction fees, and they are apportioned by stake and by how reliably each validator signed. Because the staking contracts sit on Ethereum, a validator's operating costs include Ethereum gas for checkpoint submission and for staking transactions, a meaningful expense that chain-local fee models do not capture.

Delegation works through validator-specific share pools. A holder exchanges the native asset for shares in a chosen validator, and as rewards accrue the redemption value of each share rises, so returns appear as appreciation of the share rather than as separate payments. Validators take a commission before the remainder flows to their delegators. Stake withdrawn from a validator remains locked for a defined number of checkpoints before it can be moved out, while switching between validators carries no such delay.

The penalty structure is weighted toward lost income. The staking contracts define consequences for double-signing and for sustained unavailability, but in normal operation the dominant economic pressure on a validator is forfeited reward: missed checkpoint signatures and poor block-production uptime reduce what a validator and its delegators earn. The producer election introduced by the Rio upgrade also redistributes fee income, including value captured from transaction ordering, toward validators that are not currently producing, so that supporting the chain stays worthwhile for the rest of the set.

Users pay fees in the native asset under a base-fee-plus-tip model. The base fee moves with how full recent blocks have been and is routed to a burn path, while the optional tip goes to the producer. A 2026 protocol change made that base-fee destination configurable in order to fund a time-limited program that recycles fees for one narrow category of activity, with ordinary transactions continuing to follow the burn path. There is no storage rent, and contract deployment and execution are charged purely as metered gas on the resources they consume.

Two streams of payment reach validators. The protocol issues new units of the native asset on a defined schedule and distributes them at the close of every epoch to validators and to the stake delegated to them, in proportion both to that stake and to the voting credits the validator actually accrued over the epoch; an operator that missed its slots or stopped voting accrues fewer credits and receives a correspondingly smaller share. Holders who do not wish to run hardware delegate through a stake account to an operator of their choice, retain control of that account, and receive the reward net of whatever commission the operator has set. Delegated stake becomes active and inactive only at epoch boundaries, so capital committed to securing the network cannot be pulled out on demand.

Users pay a fixed base fee for every signature a transaction carries. Half of that amount is destroyed and half is paid to the validator that produced the block. A transaction may attach an optional priority fee, quoted per unit of requested compute, which under a protocol change adopted in 2025 goes in full to the block producer; this is the mechanism that rations capacity when demand exceeds what a slot can hold. Program execution is metered in compute units against a per-transaction ceiling, so the cost of a contract call tracks the work it requests rather than a flat tariff.

Storage is charged once, not continuously. An account has to hold a minimum balance scaled to the number of bytes it occupies in order to be exempt from rent, and that balance is a refundable deposit rather than a fee: closing the account returns it. Recurring rent collection has been switched off at the protocol level and rent-paying accounts can no longer be created, so ongoing storage charges do not form part of the fee model as it now stands.

Staked assets are not confiscated by the protocol. No implemented mechanism automatically destroys a validator's stake for equivocation or for being offline; the cost of downtime is forgone reward set against operating expense, including the fees an operator pays to submit its own votes. A scheme to record provable duplicate-block violations on chain, as groundwork for any future economic penalty, is still at proposal stage and would not itself remove stake.

Rewards on Tezos are paid for proposing blocks, for attesting them and for participating in the data availability layer, with a share of baker reward conditional on that last duty. Issuance is adaptive rather than fixed: the rate at which new units of the native asset are created moves within bounds according to how much of the supply is frozen, rising when the frozen proportion falls below a target of roughly half and falling as it rises above, so the protocol leans against both under- and over-participation instead of paying a constant rate. Three roles share the proceeds. A baker runs the infrastructure and takes a fee on what it distributes. A staker freezes funds alongside a baker, earns the higher rate and accepts the baker's penalty exposure. A delegator lends only weight, keeps its funds liquid, earns less and bears no penalty exposure at all.

Penalties are real and are applied to frozen funds. Signing two different blocks at the same level and round, or attesting two conflicting proposals, is denounced by evidence included on chain, and the offending baker's frozen funds, together with the frozen funds of those staking with it, are cut at the end of the cycle. Double baking draws a fixed proportion. Double attesting draws an adaptive proportion that grows with the square of the share of the committee that equivocated together, so an isolated accident costs little while a coordinated attack can consume the entire frozen balance. A fraction of what is taken rewards whoever included the evidence and the remainder is destroyed, and a denounced baker is barred from baking and attesting for a period. A baker that simply goes quiet is deactivated rather than penalized, and must reactivate to resume.

Users pay a transaction fee that scales with the size of the operation in bytes and the gas it consumes, and that fee goes to the block proposer. Storage is charged separately and permanently: allocating space in the ledger, originating a contract or creating a new account destroys an amount proportional to the bytes claimed rather than paying it to anyone. Smart contract execution is metered in gas against per-operation and per-block ceilings, and rollup and data availability operations carry their own costs.

VNX Gold is present on the following networks: Base, Ethereum, Polygon, Solana, Tezos.

The estimate for this network has two components, and they are constructed differently.

The first is the network's own infrastructure. This is a small and largely identifiable set of machines rather than a large permissionless population: the sequencer that orders and executes transactions, the batching service that compresses and submits data to the settlement layer, the service that publishes state commitments, and the replica and archive nodes that third parties operate to serve applications and to independently check what the sequencer produced. The number of independent replicas is estimated from crawlers of the Layer 2 peer-to-peer network and from public information about node operators and infrastructure providers. Hardware profiles are inferred from the published requirements of the node software, which for a high-throughput rollup are materially heavier than for an ordinary chain, and per-device power draw comes from measurement on representative equipment under controlled laboratory conditions, counting idle draw as well as load. The fault-proof machinery adds little in normal operation, since the interactive dispute game runs only when a commitment is actually challenged rather than continuously.

The second component is the share of the settlement layer's consumption that this network causes. That layer is Ethereum, whose own consumption is estimated from its validator population using the node-level method described for that network. A portion is attributed here in proportion to what this network occupies there, principally the data space its batches consume, alongside the gas used by its commitment and dispute contracts. Because the settlement layer's consumption is driven by a continuously running validator set rather than by throughput, this attributed share is modest next to the Layer 2's own footprint, but it is included so that settlement is not treated as free.

Both components are estimates built on public observation and stated software requirements, not metered readings. The replica population is the least observable part and the largest source of uncertainty. Where evidence is thin, the assumptions used are those more likely to overstate impact than understate it, and figures are revised as observation improves. The settlement layer publishes its own account of its energy profile at Ethereum energy consumption.

The figure reported for this network is assembled machine by machine, treating the computers that run the protocol as the thing that draws electricity. The starting point is an estimate of how many independent nodes are operating, built from crawlers that walk the peer-to-peer layer and record every peer they can reach, supplemented by public listings of infrastructure and staking providers and by the protocol's own visible record of how much stake is active and how it is spread across operators.

A representative hardware profile is then inferred for those machines. The client software publishes what it requires in processor, memory and disk terms, and operators have little reason to provision far beyond that, so the profile is derived from those stated requirements rather than from a survey of individual operators. Power draw for the resulting device classes comes from measurement on representative equipment under controlled laboratory conditions, capturing both the load validating places on a machine and the draw of a machine that is powered on but momentarily idle, which for a network of this kind accounts for a large share of the total. Multiplying measured per-device draw across the estimated population over the reporting period yields the network figure. Where a disclosure concerns one of the many assets issued on this network rather than the network itself, a portion of the network total is assigned to it in proportion to observed on-chain transfer volumes.

The limits deserve stating plainly. The node count records what is reachable, not a census, and machines behind restrictive network configurations are missed. The hardware profile is a reasoned inference from published software requirements, not a record of what any particular operator bought. Nothing here is metered at the wall. Where the evidence runs out, the assumptions chosen are those that push the estimate upward rather than downward, so the result is more likely to overstate consumption than to understate it, and it is revised as observation improves. The network's own account of its energy profile is published at Ethereum energy consumption.

Polygon PoS is a staked network, so its consumption is modeled from the machines that run it rather than from mining economics. Two components are added together. The first is the chain's own infrastructure: every validator operates a paired execution and consensus process, which in practice means a heavier machine than a single-process chain of comparable throughput would need, plus the wider population of full and archive nodes serving applications and data consumers. The second is a share of Ethereum's consumption, because the checkpoint and staking transactions that give Polygon PoS its anchor are executed by Ethereum's validators; that share is apportioned by the gas those transactions consume as a fraction of total Ethereum gas.

For the chain's own component, the node count is estimated from peer-discovery crawls, public node listings and the validator set recorded on chain, with the understanding that crawls see only nodes willing to accept connections. A representative hardware profile is inferred from the published requirements for running both node processes, and the electrical draw of such a configuration is taken from measurement of comparable machines, at load and at idle, since a validator's hardware draws power continuously regardless of whether it is currently producing. Aggregating across the estimated population, with an allowance for the overhead of the facilities housing it, gives the chain-local total.

The usual qualifications apply and matter here. The node population and the hardware behind it are inferred from public observation and stated software requirements, not metered. Where evidence is incomplete, the assumptions used err toward a higher figure rather than a lower one. The estimate is revised as observation improves. The gas-based apportionment of Ethereum's consumption is a convention rather than a physical measurement, since Ethereum's validators would run whether or not the checkpoints were posted. And where a share of the network total is attributed to an individual asset issued on the chain, that attribution is made from observed on-chain transfer volumes, which reflects how heavily an asset is used rather than the energy it uniquely causes.

The figure reported for this network is assembled from the machines that run it rather than inferred from any single aggregate quantity. The starting point is a count of active nodes, put together from network crawlers, publicly reachable cluster and gossip information, and data operators choose to publish. That population is then divided by role, because a validator taking part in voting, a machine that only replays the ledger, and the infrastructure that answers application requests do not draw comparable amounts of power.

Each role is matched to a representative hardware profile derived from the resources the client software is documented to need. Requirements here are heavy by the standards of proof-of-stake systems, running to many processor cores, large memory and fast solid-state storage, and the profiles reflect that rather than assuming commodity equipment. Electrical draw per profile is taken from controlled bench measurement of equivalent devices, capturing both the load imposed by processing and the draw of a machine that is powered up but idle, since a node consumes electricity continuously whether or not it is producing a block. Multiplying profiles by the estimated population across the hours of the reporting period gives consumption for the network as a whole. Where a figure is attributed to one asset issued on the network rather than to the network itself, the share is taken from observed on-chain transfer activity for that asset.

The output is an estimate and should be read as one. The node count rests on what is visible from outside, and operators are under no obligation to be visible; the hardware mix is inferred from stated requirements rather than surveyed; and facility overheads such as cooling and power conversion are approximated rather than metered. Where the evidence does not settle a question, the assumption adopted is the one more likely to overstate consumption than understate it, and figures are restated as observation improves or as protocol changes alter the work a node must perform. The network's own climate reporting is published at Solana Climate Dashboard.

Because this is a stake-based network with no computational race, the energy estimate is constructed from the machines that keep it running rather than from any model of mining economics. The relevant population is more varied than a single validator count suggests: bakers and the attesting infrastructure behind them, the accompanying nodes that serve and sample the data availability layer, the nodes operating smart rollups, and the ordinary full nodes that relay and validate without taking part in consensus. Their number is estimated from publicly observable network data, from peer crawling, and from on-chain records of which addresses hold baking rights, which makes the consensus-active portion of the population unusually well bounded even though the servers behind those addresses are not directly enumerable.

Hardware is inferred rather than surveyed. The published operating requirements for the node software, covering processor, memory, disk and bandwidth, indicate what a competent operator would deploy, and the electrical draw of machines of that class is taken from controlled bench measurement at load and at rest rather than from nameplate figures. The annual total is the aggregate across the estimated population including idle draw, since a baking node consumes power continuously regardless of how often it is called on. Where a token issued on this network is being reported rather than the network itself, a portion of the network total is attributed to it using observed on-chain transfer volumes.

Two caveats are specific to this network. Its capabilities are extended by on-chain amendment, and successive amendments have changed cycle length, block interval, attestation duties and data availability bandwidth; each of those alters what a node must store, verify and transmit, so a hardware profile inferred before an amendment can understate requirements after it. Separately, a baker may run several machines for redundancy and signing separation, which a public count of endpoints will not reveal. More broadly, the population and hardware mix are estimates from public observation, not metered readings; where evidence is thin the assumption chosen is the one more likely to overstate consumption than understate it; and figures are restated as observation improves.

VNX Gold is present on the following networks: Base, Ethereum, Polygon, Solana, Tezos.

The renewable share reported for this network is a weighted average of the electricity mixes of the grids its infrastructure draws on, assembled in two steps: establish where the machines are, then attach regional generation statistics to those places.

Locating them is easier for some parts of the network than others. The sequencing, batching and commitment services run in identifiable data center regions, and the hosting regions an operator uses are publicly observable. The wider population of replica and archive nodes is inferred as it would be for any peer-to-peer network, from the addresses peers advertise so that others can reach them, collected by crawlers and supplemented by public directories of infrastructure providers. Resolving a single address to a country is unreliable, but in aggregate these resolutions describe a distribution well enough to weight against. Where the observable sample is too thin, the geographic spread of a structurally comparable network is used in its place, chosen because its operators face similar hosting economics rather than because it runs similar software. The same exercise is carried out for the settlement layer, because part of the figure reported here is an attributed share of Ethereum's consumption, and Ethereum's validator population is spread quite differently from a rollup's concentrated operator infrastructure. The two distributions are weighted by their respective contributions to consumption and combined.

Each location is then matched to published statistics on how electricity is generated in that country or region, and the renewable proportion is the consumption-weighted share falling in regions supplied by renewable generation. Grid averages are used throughout, because the actual supply arrangements of individual hosting facilities are not observable; a facility on a dedicated renewable supply and one drawing ordinary grid power in the same country are treated alike.

Energy intensity is a marginal figure rather than an average: the additional electricity attributable to one further transaction on the network as it currently runs. Because most of the infrastructure runs continuously whether or not it is busy, that marginal quantity is much smaller than dividing total consumption by the transaction count would suggest. The generation statistics come from Share of electricity generated by renewables, compiled by Our World in Data from Ember's electricity datasets and the Energy Institute's Statistical Review of World Energy.

The renewable share reported here is a weighted average of grid mixes rather than a record of what any operator actually buys. It is produced in two steps: establish where the infrastructure sits, then attach regional electricity statistics to those places.

Location is inferred from what the network exposes publicly. Nodes advertise network addresses in order to be reachable by peers, and those addresses resolve to a country accurately enough to describe an aggregate distribution, even though any single resolution may be wrong. Crawlers of the peer-to-peer layer and public directories of hosting and staking infrastructure supply the input. Where the observable sample is too thin or too skewed to stand for the whole population, the geographic spread of a structurally similar network is substituted, chosen because its participants face comparable hardware costs and comparable pressures over where to site machines, on the reasoning that operators respond to the same commercial forces even where the software differs.

Each location is then matched to published statistics on how electricity in that country or region is generated. The renewable proportion for the network is the consumption-weighted share falling in regions where generation is renewable. Grid averages are used because the alternative, knowing each operator's actual supply contract, is not observable; an operator on a dedicated renewable supply and one drawing ordinary grid power in the same country are treated alike.

Energy intensity is reported on a different basis from total consumption. It is a marginal quantity: the additional electricity attributable to processing one further transaction on the network as it currently runs. For a network whose consumption is driven by a validator set that operates continuously regardless of how busy the chain is, that marginal figure is small, and it is not the total divided by the transaction count. The generation statistics are drawn from Share of electricity generated by renewables, compiled by Our World in Data from Ember's electricity datasets and the Energy Institute's Statistical Review of World Energy.

The renewable share attributed to Polygon PoS depends on where its infrastructure physically sits, so the method begins with geolocation. Nodes visible through peer discovery and public network observation are resolved to hosting providers, autonomous systems and countries, giving an approximate map of where validator and full-node capacity is concentrated. Coverage is never complete; where it is too thin to be relied on, the geographic distribution of a network with a similar staking design and operator economics is used as a proxy. The same exercise applies to the portion of Ethereum's footprint brought in through checkpointing, using Ethereum's own observed node distribution.

Those locations are then matched to national electricity statistics. Each country's share of generation from renewable sources comes from Share of electricity generated by renewables, compiled and processed by Our World in Data from Ember's yearly electricity datasets and the Energy Institute's Statistical Review of World Energy. Weighting the country-level shares by the estimated node capacity in each produces a single renewable figure for the network.

Energy intensity is reported as a marginal quantity: the extra electricity associated with processing one more transaction, not the annual total divided by the number of transactions. On a chain whose validators run continuously and produce blocks on a schedule, that marginal figure is much smaller than a simple average would suggest, and the two are not interchangeable.

The limitations are inherent to the approach. An observed hosting location identifies a grid, not a power purchase agreement, so an operator sourcing renewable electricity on a carbon-heavy grid is invisible to the method. Cloud hosting and proxying can misplace a node relative to the hardware actually running it. Annual national averages cannot capture the hourly and seasonal swings in generation mix that continuously running machines draw from. And the borrowed share of Ethereum's footprint carries whatever geographic error is present in Ethereum's own distribution.

The renewable share is derived geographically. Node locations are inferred from what the network exposes about itself: addresses observable through crawlers and public cluster information, resolved to a country or region. Coverage is never complete, because operators may sit behind hosting providers or relays that obscure where the hardware physically sits. Where the geographic spread of this network cannot be observed directly, the distribution of a structurally similar network stands in as a proxy, chosen because its validator economics and agreement protocol place comparable demands on operators and therefore tend to attract them to comparable locations.

Each located node is then assigned the generation mix of the grid that serves it. Those regional mixes come from Share of electricity generated by renewables, compiled by Our World in Data from Ember's yearly electricity data and the Energy Institute's Statistical Review of World Energy. Weighting each region's renewable share by the estimated consumption sitting in that region produces a network-wide proportion. The result describes the grids the infrastructure draws from, not contractual purchases: an operator buying renewable certificates is not treated differently from a neighbor on the same grid, because that distinction cannot be observed from outside.

Energy intensity is reported separately and means something narrower than total consumption divided by transaction count. It is the marginal quantity of energy associated with processing one further transaction. That distinction matters for a network of this type, where validators run continuously at close to constant power regardless of how full the blocks are, so the incremental energy attached to an additional transaction is small while the standing consumption of the validator set is not. Both the renewable share and the intensity figure therefore move with two separate things: the composition and location of the node population, and the grid statistics for the years covered, which are themselves restated as national reporting is revised.

The renewable share follows from where the hardware sits, so the first task is placing it. Locations are inferred from publicly observable network information: the addresses nodes advertise to peers, the autonomous systems and hosting providers those addresses belong to, and what operators publish about themselves. Many bakers are publicly identified services that state where they operate, which helps, but the address of a node still identifies a facility rather than an owner, and it is the facility that draws current from a grid, so hosting location takes precedence over any nationality attributed to the operator.

The picture is inevitably partial. Signing infrastructure is commonly kept off the public network behind a remote signer, redundant machines are not separately visible, and nodes that accept no inbound connections cannot be observed at all. Where the geographic spread cannot be pinned down from the network's own data, the distribution observed on a network with a comparable operating profile stands in for it, selected for similar participation requirements, similar operator incentives and similar hosting behavior. That substitution is the largest single source of uncertainty in the renewable figure and a reason it is less firm than the consumption estimate beneath it.

Locations are then weighted by the consumption attributed to them and matched against regional electricity statistics, so each part of the network's draw inherits the generation mix of its supplying grid. The renewable proportion reported is that consumption-weighted average, which is not the same as the proportion of nodes situated in countries with clean grids. The underlying statistics are annual averages and carry no hourly resolution, so time-of-day variation in the mix is invisible to the method.

Energy intensity is expressed at the margin: the extra electricity associated with one additional transaction at the network's current node population and throughput. Since these nodes run continuously and their draw hardly varies with how busy the chain is, that marginal quantity is small and shrinks as throughput grows, a consequence of how the measure is defined rather than a change in the machines. Regional generation mix is drawn from Share of electricity generated by renewables, compiled by Our World in Data from Ember and the Energy Institute's Statistical Review of World Energy.

VNX Gold is present on the following networks: Base, Ethereum, Polygon, Solana, Tezos.

Emissions are not measured directly. They are derived by attaching a carbon intensity to each unit of electricity the network is estimated to consume, across both parts of its footprint: the machines the network operates itself, and the share of the settlement layer's consumption attributed to the data and commitments it posts there.

The geographic step repeats the one used for the renewable share. The hosting regions of the sequencing and batching infrastructure are publicly observable; the wider set of replica and archive nodes is located from the addresses peers advertise, collected by crawlers and public directories. Where observation is too sparse to characterize the population, the spread of a structurally comparable network is used in its place. The settlement layer's validator population is located separately, because it is distributed quite differently, and the two are weighted by how much consumption each accounts for. Each region is then assigned a carbon intensity, the average greenhouse gas released per unit of electricity generated on that grid, expressed in carbon dioxide equivalent so that methane and the other gases are counted on a common basis. Estimated consumption in a region multiplied by that region's intensity, summed across regions, gives the total.

Two scopes are distinguished. Scope 1 covers emissions from sources the operators of the infrastructure control directly, such as fuel burned on site in a generator. For infrastructure that consists of ordinary servers in commercial data centers drawing from public grids, there is generally nothing in that category, and it is reported as such rather than left out. Scope 2 covers the indirect emissions embodied in the purchased electricity, and is where essentially the whole footprint sits. Emissions from manufacturing and transporting the hardware fall outside this boundary.

Greenhouse gas intensity follows the marginal logic used for energy intensity: the additional emissions attributable to one further transaction, not an average spread across all of them. It inherits the uncertainty of both the consumption estimate and the grid averages. Carbon intensities are taken from Carbon intensity of electricity generation, compiled by Our World in Data from Ember's electricity datasets and the Energy Institute's Statistical Review of World Energy, and made available under the CC BY 4.0 license.

Emissions are derived from the consumption estimate rather than measured, by attaching a carbon intensity to each unit of electricity the network is estimated to draw and summing across the network.

The geographic step repeats the one used for the renewable share. Node locations are inferred from publicly observable network data, principally the addresses peers advertise so that others can connect to them, gathered by crawlers and supplemented by public information about where staking and hosting infrastructure is operated. Where that observation is too sparse to characterize the whole population, the distribution of a comparable network stands in for it, selected because its participants face similar operating economics rather than because its software resembles this one. Each region is assigned a carbon intensity, meaning the average greenhouse gas released per unit of electricity generated on that grid, expressed in carbon dioxide equivalent so that methane and the other gases are counted on a common basis. Estimated consumption in a region multiplied by that region's intensity, summed across regions, gives the network total.

The reporting separates two scopes. Scope 1 covers emissions from sources the operators of the infrastructure control directly, such as fuel burned on site in a generator. For a network of this kind, whose participants overwhelmingly run ordinary servers connected to a public grid, there is generally nothing in that category, and it is reported as such rather than left out. Scope 2 covers the indirect emissions embodied in the electricity purchased to run that infrastructure, and that is where essentially the whole footprint sits. Emissions further up the supply chain, such as those from manufacturing and shipping the hardware, fall outside this boundary.

Greenhouse gas intensity follows the same marginal logic as energy intensity: it expresses the additional emissions attributable to one further transaction rather than an average spread across all of them. Because it inherits both the consumption estimate and the grid averages, its uncertainty combines theirs. Carbon intensities are taken from Carbon intensity of electricity generation, compiled by Our World in Data from Ember's electricity datasets and the Energy Institute's Statistical Review of World Energy, and made available under the CC BY 4.0 license.

Emissions attributed to Polygon PoS rest on the same geographic work as the renewable share, with regional carbon factors applied in place of renewable percentages. Validator and full-node locations are approximated from peer discovery, public network observation and hosting attribution, and a comparable network's distribution stands in wherever direct observation is too sparse. The share of Ethereum's footprint brought in through checkpointing is located the same way, against Ethereum's own node distribution. Each location is paired with the carbon intensity of its national grid, taken from Carbon intensity of electricity generation, processed by Our World in Data from Ember's yearly electricity data and the Energy Institute's Statistical Review of World Energy, and made available under a CC BY 4.0 license. Multiplying regional electricity by regional grams of carbon dioxide equivalent per kilowatt-hour, then summing, yields the annual total.

Scope matters to how the result should be read. Scope 1 captures emissions from sources under the direct control of the network's operators, such as fuel burned on site, which for servers in rented facility space is generally negligible and reported as such. Scope 2 captures the indirect emissions embodied in purchased electricity, and that is where essentially the entire footprint falls. Manufacture and disposal of the hardware sit outside the boundary of this accounting.

Greenhouse-gas intensity is defined marginally, as the incremental emissions associated with one additional transaction rather than the annual total spread across throughput.

The error bars on the emissions figure inherit those on the electricity estimate and add to them. Grid carbon intensity differs by more than an order of magnitude between countries, so a misallocated share of node capacity shifts the result considerably, and annual national averages hide the hourly swings in intensity that machines running around the clock experience in full.

Emissions are derived from the same geographic picture used for energy sources, applied to a different set of grid statistics. Node locations are inferred from addresses observable through crawlers and public cluster information and resolved to a region; where direct observation falls short, the geographic distribution of a structurally comparable network is substituted, selected on the basis that its incentive design and agreement protocol impose similar operating demands.

Each region is then paired with a carbon intensity for its electricity, taken from Carbon intensity of electricity generation, compiled by Our World in Data from Ember's yearly electricity data and the Energy Institute's Statistical Review of World Energy and made available under the CC BY 4.0 licence. Multiplying the electricity estimated to be consumed in a region by that region's carbon intensity, and summing across regions, gives the emissions attributable to running the network.

The disclosure separates two scopes. Scope 1 covers emissions from sources the operators of the infrastructure control directly, such as fuel burned on site; for a network of this kind, whose nodes are ordinary servers in rented facilities, this is normally nil or immaterial, and a zero figure reflects the absence of such sources rather than an omission. Scope 2 covers the indirect emissions embodied in the electricity those machines purchase, and is where essentially the whole footprint of this network falls.

Greenhouse gas intensity follows the same marginal logic as its energy counterpart: it expresses the emissions associated with one additional transaction rather than an average obtained by dividing an annual total by throughput. Because validators consume electricity at a fairly steady rate whether or not blocks are full, the marginal figure is small and is not a proxy for the footprint of the network as a whole. Both the absolute emissions and the intensity figure are sensitive to the grid statistics underlying them, which are revised as national energy reporting is updated.

Emissions figures are calculated, not metered. Each geographically attributed slice of the network's estimated electricity use is multiplied by the carbon intensity of the grid serving that location, using the same placement of nodes that supports the generation-mix analysis: advertised addresses and hosting registrations locate consumption, a structurally comparable network fills the gaps where placement cannot be established, and the result is a consumption-weighted distribution across grids rather than a tally of nodes by country.

Reading the result depends on understanding the scope split. Scope 1 captures emissions from sources the infrastructure operators control directly, essentially fuel combusted on site in backup generation. Bakers, rollup operators and data availability nodes run on general-purpose servers in commercial data centers and offices rather than on dedicated industrial equipment, so direct combustion attributable to the network is negligible and the scope 1 figure is reported as effectively nil. Scope 2 captures the indirect emissions embodied in the electricity those servers purchase and accounts for practically the whole footprint. It is derived on a location basis from average grid intensity, not on a market basis, because renewable energy certificates or power purchase agreements held by individual operators leave no trace in network data and cannot be verified from outside.

The boundary excludes emissions embodied in manufacturing and eventually disposing of the hardware, and it excludes the electricity used by wallets, indexers, explorers and applications built on the chain, which are counted against those services rather than the network. Carbon intensities are annual averages, so seasonal and daily variation in a grid's mix is not represented, and every uncertainty in locating nodes carries straight through into the emissions result.

Greenhouse gas intensity is stated as a marginal figure, the additional emissions attributable to one further transaction at the present node population and throughput; because the consumption behind it is close to fixed, that figure falls as activity rises and should not be read as an efficiency measurement. Carbon intensity values are taken from Carbon intensity of electricity generation, compiled by Our World in Data from Ember and the Energy Institute's Statistical Review of World Energy and published under the CC BY 4.0 license.