Zyfai (ZFI) sustainability report

Zyfai is present on the following networks: Base, Zksync.

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.

zkSync Era is a validity rollup on Ethereum — the family commonly called zero-knowledge rollups — and it runs neither a consensus algorithm nor a validator set of its own. A sequencer receives transactions, orders them and executes them against the chain's state, returning a confirmation within a second or two. Blocks are grouped into batches, and each batch passes through three stages on Ethereum: the resulting data is committed, a cryptographic proof that the batch executed correctly is submitted and checked by a verifier contract, and the state transition is then applied on the settlement layer.

What separates this design from an optimistic rollup is that correctness is established before the fact rather than assumed and disputed afterwards. Once a proof verifies on Ethereum, the batch is known to have followed the protocol's rules, so there is no fraud proof, no challenger role and no week-long challenge window standing between a withdrawal and settlement; the wait is instead however long producing and verifying a proof takes. Proving is carried out by dedicated proving infrastructure, not by ordinary users. Proofs are built recursively, with many small proofs aggregated into one, and the final proof is compact enough to verify cheaply on Ethereum. The proving stack has been replaced more than once as the technology matured; the current generation proves execution of the chain's state-transition program itself, which brings proving close to real time and removes the need to maintain a separate circuit description mirroring the same logic.

Data availability rests on Ethereum. The chain publishes compressed differences in state — what changed as a result of a batch, rather than every transaction in it — into the dedicated data space Ethereum provides for rollups, which is sufficient for an independent party to reconstruct the chain. Two limits apply here as they do across comparable networks: sequencing is performed by a single operator, and the contracts are upgradeable through a governance process with timelocks and an emergency path rather than being fixed.

Zyfai is present on the following networks: Base, Zksync.

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.

Fees on zkSync Era are denominated in the settlement layer's native asset and cover three costs rather than two. The first is executing the transaction on Layer 2. The second is publishing data to Ethereum: because the chain posts compressed state differences instead of full transaction data, a transaction's share of that cost turns on how many storage slots it touches and whether others in the same batch touch the same ones — repeated writes to one slot within a batch collapse into a single published change, so activity concentrated on the same state costs less than its raw size implies. The third is proving. Generating a validity proof consumes real computation on specialized hardware, and verifying it on Ethereum costs a fixed amount per batch however many transactions that batch contains, so both are spread across the batch and both reward filling batches fully.

The incentive structure follows from that. The operator running the sequencer and the proving infrastructure is paid out of collected fees and is out of pocket if those fees fail to cover data publication and proof verification, which ties its revenue to keeping batches full and published data compact. There is no staking, delegation, issuance or slashing at this layer, because the chain does not select block producers economically and so has no stake to penalize. What protects users instead is the proof itself — an invalid state transition simply cannot be verified on Ethereum — together with a queue on the settlement layer that gives users a route around a sequencer unwilling to include them.

Two further features shape what users actually pay. Account abstraction is part of the protocol rather than bolted on, so a contract can sponsor another account's fees or accept payment in a different asset while settlement still happens in the native one. And there is no recurring storage rent: state is paid for when it is written, through the data component of the fee, rather than carried as an ongoing charge against whoever wrote it.

Zyfai is present on the following networks: Base, Zksync.

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 estimate combines two sources of consumption. One is the chain's own infrastructure, which has a component most rollups lack: alongside the sequencer, the process that publishes data to Ethereum, and the full and archive nodes that applications and infrastructure providers run, there is a proving fleet. Generating validity proofs is genuine computation on servers with high core counts and accelerator hardware, run continuously as batches arrive, and it is a material line in the total rather than a rounding error. The other source is the share of Ethereum's consumption attributable to the rollup, since every batch commitment and every proof verification consumes capacity that the settlement layer's validators pay to provide; that share is apportioned by how much of Ethereum's resources those postings occupy. Ethereum publishes its own account of how its consumption is estimated (Ethereum energy consumption).

Because nothing is mined, the chain's own side is assessed machine by machine. The node population is approximated from crawlers, peer discovery and published endpoints. Representative hardware is inferred from what the client software states it needs, and for the proving side from what the proving implementation documents as its requirements, which are considerably heavier. Power draw for each profile comes from controlled measurement of equivalent equipment under load and at idle, and the network total aggregates across the estimated population including idle draw. A fraction of that total is then attributed to an individual asset according to observed on-chain activity involving it.

The caveats are the substance of the method, not a disclaimer attached to it. Node counts are floors, since machines on private networks cannot be discovered. Proving capacity is particularly hard to observe from outside, because it is operated privately rather than announced to peers, so its size is inferred from proof cadence and stated hardware requirements. Where evidence is missing, assumptions are chosen that are more likely to overstate consumption than understate it, and figures are revised as observation improves.

Zyfai is present on the following networks: Base, Zksync.

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.

Working out a renewable share is a question of geography before it is a question of energy. The relevant infrastructure is the sequencing and data-publishing servers, the proving fleet, and the full and archive nodes operated by applications, bridges and infrastructure providers. Locations are inferred from what the network exposes publicly — peer addresses resolved against hosting and autonomous-system registries, and published operator endpoints — giving a country-level distribution rather than a fixed address for any one machine. Since much of this capacity is rented from cloud and colocation providers, the region a provider assigns to a facility stands in where nothing finer is available. The proving fleet is the hardest part to place, because it is run privately and does not announce itself to peers; where it cannot be located directly, the distribution of comparable computation-heavy infrastructure is used in its place. The same substitution applies more generally: where the chain's own sample is too thin, the pattern seen on networks of similar design fills the gap.

The settlement layer is treated on its own terms. The portion of Ethereum's consumption attributed to the rollup follows the geography of Ethereum's validator population, not of the rollup's servers, so the two distributions are weighted by their shares of estimated consumption and then combined.

Those country weights are applied to published figures for the renewable proportion of national electricity generation, taken 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 output is a consumption-weighted average across the inferred footprint. Procurement is outside what this can see: renewable supply contracts, certificates and on-site generation leave no trace in network data and are not credited.

Energy intensity is a marginal measure — the additional electricity associated with one further transaction on top of infrastructure that is already running. Sequencing and node power draw barely move with block occupancy, and proving cost is amortized across a batch, so the marginal figure is small and declines as batches fill.

Zyfai is present on the following networks: Base, Zksync.

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 energy estimate rather than measured. The geographic breakdown used for the renewable share — sequencing and data-publishing servers, the proving fleet, full nodes, and the portion of Ethereum's validator population attributed to settlement — is reused, and each country's share of estimated electricity is multiplied by the average carbon intensity of that country's grid. Those intensity figures 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 a Creative Commons BY 4.0 license. Country-level results are summed into a network total, and a fraction of that total is attributed to an individual asset in proportion to observed on-chain activity.

The scopes are separated deliberately. Scope 1 covers emissions from sources the operators directly control, meaning fuel burned on their own premises. Infrastructure hosted in commercial data centers normally has nothing material here, and it is reported as zero or negligible rather than inflated by guesswork. Scope 2 is where the figure sits: the indirect emissions embodied in the electricity purchased to run sequencing, proving and node hardware. It is calculated on a location basis, applying the average intensity of the grid serving each region, since a market-based figure would require supply contracts and certificates that are not observable from network data. Emissions embodied in manufacturing the hardware — which for accelerator-heavy proving equipment is not trivial — and in constructing the facilities that host it fall outside this boundary and are not included.

Greenhouse gas intensity is expressed as the marginal emissions of one more transaction, mirroring the treatment of energy intensity. Three limits should be read with the figure: national annual averages conceal hourly and regional variation in real grids; every uncertainty in the energy and location estimates carries through; and where assumptions must be chosen, the more conservative one is taken, so the result is better understood as an upper bound than as a precise quantity.