Synthetix (SNX) sustainability report

Synthetix is present on the following networks: Avalanche, Base, Ethereum, Fantom, Harmony One, Huobi, Near Protocol, Optimism, Polygon.

Avalanche's Primary Network is not a single chain but three, each specialized and all validated by the same set of operators. The contract chain hosts smart-contract execution in an Ethereum-compatible environment and is where most applications and issued assets live. The exchange chain handles asset creation and transfers. The platform chain tracks the validator set, staking, and the registration of the sovereign networks that run alongside the Primary Network.

Agreement across all three comes from the Snow family of protocols, which reaches consensus through repeated randomized sampling rather than through the round-based voting of classical Byzantine fault tolerant designs. There is no leader gathering votes from the entire validator set. Instead each node repeatedly asks a small random sample of validators what they currently prefer, adopts whichever answer carries a sufficient majority of that sample, and accepts a decision once it has seen enough consecutive samples agree. Because a node queries a fixed-size sample rather than everyone, the messaging load per node barely grows as the validator set grows, which is what allows the set to be large without consensus becoming the constraint.

Snowman is the variant used for linearly ordered chains, and Snowman++ layers a proposer schedule over it: block-building windows are assigned to proposers in proportion to stake, with production opening more widely if a designated proposer fails to act, which limits contention without introducing a fixed committee. Sampling remains the voting mechanism throughout. An earlier design in which the exchange chain ordered transactions as a directed acyclic graph was retired in 2023 when that chain was linearized, and the whole Primary Network now runs on the same linear engine.

Acceptance is fast, typically under a second, and once a decision is accepted the protocol treats it as irreversible. Formally the guarantee is probabilistic: sampling parameters can drive the chance of two conflicting decisions both being accepted arbitrarily close to zero, but not to exactly zero, which is a different kind of statement from the deterministic finality a quorum-certificate protocol offers. Validators join the Primary Network by bonding the native asset for a chosen term, holders may delegate to them, and the protocol does not slash bonded principal.

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.

Fantom Opera settles on a transaction order through Lachesis, an asynchronous Byzantine fault tolerant protocol running over a proof of stake validator set. There is no proposer chosen for each round. Every validator gathers the transactions it has received into an event, points that event at the most recent events it has seen from its peers, signs it and gossips it onward. Those events pile up into a directed acyclic graph that each participant holds locally, and because the gossip eventually delivers the same events to everyone, the graph itself carries enough information to derive a single ordering without a further round of voting messages. Once an event has been seen, directly or through the references of later events, by validators holding more than two thirds of the bonded native asset, the protocol treats it as decided and the ordering routine converts that region of the graph into a numbered block.

The safety argument is the classical Byzantine one measured in stake rather than in machines: the derived order holds so long as validators controlling more than two thirds of the bonded amount follow the rules. Joining the validator set is open but capital gated. An operator registers through the chain's staking contract with a self bonded minimum, and other holders may delegate to that operator up to a fixed multiple of its own bond, which caps how much weight any one operator can accumulate. Confirmation is deterministic and typically arrives a second or two after an event is gossiped, so there is no confirmation depth to wait out and no reorganization of sealed blocks. Execution is an Ethereum compatible virtual machine, so ordering and execution are separate concerns.

The network is still live and still sealing blocks, but it is in a managed wind down. Development effort, liquidity and most operators have moved to a successor chain that inherits this consensus design, and a retirement date announced for mid 2026 was afterwards deferred. The mechanism above is the one still running, on a much reduced operator base.

Block production on the Harmony network stopped on 10 September 2026, when its two remaining shards sealed their final blocks within minutes of each other, and the chain no longer reaches agreement on new transactions. What follows describes the mechanism as it ran until that point. Harmony combined a stake-weighted validator election with a Byzantine fault tolerant block protocol and a partitioned chain structure. The network began with four parallel shards and was later consolidated to two: shard 0, which also acted as the beacon chain, and shard 1. Each shard kept its own ledger and state database, so an operator elected to a shard stored and verified only that shard's portion of the whole.

Seats were allocated once per epoch, a period of roughly eighteen hours, through an election run on the beacon shard. Operators registered signing keys and committed stake behind them, but the weight a key carried was not simply the amount behind it. A key's counted weight was confined to a narrow band around the median commitment across all candidates, so stake far above the median was trimmed down to the upper bound of that band and stake below it was lifted to the lower bound. Keys were then ranked by the adjusted weight, the highest-ranked filled the available seats, and each was assigned to a shard by a deterministic function of the key itself. The intent was that accumulating stake beyond a point would yield no further influence over consensus.

Within a shard, blocks were produced by a leader and confirmed by the elected committee in a single round of voting. Votes used an aggregatable signature scheme, collapsing hundreds of individual signatures into one constant-size signature and keeping message volume linear in committee size. A block committed once signatures representing more than two thirds of the shard's voting power had been gathered, giving finality in roughly two seconds, and an unresponsive or faulty leader was replaced through a view change. Shard 1 anchored its blocks to the beacon shard through crosslinks, and value crossed between shards as receipts that the receiving shard checked against those anchors. A defect in that cross-shard receipt accounting was exploited in August 2026, allowing receipts to be credited more than once, and the chain was rolled back to an earlier checkpoint before the wind-down was agreed.

The Huobi ECO Chain, generally known as HECO, no longer operates. Its validators stopped producing blocks in January 2025 after the body that governed the chain resolved to wind it down, and the public nodes, block explorer and project site that supported it have since been withdrawn; the hostnames they used no longer resolve. What follows describes the mechanism as it last ran.

HECO was an Ethereum-compatible chain built from a fork of the predominant Ethereum client, and its consensus combined authority-based block sealing with an on-chain staking contract, a pairing its documentation called hybrid proof of stake. At most twenty-one accounts formed the active set at any moment. Membership was not granted administratively: a candidate locked the network's native asset in a validator contract, any account could add stake to a candidate, and the ranking by total stake decided who was in. The set was not recomputed continuously. Blocks were sealed in a fixed rotation and the chain advanced roughly every three seconds, with every two hundred blocks closing an epoch; only at an epoch boundary did the client consult the staking contract and install the new leading twenty-one.

Agreement worked differently from the two-thirds voting schemes used by Byzantine fault tolerant chains, and the distinction is material. A HECO block was valid because it had been sealed by the member of the current authority set whose turn it was, not because a supermajority had voted for it, so the chain could in principle fork and confirmation was probabilistic: a block became safe as successive blocks were built on top of it, not at the instant it was proposed. Settlement was quick in practice because the authority set was small, identifiable and rotated predictably, but the network did not provide the immediate, irreversible finality that a voting protocol delivers. Security rested on the assumption that a majority of those twenty-one sealers, economically committed through the stake locked behind them, would not collude. Separate contracts governed admission, proposals and the recording of misbehavior, which kept the rules of participation on the chain itself rather than in agreements between operators.

NEAR Protocol is a sharded proof-of-stake network. Rather than running independent chains alongside one another, it treats the whole system as a single logical chain whose every block is assembled from per-shard pieces called chunks, so one block header commits to the state of every shard at once. An account's address determines which shard holds it. The network launched with a single shard and has since grown to several; a 2026 protocol release made the split automatic, so a shard now divides at an epoch boundary once its state passes a configured size threshold, instead of waiting for a coordinated upgrade.

Participation is decided by a recurring auction over stake. An operator wanting to validate submits a staking proposal; at each epoch boundary the protocol sorts the proposals, derives a seat price from them, and assigns duties to those clearing it, with some accounts producing blocks, others producing the chunks of a particular shard, and the rest acting as chunk validators. Epochs last roughly half a day and assignments are reshuffled each time, so no operator holds a given shard indefinitely. Because the seat price floats with the total stake competing for entry, the threshold rises and falls with demand rather than being fixed in the protocol.

The design that most distinguishes this network is stateless validation, adopted in 2024. A chunk producer publishes, alongside its chunk, a compact witness carrying exactly the state that chunk touched together with proofs against the shard's state root. Chunk validators check the work from that witness alone, so they can verify a shard without storing it, and shards can be added without pushing hardware requirements steadily upward.

Blocks are produced under Doomslug, which allows the next block to be built once more than half of the stake has endorsed its predecessor, giving a fast practical guarantee that the chain will not reorganize. Full finality comes from a separate rule once two-thirds of the stake has endorsed, normally within a small number of blocks. Safety rests on more than two-thirds of staked value behaving honestly, and activity crossing shard boundaries travels as receipts routed asynchronously over subsequent blocks.

OP Mainnet operates no validator set and no consensus algorithm of its own. It is an optimistic rollup: blocks are produced away from Ethereum, but the canonical history and final settlement live on Ethereum. A sequencer accepts transactions, orders them and produces Layer 2 blocks on a two-second cadence, which is what gives users a fast confirmation. The ordered transaction data is compressed and published to Ethereum in batches, and every node derives the canonical chain by reading that data back from the settlement layer. Deriving the chain from Ethereum rather than from the sequencer's word is what makes the arrangement verifiable: anyone holding the published data can recompute the same state independently.

Correctness is enforced after the fact. Claims about the chain's output state are posted to a dispute-game contract on Ethereum, and since the fault-proof system was opened to the public in mid-2024 anyone may post such a claim or dispute one, with no allowlist involved. A challenge proceeds as a bisection game in which the two sides repeatedly narrow their disagreement until a single step of execution remains; that step is then executed inside a deterministic fault-proof machine on Ethereum, which settles the matter on-chain. Both sides lock bonds and the loser forfeits. A claim that survives a challenge window of roughly a week is treated as final for the purpose of withdrawing assets to Ethereum.

Two limits belong in any accurate description. Sequencing rests with a single operator, so ordering is centralized in practice; censorship is constrained rather than prevented, because a transaction can be deposited through a contract on Ethereum and must then be included in the chain. And a guardian role, alongside a security council, retains emergency powers, including pausing withdrawals and returning the dispute system to a permissioned mode should it fail — a deliberate safeguard that nonetheless keeps the chain short of full trust-minimization. Ultimate security comes from Ethereum's proof-of-stake consensus, whose validators finalize the data the rollup depends on.

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.

Synthetix is present on the following networks: Avalanche, Base, Ethereum, Fantom, Harmony One, Huobi, Near Protocol, Optimism, Polygon.

Validators on the Primary Network are compensated out of protocol issuance under a capped supply schedule rather than out of user fees. An operator bonds a minimum amount of the native asset for a chosen staking term and is paid at the end of that term provided it met the uptime requirement. A validator's effective weight is capped relative to its own bonded stake, which limits how much delegated stake any single operator can concentrate. Holders who do not run infrastructure may delegate to a validator for a term and receive the reward net of the fee that validator charges.

The enforcement model is unusual in that bonded principal is not slashed. A validator that fails to meet the uptime threshold simply does not receive its reward for that period and gets its stake back, so the penalty is forfeited income rather than confiscated capital. The most recent protocol upgrade reworked these terms considerably: the minimum staking term was shortened from two weeks to two days, staking terms can now renew automatically with rewards compounded at a chosen ratio, the uptime threshold required to earn a reward was raised for newly started validations, and the average rate at which rewards are issued was reduced.

Sovereign networks running alongside the Primary Network are funded differently. Since the late-2024 upgrade that separated them, their validators no longer need to bond a large stake and validate the Primary Network as well; instead they pay a continuous fee to the platform chain that adjusts with the number of active such validators relative to a target, rising when the population exceeds it and easing when it falls short.

Users of the contract chain pay a base fee plus an optional tip, priced dynamically in the style of Ethereum's fee market. The distinguishing feature is that the fee is burned rather than paid to the block producer, so transaction activity reduces supply and offsets issuance instead of rewarding validators directly. The minimum base fee has been lowered by upgrade and is now a floor that validators adjust collectively rather than a hard-coded constant. The exchange and platform chains likewise price their operations dynamically, and those fees are burned as well. There is no storage rent.

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.

Compensation on this network changed materially when its successor chain launched. Newly issued native asset that previously paid validators for sealing blocks was redirected to the successor, so ongoing issuance to operators here has been wound down to nothing and what an operator earns now comes from the transactions it helps order. A portion of every fee collected in a period is destroyed rather than paid out, and the remainder is shared among validators when the period closes, weighted by the stake behind each one and by whether it stayed available and responsive. That distribution is handled by an upgradeable staking contract rather than hard coded into the client, so the split can be changed by governance without a coordinated fork.

Holders who do not operate infrastructure participate by delegating to an operator. Delegated amounts count toward the operator's weight in consensus and in the fee split, and the delegator receives the corresponding share of what the operator earns, less a commission the operator retains. Delegation can be left liquid or committed for a fixed term, with longer commitments historically earning a larger share, and withdrawing a bond or a delegation takes effect only after a waiting period, which prevents stake from leaving immediately after misbehavior.

Penalties operate on the bond. An operator that signs conflicting events for the same position is flagged by the protocol as having broken the rules, is removed from the active set and forfeits its bonded amount; balances delegated to that operator can be reduced along with it, which is why the choice of operator is a real decision for a delegator rather than a formality. Simple unavailability is not punished by confiscation, but an absent operator earns nothing for the period.

Users pay for computation and storage through gas metering in the native asset, with the price per unit set by demand for block space. Contract calls cost in proportion to the work they cause, ordinary transfers cost a fixed minimum, and there is no recurring rent charged for data already stored.

No rewards accrue and no fees are payable on Harmony now that block production has ceased; the arrangements below are those that applied while the network ran. Two roles were paid. Operators ran the signing nodes and set a commission rate, and delegators assigned stake to an operator without running any infrastructure themselves. Each block minted an amount of the network's native asset, and that issuance was distributed across the elected keys in proportion to the adjusted stake weight used in the election, with each operator taking its commission before the remainder passed through to the delegators behind it. Because weight was capped near the median, stake piled onto an already large operator earned nothing extra, so the reward schedule itself carried most of the burden of discouraging concentration.

Two kinds of failure carried consequences. Signing two conflicting blocks at the same height was treated as an attack: a proportion of the stake behind the offending keys was confiscated from the operator and its delegators alike, a floor applied regardless of how small the offending voting share was, half of the confiscated amount was destroyed and half paid to whoever submitted the evidence, and the operator was barred from the network permanently. Falling short on availability was treated as neglect rather than attack. An operator whose keys signed fewer than two thirds of the blocks they were asked to sign over an epoch was marked inactive, dropped from the following election, and had to submit a transaction to put itself forward again.

Users paid for execution in the network's native asset, metered in gas in the manner familiar from Ethereum-compatible chains: a fixed charge for a plain transfer and a charge proportional to the computation and storage a contract call consumed, multiplied by a price the sender set. There was no congestion-responsive base fee, no separate priority auction, and no recurring charge for holding state on the chain. Transaction fees were destroyed rather than handed to the proposer, which offset part of the issuance and meant that heavier use diluted existing holdings less. A transfer between the two shards was paid for on the originating shard and completed once its receipt had been taken up on the receiving side.

Nothing is paid or charged on HECO today, since block production ended in January 2025; the arrangements below are those in force when it stopped. The economics were simple, and in one respect unusual: the protocol minted nothing. There was no block subsidy and no new issuance of the native asset to compensate the sealers. The entire reward available to the active set was the gas that users had already paid in the blocks it produced, collected and shared out in proportion to the stake standing behind each validator. Security was funded wholly by demand for block space, so a quiet chain paid its operators correspondingly little.

Participation had a low floor. A candidate registered by locking a small fixed quantity of the native asset, and any holder could add stake to any candidate without operating infrastructure, with no minimum on the delegated amount. Stake moved a candidate up the ranking, and a candidate that reached the leading twenty-one entered the active set at the next epoch boundary, so entry and exit were continuous rather than requiring a governance decision each time. Withdrawal was deliberately slow: after submitting an unstaking request a participant waited a fixed number of blocks, on the order of a few days, before the assets could be moved, keeping capital exposed long enough for misconduct to come to light.

Discipline was administered by a dedicated contract that counted failures to produce a block when due. The penalties escalated with that count rather than arriving in one step: past a first threshold the validator forfeited the rewards it had accumulated, and past a second it was removed from the active set. The principle is worth stating precisely, because it differs from the slashing designs common elsewhere: the locked stake itself was not confiscated, so an absent or unreliable validator lost its income and its seat but not its capital. Users paid for execution in the native asset, metered in gas exactly as on Ethereum, with a fixed charge for a plain transfer and a charge proportional to the computation and storage a contract call performed, multiplied by a price the sender chose. There was no congestion-responsive base fee, no burn, and no recurring charge for occupying state; the whole of every fee reached the validators.

The network issues a fixed proportion of the supply of its native asset each year, on the order of five percent, and divides it: roughly nine-tenths is paid to the validators of each epoch and the remaining tenth goes to a protocol treasury. Epoch rewards are proportional to the seats an account holds, and they are conditional on work actually performed. A validator that produces fewer than the required proportion of the blocks or chunks it was assigned earns nothing for that epoch and is removed from the set for the following one, with re-entry taking a further two epochs. Holders who do not want to operate infrastructure can delegate to a staking pool contract, which pools their stake behind an operator and passes back rewards net of that operator's commission.

Penalties deserve to be stated precisely. The protocol specifies a design under which stake could be confiscated for provable misbehavior, but that mechanism is not switched on. What actually operates is economic exclusion: forfeited rewards, ejection from the validator set, and the delay before an ejected account can return. Stake itself is not taken.

Execution is paid for in gas. Unlike an auction-priced fee market, the cost of each operation is fixed in protocol configuration, and the gas price moves only gradually in response to how full recent blocks have been, so costs stay predictable and do not spike sharply with congestion. Gas spent is destroyed rather than handed to validators, who are paid from issuance instead. Historically three-tenths of the gas burned by a contract call was credited to the account of the contract being called, a rebate meant to fund contract authors; network governance has voted to set that share to zero so the entire amount is burned.

State is charged for separately through storage staking. An account must keep an amount of the native asset locked in proportion to the bytes it occupies, in the region of one unit per hundred kilobytes, and the lock is released when the data is deleted, so there is no recurring rent. Access keys and meta-transactions additionally let one party cover another's gas.

Transactions are paid for in the settlement layer's native asset, and the amount splits in two. The execution component prices computation and state access on Layer 2 through a base fee that adjusts with demand plus an optional priority fee, and it is small because the work happens away from Ethereum. The data component covers publishing the transaction's data to Ethereum so the chain can be reconstructed, and it usually dominates. Since Ethereum introduced a dedicated data space for rollups, batches are posted there instead of as ordinary call data, and the pricing function reads both Ethereum's ordinary base fee and the separate fee for that data space — each relayed onto Layer 2 by a system contract every block — scaled by two parameters the chain operator can tune. What a transaction pays is proportional to its compressed size, estimated with a compression function, so the cost of a posting is apportioned across the transactions in the batch rather than charged to whichever one happens to trigger it.

There is no staking, delegation or reward issuance at this layer, and consequently no slashing. The sequencer's incentive is the margin between the fees it collects and what it spends publishing data to Ethereum, which gives it a direct reason to batch efficiently. Net of those costs, the surplus from this chain is directed to the collective treasury that funds protocol development and public-goods programs, and other chains built on the same software contribute a defined share of their own revenue on the same basis. Participants in the proof system are paid differently: bonds locked in a dispute are forfeited by the losing side to the winner, so challenging an incorrect claim is rewarded while posting one is expensive.

Deploying and calling smart contracts is charged on the resources consumed, on the same basis as on Ethereum, and there is no recurring storage rent — state is paid for when it is written. Underneath, the data the chain posts is subject to Ethereum's own rules, where the base fee is burned and the priority fee goes to the block proposer.

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.

Synthetix is present on the following networks: Avalanche, Base, Ethereum, Fantom, Harmony One, Huobi, Near Protocol, Optimism, Polygon.

Avalanche is a staked network, so its energy estimate is assembled from the machines that participate rather than from hardware economics driven by block rewards. One structural feature shapes the calculation: a single Primary Network validator runs one node that validates the contract chain, the exchange chain and the platform chain together. The three are therefore not summed as though they were three independent populations, which would count the same hardware three times; the footprint is modeled against one node population serving all of them.

The estimate combines three inputs. The validator set is read directly from the platform chain, which makes the consensus-participating population unusually well observed compared with networks where it has to be inferred. The surrounding population of non-validating full and archive nodes, run by applications, data services and trading venues, is approximated from peer-discovery crawls and public listings, which see only nodes that accept inbound connections and so tend to undercount. A representative hardware profile is then inferred from the published requirements for the node software, and the power draw of such a configuration is taken from measurement of comparable machines under sustained load and at idle, since a validator draws power continuously whether or not it is currently proposing.

The result carries qualifications that should be read as part of the figure rather than as footnotes to it. Node counts and hardware profiles are inferred from public observation and stated requirements, not metered at the socket. Where evidence is missing, the assumptions chosen are the ones more likely to overstate consumption than to understate it. Estimates are revised as crawler coverage and hardware information improve. Sovereign networks that maintain their own validator sets are accounted for separately from the Primary Network rather than folded into it. And where a share of the total is attributed to an individual asset issued on the chain, that share is derived from observed on-chain transfer volumes, which measures how heavily an asset is used rather than the energy it uniquely causes.

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.

The figure reported for this network is an estimate assembled from the machines that run it, not a metered reading taken at the wall. The starting point is the size and composition of the node population. Validating and non validating nodes are counted using network crawlers, peer discovery traffic and information operators publish themselves, and that count is the unit of consumption, because in an asynchronous Byzantine fault tolerant design the work of taking part is ordinary server work: receiving gossip, checking signatures, executing transactions and holding state. There is no competitive computation to model and no hash rate to convert into equipment.

A representative hardware profile is then inferred from what the client software asks for. The processor, memory and storage specifications published as the requirements for running a node are mapped onto commercially available server configurations that meet them, and the electrical draw of those configurations is taken from laboratory measurement of the equipment both under load and at rest. The network total is the aggregate of that draw across the estimated node set for the length of the reporting period, idle time included, since a synchronized node that happens to be handling nothing still consumes power.

Two qualifications matter when reading the result. The node count and the hardware mix are inferences drawn from public observation and from stated software requirements, so neither is exact; where the evidence is thin the assumptions chosen are the ones more likely to overstate consumption than to understate it, and the figures are restated as observation improves. The second qualification is particular to this network. Because development, liquidity and the majority of operators have moved to its successor chain, the operator base is contracting and its composition shifts faster than a periodic estimate can follow, so a figure produced for one reporting period should not be read as a stable rate that will carry into the next one.

The estimate is assembled from the machines that ran the network, not derived from anything the protocol itself reports. Because Harmony settled blocks through stake-weighted voting rather than mining, there is no hash rate to reason from and no computational race to model; what the network drew was determined by how many machines were kept powered and what each of them consumed. The first input is therefore a count of participants: the elected committees on each shard, the redundant nodes operators kept ready so as not to miss their turn, and the non-validating full nodes, archive nodes and public endpoints that wallets and applications depended on. That population is reconstructed from the chain's own staking and election records together with crawls that enumerate reachable peers, and neither source sees every machine.

The second input is a hardware profile. The client software published the processor class, memory and storage needed to keep pace with the chain, and a representative machine is inferred from those stated requirements rather than surveyed from the operators themselves. Power draw for that representative machine is taken from measurements made on comparable equipment under controlled conditions, covering idle as well as loaded operation, since a validating node consumes power continuously whether or not transactions are flowing. Total consumption is that per-device draw applied across the estimated machine count over the reporting period. The shard structure matters here: an operator elected on both shards had to run a node for each, so the machine count exceeded the operator count.

Two limitations are inherent to this approach. It is a reconstruction from public observation and stated software requirements, not a metered reading of anyone's equipment, and real operators run machines that depart from the published specification in both directions. Where the evidence runs out, the assumption selected is the one that produces the larger number, so the result is likelier to overstate the impact than to understate it, and estimates are revised as observation improves. The portion of the network total attributed to an individual asset issued on the chain is set by that asset's observed on-chain transfer activity as a proportion of all activity. The chain's status is the overriding caveat: with block production stopped and the supporting infrastructure being retired, consumption attributable to continuing operation falls away, and any quantity reported relates to the period in which the network was live.

Consumption is built up from the machines that ran the chain rather than read off any protocol metric. HECO settled blocks by rotating a small authority set, not by mining, so there is no computational race to model and no work rate to convert into hardware; what the network drew was decided by how many servers were kept powered and what each consumed while idle, which for this design was most of the time. The first input is a machine count. The active set was capped at twenty-one, but the relevant population was larger: candidates outside the active set who stayed ready to enter at an epoch boundary, the redundant nodes operators kept so as not to miss their turn, and the non-validating full nodes, archive nodes and public endpoints that wallets and applications relied on. Those are reconstructed from the chain's own staking records and from crawls of reachable peers, and neither source was ever complete.

The second input is what such a machine draws. The client published the specifications required to keep pace with the chain, and because it derived from an Ethereum client those specifications pointed at ordinary server hardware rather than anything purpose-built. A representative configuration is inferred from them, and its power draw is taken from measurements on comparable equipment made under controlled conditions, covering idle as well as loaded operation. The total is that draw applied across the estimated machine count for the period in question.

The honest description of the result is a reconstruction rather than a measurement. No operator's hardware was metered; the machine count rests on what a crawler could see and the hardware profile on what the software asked for rather than on what operators actually bought. Where evidence ran out, the more conservative assumption was taken, meaning the one yielding a larger figure, so the result errs toward overstatement. The share of the network total attributed to an individual asset issued on the chain is set by that asset's observed on-chain transfer activity relative to all activity. The decisive caveat is the chain's status: with block production ended and the supporting infrastructure withdrawn, there is no ongoing consumption to estimate, and any quantity reported describes the period when the network was still running.

The figure reported for this network is an estimate assembled from the infrastructure that runs it, not a metered reading. It works upward from the population of machines taking part, and from what each of those machines can be expected to draw.

The first input is the size and composition of that population. It is estimated from publicly observable network data, including peer discovery, the published validator set and its per-epoch duty assignments, and open directories of operators, gathered by automated collection of the same information. For a sharded network this matters more than a headline count, because duties are divided between block producers, the producers of each shard's chunks, and the chunk validators that verify them; the estimate has to cover all of those roles rather than only the accounts holding seats. Machines that take no part in consensus but that the network needs in order to be usable, such as archival nodes and query-serving infrastructure, are included as well.

The second input is consumption per machine. A representative hardware profile is inferred from the resources the client software states it requires, covering processor cores, memory, disk and bandwidth, and power draw is attributed from laboratory measurement of devices matching that profile. Draw is counted continuously, including the large idle component, since a validator has to stay online whether or not it currently holds an assignment. Multiplying the per-device figure across the estimated population produces the network total.

The limits should be read plainly. Both the population and the hardware mix are inferences drawn from public observation and from stated software requirements; operators are not surveyed and meters are not read. Where the evidence runs out, the assumption chosen is the one that makes the impact look larger rather than smaller, so the resulting figure is more likely to overstate than understate. Stateless validation also changes the profile over time, since verifying a shard no longer requires storing it, and the estimate is revised as observation of the network improves and as the shard count changes.

Two distinct things are being estimated, and conflating them is the usual source of error. The first is the electricity drawn by the chain's own infrastructure: the sequencer, the process that publishes batches, the challenger software that watches state claims and would contest an invalid one, and the population of nodes that other participants run, each of which pairs a consensus client deriving the chain from Ethereum with an execution client replaying it. The second is the portion of Ethereum's own consumption that belongs to the rollup, since every batch it posts occupies capacity that the settlement layer's validators pay to provide. That portion is apportioned by how much of Ethereum's resources the chain's postings take up. Ethereum publishes its own description of how its consumption is estimated (Ethereum energy consumption).

Nothing in this design is mined, so the chain's own side is estimated at the level of individual machines rather than through the economics of hardware competition. The node population is approximated from crawlers, peer discovery and publicly advertised endpoints. A representative machine specification is inferred from what the client software states it requires to keep pace with the chain, and the power that specification draws is taken from controlled measurement of comparable hardware, recorded both under load and at rest. The network total is the aggregate across the estimated population, including idle draw, since these machines run continuously. From that total, a fraction is assigned to an individual asset according to observed on-chain activity involving it.

The honest caveats belong with the number. The node count is a floor rather than a census, because machines behind private networks are not visible to a crawler. The hardware profile comes from stated requirements, not from a survey of what operators actually bought. And where evidence is thin, the assumption taken is the one that produces the larger figure rather than the smaller one. Estimates are revised as observation of the network improves.

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.

Synthetix is present on the following networks: Avalanche, Base, Ethereum, Fantom, Harmony One, Huobi, Near Protocol, Optimism, Polygon.

Establishing a renewable share for Avalanche is first a question of geography, because the same hardware draws very different electricity depending on which grid it sits on. The validator set is enumerated from the platform chain, and the network addresses behind those validators, together with the wider set of nodes seen through peer discovery and public network observation, are resolved to hosting providers, autonomous systems and countries. That yields an approximate map of where node capacity is concentrated. Where the mapping is too incomplete to support a result, the observed distribution of a network with comparable staking economics is used in its place.

The map is then joined to national electricity statistics. Each country's share of generation from renewable sources is taken 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 country-level shares by the estimated node capacity located in each gives one renewable percentage for the network as a whole.

Energy intensity is a marginal measure rather than an average: the additional electricity associated with one further transaction being processed. Because validators run continuously and blocks are produced on a schedule regardless of how full they are, the marginal figure is considerably lower than the annual total divided by transaction count, and the two answer different questions.

Several limits constrain what the renewable percentage can mean. Hosting location reveals a grid but not a contract, so operators procuring renewable electricity on a carbon-heavy grid are not distinguished from those that are not. Cloud regions and proxied connections can place a node's apparent location away from its actual hardware. Annual national averages flatten the hourly and seasonal movement in generation mix. And validator infrastructure is concentrated in a relatively small number of hosting markets, so the result is sensitive to how a handful of large operators are located.

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 reported here is not measured at any node; it is inferred from where the machines appear to be and from what the electricity in those places is generated by. Placement is reconstructed from what the network exposes in public: the addresses observed while peering and in crawler records, resolved only as far as a country or a region, never to a particular building. Coverage is never complete. Operators sit behind hosting providers, relays and privacy services, and on a network whose operator base is shrinking the observable sample thins further, so where a distribution cannot be observed with confidence the geographic spread of a structurally similar network, one with a comparable operating model and a comparable cost of participation, stands in for the missing part.

That distribution is then weighted against regional electricity statistics. The share of generation from renewable sources in each region where nodes are placed is taken from published national and regional figures, drawn here from Share of electricity generated by renewables, compiled by Our World in Data from Ember and from the Energy Institute's Statistical Review of World Energy. Combining the two gives a weighted renewable proportion for the estimated consumption of the network as a whole, on the working assumption that each machine takes its power from the surrounding grid at that grid's ordinary generation mix. That is an approximation, and it cuts both ways: an operator buying certified clean power is invisible to it, as is one on an unusually carbon heavy local supply.

Energy intensity is reported on a different basis from the total. It expresses the marginal energy associated with one additional transaction rather than an average obtained by dividing annual consumption by annual transaction count. The distinction matters most on a lightly used network, where the equipment continues to draw power whether or not anyone transacts, and where a falling transaction count would otherwise inflate a simple average without any change in the physical energy being consumed.

The renewable share is not something the protocol records, so it is inferred from where the machines stood. Reachable nodes advertise network addresses, and those addresses are resolved to a country through public address-allocation registries. What comes out is a distribution of the node population across jurisdictions rather than a list of sites, and it is imperfect in predictable ways: addresses can be proxied, they can belong to hosting providers registered in one country while the equipment sits in another, and a machine behind a firewall may not be visible to a crawler at all.

Where too little of a population can be placed this way to be credible, the geographic profile of a different network stands in. The substitute is chosen because its participants face a comparable cost structure and a comparable reason to run a node, on the reasoning that similar incentives draw operators to similar places. Harmony's elected set was small, and a small set placed only partially is exactly where such a substitution does real work, so it is a genuine source of uncertainty rather than a formality.

Each jurisdiction in the distribution is then matched to published statistics on how its electricity is generated, and the renewable proportion for the network is the share-weighted average across those jurisdictions. Those statistics are 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 figures are annual and national, so they cannot capture an individual facility's supply arrangements or the hour of day at which load actually fell.

Energy intensity, as the term is used here, is a marginal rather than an average quantity: the additional electricity needed to carry one more transaction. On a network that settles by scheduled voting, almost the whole of the draw is fixed, because committees sign at a set interval whether the block is full or empty. The true marginal cost of one further transaction is accordingly close to nothing, and the intensity figure is better read as total consumption spread over observed throughput. It serves to compare networks of different designs on a common basis; it does not measure what any single transaction caused. Since the chain stopped producing blocks, no fresh node observation is possible and the distribution can only be drawn from what was recorded while it operated.

The renewable share is not a property the protocol records, so it is inferred from where the machines stood. Reachable nodes advertise network addresses, and those addresses are resolved to a country using public address-allocation registries, producing a distribution of the node population across jurisdictions rather than a list of physical sites. The method is imperfect in known ways: addresses can be proxied, they can belong to hosting providers registered in one country while the equipment sits in another, and machines behind firewalls never appear to a crawler.

Two features of this network compound that uncertainty. The authority set was capped at twenty-one and much of the surrounding infrastructure was operated by a small number of parties, so a handful of unplaceable nodes could shift the distribution materially, in a way that would not happen on a network with thousands of independent operators. More decisively, the chain has stopped: its nodes and endpoints are gone, so no fresh observation is possible and the geographic picture can only be drawn from what was recorded while it ran. Where a population cannot be placed with enough confidence, the geographic profile of a structurally similar network is substituted, chosen because its participants face a comparable cost structure and a comparable reason to run a node, on the reasoning that like incentives put operators in like places.

Each jurisdiction in the resulting distribution is matched to published statistics on how its electricity is generated, and the renewable proportion for the network is the share-weighted average across them. Those statistics come 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. They are annual national figures, so they cannot reflect an individual facility's supply arrangements or the hour at which load fell.

Energy intensity here is a marginal quantity, the additional electricity required to carry one further transaction. On a chain that sealed blocks on a fixed schedule, nearly all of the draw was fixed and independent of how full the blocks were, so the genuine marginal cost of one more transaction was close to zero. The reported intensity is better understood as total consumption spread over observed throughput, a way of putting networks of different designs on a common footing rather than a measurement of any single transaction's effect.

The renewable share attributed to this network is derived from where its machines sit, not from any claim about the electricity the network chooses to buy. The first step is to place the node population geographically. Locations are inferred from publicly observable network data, including the addresses peers announce to one another, information operators publish about themselves, and the hosting providers and data-center ranges those addresses resolve to, all gathered by automated crawling of the peer network. Sharded duty assignment does not complicate this step, since what matters is where a machine physically sits rather than which shard it happens to serve in a given epoch.

Coverage is never complete. Some operators sit behind relays or cloud infrastructure that hides the physical site, and others publish nothing at all. Where a network's own geographic spread cannot be observed in sufficient detail, the distribution observed for a structurally similar network is used in its place, chosen because its participants face comparable incentives and carry comparable consensus duties, and can therefore be expected to cluster in broadly the same regions.

Those locations are then matched to regional electricity statistics. Each machine is associated with the grid serving its location, and the renewable proportion of that grid's generation is applied, weighted by the share of estimated consumption sitting in each region. The result is a consumption-weighted renewable share for the network as a whole, which moves both when operators relocate and when the underlying grids change from year to year.

Energy intensity is reported alongside it and means something narrow: the marginal energy associated with processing one further transaction. Because most of this network's consumption is the fixed cost of keeping validators online rather than anything proportional to throughput, that marginal figure falls as activity rises, and it should not be read as an average cost per transaction. Grid statistics come from Share of electricity generated by renewables, compiled by Our World in Data with major processing from Ember and from the Energy Institute's Statistical Review of World Energy.

Establishing a renewable share begins with location rather than with energy. The infrastructure in question is the sequencing and batch-publishing servers, the challenger nodes that watch the dispute system, and the wider set of full and archive nodes run by applications, bridges and infrastructure providers. Where those machines sit is inferred from publicly observable network information — announced peer addresses resolved against hosting and autonomous-system registries, and public listings of node operators — which supports a country-level picture rather than a precise location for any one machine. Because much of this runs on rented cloud capacity, the region a provider states for a facility is used in place of a finer-grained address. Where the chain's own sample is too thin to support a distribution, the pattern observed on networks built along similar lines, with comparable participant roles and hardware classes, substitutes for the missing portion.

The settlement layer is handled separately and then combined. The share of Ethereum's consumption attributed to the rollup takes on the geographic profile of Ethereum's validator population, which is distributed differently from the rollup's own servers, so the two distributions are weighted by their respective contributions to estimated consumption before being merged.

Those country weights are applied to published figures for the renewable proportion of each country's 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. What comes out is a consumption-weighted average across the inferred footprint, reflecting the grids the infrastructure most likely draws on. It does not capture procurement: renewable supply contracts, certificates and behind-the-meter generation cannot be seen in network data and are not credited.

Energy intensity here means a marginal quantity — the extra electricity associated with one further transaction, given the infrastructure already running. Node and sequencer power draw barely varies with how full a block is, so this marginal figure is small and falls as throughput rises. It is not the network total divided by the transaction count, and the two should not be compared.

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.

Synthetix is present on the following networks: Avalanche, Base, Ethereum, Fantom, Harmony One, Huobi, Near Protocol, Optimism, Polygon.

The emissions estimate for Avalanche reuses the geographic work behind the renewable share and substitutes carbon factors for renewable percentages. The validator set enumerated from the platform chain, together with the nodes observed through peer discovery and public network data, is resolved to countries; where that resolution is too sparse, the distribution of a network with comparable staking economics stands in. Each country is then paired with the carbon intensity of its electricity, 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 published under a CC BY 4.0 license. The estimated electricity in each region, multiplied by that region's grams of carbon dioxide equivalent per kilowatt-hour and summed across regions, gives the annual emissions figure.

Reporting separates two scopes. Scope 1 covers emissions from sources the operators control directly, such as fuel burned on site for power or heat; for a population of servers hosted in rented facility space this is generally negligible and is reported accordingly. Scope 2 covers the indirect emissions embodied in the electricity those machines buy from their grids, which is where effectively the entire footprint of a staked network falls. Hardware manufacture and end-of-life disposal lie outside the boundary of this accounting.

Greenhouse-gas intensity follows the same marginal logic used for energy: the incremental emissions associated with one more transaction, not the annual total divided by throughput.

Uncertainty accumulates across the two steps. Whatever error exists in the electricity estimate passes straight through into emissions, and the geographic step adds its own, since national grid intensities span more than an order of magnitude and shifting a large operator from one country to another visibly moves the answer. Annual averages also conceal the hourly variation in grid intensity that continuously running machines are exposed to in full.

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 are derived from the estimated electricity consumption of the network and the carbon content of the electricity that supplies it, not from any direct measurement of exhaust. The chain of reasoning begins with the same inferred geography used for the energy assessment: node locations are approximated from publicly observable network data, resolved to regional level, and where the observation is too sparse to support a distribution, the spread of a network with a comparable operating model is used in its place.

Each region is then matched to a carbon intensity for its electricity supply, expressed as emissions per unit of electricity generated, using Carbon intensity of electricity generation, compiled by Our World in Data from Ember and from the Energy Institute's Statistical Review of World Energy and made available under the Creative Commons Attribution 4.0 license. Applying each region's intensity to the consumption attributed to that region, and summing, gives the network total.

The result is almost entirely indirect. Scope one covers emissions released by sources the operators themselves control, which for a network of this kind means essentially nothing: there is no combustion in running a node, and on site generation, where it exists at all, is not separately observable, so this component is reported at or near zero. Scope two covers the emissions embodied in the electricity purchased to run the machines, and that is where the network's footprint sits. Because the estimate is grid average by region, it does not credit an operator who buys certified renewable supply, nor does it charge an operator whose actual supply is dirtier than the regional figure suggests.

Greenhouse gas intensity is stated on a marginal basis, as the additional emissions associated with one further transaction, for the same reason that energy intensity is. On a network in wind down, where activity can fall while the infrastructure keeps running, an average computed from a shrinking transaction count would move sharply without anything physical having changed.

Emissions are derived from the same node distribution that underpins the renewable share, with a different coefficient applied to it. Once the node population has been apportioned across jurisdictions, the electricity attributed to each jurisdiction is multiplied by the average carbon intensity of that grid, meaning the greenhouse gases released per unit of electricity generated there, and the network total is the sum across jurisdictions.

The reporting boundary separates two scopes. Scope 1 covers emissions from sources the operators control directly, which for a network of this kind is effectively nothing: consensus and endpoint nodes are general-purpose servers that burn no fuel on site, and standby generation at hosting facilities does not run in normal operation. Scope 2 covers the indirect emissions embodied in the electricity those machines purchase, and it accounts for essentially the entire result. A location-based convention is applied, using the average intensity of the grid each node draws from, because contractual instruments such as renewable supply certificates are not observable per node and cannot be verified from outside the operator. Emissions embodied in manufacturing, shipping and disposing of the hardware fall outside this boundary and are not counted.

Carbon intensity figures come 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 Creative Commons Attribution 4.0 license. These are annual national averages, so they smooth over the hourly and sub-national variation that in reality determines what a given machine's electricity caused.

Greenhouse gas intensity per transaction follows the same marginal logic as its energy counterpart and inherits the same qualification. The network's emissions were driven by how many machines were kept running, not by how many transactions crossed them, so dividing the total by throughput produces a comparison aid rather than a causal statement about any one transfer. Uncertainty in the node count and in the geographic distribution propagates directly into the emissions figure, and where a substitute distribution has stood in for locations that could not be observed, the result is only as sound as that substitution. Because the chain no longer produces blocks, emissions attributable to its operation end as the remaining infrastructure is decommissioned, and reported quantities describe the period in which it ran.

Emissions are derived from the same node distribution that underpins the renewable share, with a different coefficient applied. Once the node population has been apportioned across jurisdictions, the electricity attributed to each is multiplied by the average carbon intensity of that grid, meaning the greenhouse gases released per unit of electricity generated there, and the network total is the sum of those products.

The reporting boundary distinguishes two scopes. Scope 1 covers emissions from sources the operators controlled directly, which for a network of this kind was effectively nil: the sealing and endpoint nodes were general-purpose servers burning no fuel on site, and standby generation at hosting facilities does not run in normal operation. Scope 2 covers the indirect emissions embodied in the electricity those machines purchased, and it accounts for virtually the whole figure. A location-based convention is used, applying the average intensity of the grid each node drew from, because contractual instruments such as renewable supply certificates cannot be observed per node or verified from outside the operator. Emissions embodied in manufacturing, transporting and disposing of the hardware sit outside this boundary.

Carbon 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 the Creative Commons Attribution 4.0 license. They are annual national averages and therefore smooth over the hourly and sub-national variation that in reality determines what a particular machine's electricity caused.

Greenhouse gas intensity per transaction follows the same marginal logic as its energy counterpart, with the same qualification attached. Emissions were driven by how many servers were kept running rather than by how many transactions passed through them, so dividing the total by throughput yields a comparison aid rather than a causal claim about any individual transfer. Uncertainty in the machine count and in the geographic distribution carries straight through into the emissions result, and where a substitute distribution has stood in for locations that could not be observed, the figure is only as reliable as that substitution. Because the chain has ceased operating and its infrastructure has been withdrawn, no emissions arise from its continued operation, and any quantity reported relates to the period when blocks were still being produced.

Emissions for this network are derived from its electricity use and from the carbon content of the grids supplying it. The estimated energy total is first broken down by location, using the same geographic picture built for the energy analysis: node addresses observed on the peer network, operator information published openly, and the hosting ranges those addresses belong to. Where the observed spread is too sparse to rely on, the distribution of a structurally comparable network is substituted, selected for similar incentives and similar validation duties rather than similar size.

Each block of consumption is then multiplied by the carbon intensity of the grid serving it, expressed as emissions per unit of electricity generated in that region, and the results are summed. This means the outcome is driven as much by where operators choose to host as by how much electricity the network draws, and it changes year to year as national generation mixes shift.

The reported figures separate two scopes. Scope 1 covers emissions from sources the network's operators control directly, such as on-site fuel combustion; for a network of this kind that is effectively nil, since the infrastructure is commodity servers drawing grid power in third-party facilities. Scope 2 covers the indirect emissions embodied in the electricity purchased to run that infrastructure, and it accounts for essentially the whole footprint. Emissions embodied in manufacturing the hardware, and in building and cooling the facilities that house it, fall outside both and are not included.

GHG intensity is the marginal figure, the emissions attributable to one additional transaction. As with energy, the bulk of the total is a fixed cost that continues regardless of throughput, so intensity falls as usage grows and is not an average. Carbon intensity values come from Carbon intensity of electricity generation, compiled by Our World in Data with major processing from Ember and from the Energy Institute's Statistical Review of World Energy, and made available under the CC BY 4.0 license.

The emissions figures are computed from the energy estimate, not observed directly. The geographic breakdown assembled for the renewable share — sequencing and batch-publishing servers, challenger and full nodes, and the slice of Ethereum's validator population attributed to settlement — is carried over, and each country's portion of estimated electricity is multiplied by that country's average grid carbon intensity. The intensity figures come 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 released under a Creative Commons BY 4.0 license. Country results are summed into a network total, from which a share is attributed to an individual asset in line with observed on-chain activity.

Scope 1 and scope 2 describe different things and are reported separately. Scope 1 is direct combustion under the operators' own control — fuel burned on site, standby generation. For infrastructure hosted in commercial data centers there is normally nothing material to report here, and it is stated as zero or negligible rather than estimated upward. Scope 2 carries the weight: it is the indirect emissions embodied in purchased electricity. The calculation is location-based, applying the average intensity of the grid serving each region, because a market-based calculation would need supplier contracts and certificates that are not observable from outside. Embodied emissions from manufacturing servers or constructing the facilities they occupy are not in scope.

Greenhouse gas intensity is reported as the marginal emissions of one additional transaction, consistent with how energy intensity is treated. The main uncertainties should be read alongside the number. National annual averages smooth away the hourly swings and regional differences a real facility experiences. Every assumption in the underlying energy and location estimates propagates through to the emissions figure. And where a choice between plausible assumptions has to be made, the one producing the higher result is preferred, so these figures are better understood as a conservative ceiling than as a precise measurement.

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.