VNX (VNX) sustainability report

VNX Community Hub is present on the following networks: Arbitrum, Base, Solana.

Arbitrum One does not run a consensus algorithm or a validator set of its own. It is an optimistic rollup: transactions are executed off Ethereum, while Ethereum holds the canonical record and provides final settlement. A sequencer accepts transactions, orders them on a first-come basis and executes them under the chain's state-transition rules, producing blocks roughly four times a second and giving users an immediate local confirmation. The ordered transactions are compressed and published to Ethereum in batches. Because that input data sits on the settlement layer, anyone running the node software can replay it and arrive at the same Layer 2 state without trusting the operator.

Agreement about what that state is happens on Ethereum. Validators post assertions — claims about the rollup's resulting state — to contracts on the settlement layer. Since early 2025 the chain has used a dispute protocol that made validation permissionless, so any party may post an assertion or challenge one rather than only an approved list of operators. Conflicting claims are resolved by an interactive process that narrows the disagreement down to a single step of execution, which Ethereum then adjudicates directly. The protocol is designed so that disputes conclude within a bounded period no matter how many adversaries join them, and so that a single honest participant is enough to defend the correct state. Once the challenge window has passed without a successful dispute, the assertion is confirmed and withdrawals that depend on it become executable through the canonical bridge.

Two qualifications matter for an accurate picture. Ordering is still performed by a single sequencer operated by the chain's development company, so transaction ordering is not decentralized today; censorship is bounded rather than impossible, because a user can submit a transaction to a queue contract on Ethereum and force its inclusion once a defined delay has elapsed. Separately, a security council retains powers over the contracts, which keeps the arrangement short of full trust-minimization. Security therefore rests on Ethereum's proof-of-stake consensus combined with the rollup's fraud-proof mechanism, not on a validator set belonging to the chain itself.

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.

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.

VNX Community Hub is present on the following networks: Arbitrum, Base, Solana.

Fees on Arbitrum One are paid in the settlement layer's native asset and split into two economic components. The execution component prices computation and state access on Layer 2 through a base fee that a control loop raises and lowers with demand, in the style of Ethereum's own fee market. The data component covers the cost of publishing compressed batches to Ethereum. A transaction's share of that component is estimated from how many bytes it adds to a compressed batch, so how well its data compresses matters as much as its size, and the fixed cost of a posting is spread over everything in the batch rather than falling on one transaction. Since Ethereum opened a dedicated data space for rollups, batches are posted there and priced by that space's separate fee market. Both components are converted into a single unit, so a user sees one price rather than two.

Payments flow to several places. The party that posts batches is reimbursed from collected fees, with the data price adjusted over time so that reimbursement tracks what was actually spent. Remaining Layer 2 revenue accrues to protocol-controlled accounts — one covering baseline infrastructure, another collecting congestion revenue — which governance directs, rather than being burned. A portion of ordering rights is also sold: a sealed-bid auction awards a short-lived priority lane for a round lasting under a minute, and the proceeds go to an account that chain governance designates. Contracts compiled to WebAssembly run alongside EVM contracts and are metered on their own resource unit.

There is no staking, delegation, issuance or slashing at this layer. The equivalent penalty is a bond: participants that assert or challenge state must lock collateral, and a party that loses a dispute forfeits it, with part compensating the honest side, so an incorrect claim carries a direct cost. Beneath the rollup, Ethereum's own incentives apply to the data it posts — the base fee there is burned and the priority fee goes to the block proposer. There is no recurring storage rent; state is paid for when it is written.

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.

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.

VNX Community Hub is present on the following networks: Arbitrum, Base, Solana.

The consumption attributed to Arbitrum One has two parts, and they are estimated in different ways. The first is the chain's own infrastructure: the machines running the sequencer and the batch-posting process, the validators that track state assertions and would take part in a dispute, and the broader population of full, archive and RPC nodes that other participants operate. The second is the share of Ethereum's consumption that belongs to the rollup, because settlement and data availability happen there. Ethereum's validators do work on the rollup's behalf whenever a batch is posted, and a proportion of their consumption is apportioned to the chain according to how much of the settlement layer's capacity those postings occupy. Ethereum publishes its own account of how that figure is arrived at (Ethereum energy consumption).

Since nothing here is mined, the first part is estimated by counting machines rather than by modeling operator profitability. The size of the node population is approximated from network crawlers, peer discovery and publicly listed endpoints. A representative hardware specification is inferred from what the client software states it needs to stay in sync — processor class, memory, fast storage and bandwidth — and the electricity that specification draws is taken from controlled measurement of equivalent machines, both under load and idling. The total is the aggregate across the estimated population including idle draw, because nodes run continuously whether or not blocks are full. Dispute participation is episodic and contributes little in normal operation. A fraction of the network total is then attributed to an individual asset in proportion to observed on-chain activity involving it.

These are estimates rather than meter readings, and the limits should be read as part of the figure. Node counts are lower bounds, because machines behind private networks cannot be discovered. The hardware mix is inferred from stated software requirements rather than surveyed from operators. Where the evidence runs out, the assumption chosen is the one more likely to overstate consumption than to understate it, and figures are revised as observation improves.

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 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.

VNX Community Hub is present on the following networks: Arbitrum, Base, Solana.

The renewable share is derived geographically, starting from where the machines that keep the chain running actually sit: the servers hosting the sequencer and the batch poster, the validators that participate in the dispute protocol, and the wider population of full and archive nodes. Their locations are inferred from publicly observable network information — the addresses reachable peers announce, hosting and autonomous-system registries, and public node listings — which yields a country-level distribution rather than a precise address for any individual machine. Much of this infrastructure is hosted with commercial cloud and colocation providers, so the region a provider operates a facility in stands in where a single host cannot be placed more precisely. Where the chain's own distribution is too sparse to observe with confidence, the pattern seen on networks of similar shape — alike in how participants are paid and in the class of hardware they run — fills the gap.

The same exercise is applied to the settlement layer, because the portion of Ethereum's consumption attributed to the rollup carries the geographic profile of Ethereum's validator set rather than that of the rollup's own machines. The two distributions are combined, weighted by how much estimated consumption each accounts for.

Country weights are then matched against published statistics on how much of each country's electricity comes from renewable sources, 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. The result is a consumption-weighted average across the estimated footprint. It is not a statement about what any operator has contracted for: power purchase agreements, on-site generation and renewable certificates are invisible in network data and are not assumed.

Energy intensity is expressed marginally, as the additional electricity associated with one more transaction on top of the infrastructure already running. Because the cost of operating a node is largely fixed and barely responds to how full a block is, that marginal figure is small and moves inversely with throughput, which is why it should not be read as a per-transaction share of the total.

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 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.

VNX Community Hub is present on the following networks: Arbitrum, Base, Solana.

Emissions are derived from the energy estimate rather than measured at the source. The geographic distribution built for the renewable calculation — sequencer and batch-posting infrastructure, dispute-protocol validators, full nodes, and the share of Ethereum's validator set attributed to settlement — is reused, and each country's slice of estimated electricity is multiplied by the average carbon intensity of that country's grid. Grid figures are drawn 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 made available under a Creative Commons BY 4.0 license. Regional totals are summed to give a network figure, and a fraction of that figure is attributed to an individual asset in proportion to observed on-chain activity.

The two scopes are treated separately. Scope 1 covers combustion that the operators themselves control — on-site generators, fuel burned directly on their premises. For a chain whose infrastructure sits in commercial data centers this is ordinarily zero or negligible, and it is reported as such unless direct fuel use is known. Scope 2 is the substantive figure: the emissions embodied in the grid electricity that infrastructure draws. It is computed on a location basis, using the average intensity of the grid a machine draws from, rather than on a market basis reflecting supply contracts or certificates, because supplier-level information cannot be observed from the network. Emissions embodied in manufacturing the hardware or building the facilities that house it fall outside this boundary.

Greenhouse gas intensity is stated marginally, as the additional emissions associated with one more transaction. Two limits are worth stating plainly. Grid intensity statistics are annual national averages, so they miss the hourly and sub-national variation any specific facility experiences, and a data center on a dedicated low-carbon supply will be represented by its country's average. And the figure inherits every uncertainty in the energy and location estimates beneath it; where those rest on assumption, the assumption chosen is the one more likely to overstate the result.

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 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.