WOO (WOO) sustainability report

WOO is present on the following networks: Arbitrum, Avalanche, Base, Binance Smart Chain, Ethereum, Fantom, Linea, Near Protocol, Polygon, Solana, Zksync.

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

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

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

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.

BNB Smart Chain, the programmable chain of BNB Chain and formerly styled Binance Smart Chain, reaches agreement through Proof of Staked Authority, a design that borrows stake-weighted election from delegated proof of stake and rotating, permissioned block production from proof of authority. Bonded stake decides who may produce blocks rather than who wins any individual slot. The network keeps an active set of forty-five operators, ranked by the amount of the native asset bonded to them through self-delegation and through delegation from holders. The twenty-one highest-ranked form the cabinet tier and the next twenty-four are candidates, with everyone below inactive and producing nothing. Rankings are recomputed once a day, so membership of the set turns over on a daily cycle rather than per block.

Within each epoch a consensus group of twenty-one is drawn from the active set, weighted heavily toward the cabinet tier, and those operators take turns proposing in a fixed rotation. Turn length and epoch length are protocol parameters that have been retuned repeatedly as block intervals shortened: successive upgrades cut the interval from three seconds to 1.5, then to 0.75 in mid-2025, and to 0.45 seconds in January 2026. A separate voting layer sits above the rotation, in which validators sign attestations on recent blocks; once enough signatures accumulate a block is treated as final, giving deterministic finality in roughly a second. Should that voting layer stall, the chain falls back to confirmation by accumulated depth, which takes minutes rather than seconds.

Security rests on an honest supermajority of a deliberately small elected set, backed by on-chain penalty logic. A slashing contract watches for double signing, for contradictory attestations in the fast-finality vote, and for repeated failure to produce during an assigned turn. Consequences range from temporary jailing and lost rewards through to removal from the set and forfeiture of part of a validator's own bonded stake. The trade-off is deliberate: a compact, frequently re-elected validator set buys very short block intervals and cheap execution, at the cost of the broader operator base that larger validator sets provide.

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.

Linea is a Layer 2 network that executes transactions away from the Ethereum chain and then proves their correctness to it. It has no consensus protocol of its own in the sense a base layer does, and no independent validator set standing behind user funds. What settles the question of what is true on Linea is a cryptographic proof, checked by a contract on Ethereum, showing that the state transition the network claims is precisely what its rules produce from the data it has published.

Three components do the work. A sequencer receives transactions, orders them and produces Layer 2 blocks, which gives users an immediate result. A coordinator drives the pipeline that turns those blocks into batches, requests proofs for them and submits the outcome to Ethereum. A prover generates the succinct zero-knowledge proofs themselves, and that is by a wide margin the most computationally demanding part of the system. During 2026 block production inside the sequencer was moved onto a Byzantine-fault-tolerant protocol of the kind used in permissioned enterprise networks, replacing an earlier proof-of-authority arrangement. The change is groundwork for spreading sequencing across several independent operators, but for now a single operator, the network's originator, produces every block.

Because the proof establishes validity mathematically, there is no dispute window and no requirement that a challenger be watching. Once the contract on Ethereum accepts a proof, the state it attests to is settled, subject only to Ethereum finalizing the block that contains the verification. That is the substantive difference from optimistic designs, where commitments are presumed correct and may be contested for a period afterwards. The network also publishes the full transaction data for each batch to Ethereum rather than only the differences in state, so anyone can rebuild the Layer 2 chain from settlement-layer data alone.

Execution aims to be indistinguishable from Ethereum's, and the remaining differences in gas accounting and state representation have been narrowed successively. Operationally the network remains centralized: sequencer and prover are each run by one party, contract upgrades are not yet constrained by a long user exit window, and independent assessments place it at the earliest tier of the common rollup maturity scale, with staking-based permissionless sequencing described as a later goal.

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.

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

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

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

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

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

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

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

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

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

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

WOO is present on the following networks: Arbitrum, Avalanche, Base, Binance Smart Chain, Ethereum, Fantom, Linea, Near Protocol, Polygon, Solana, Zksync.

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

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

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

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.

BNB Smart Chain pays for its own security out of transaction fees rather than out of new issuance. The native asset carries no protocol-level block subsidy, so every reward reaching a validator or a delegator originates in gas paid by users. When a block is finalized the proposer's collected fees are routed into system contracts and split three ways. A governed fraction is sent to an unspendable address and permanently removed from supply, a slice accumulates in a reward vault used for network-wide purposes such as paying for fast-finality attestations, and the balance sits in the validator-set contract until it is distributed, on a daily cycle, to active validators and the holders who delegated to them.

Participation is staking-based. An operator must self-delegate a substantial amount of the native asset before it can be considered for the active set, and holders may bond additional stake to any validator to lift its ranking. Delegators receive their proportional share of whatever the validator earns, after the commission that validator sets for itself, and only the forty-five ranked operators earn at all: stake bonded to an inactive validator yields nothing. Unbonding is subject to a waiting period, so stake cannot be pulled out the instant misbehavior comes to light.

Penalties are graduated. Missing assigned turns or going offline for a sustained stretch triggers jailing, during which the validator produces nothing and earns nothing. Double signing and contradictory attestations in the finality vote are treated far more severely and can cost the validator a portion of its own bonded stake alongside ejection from the set.

Users face a conventional gas-metered fee model inherited from the Ethereum virtual machine. Each operation carries a gas cost, the sender chooses a gas price, and the total is charged in the native asset. There is no separate storage rent, so the cost of persisting state is bundled into execution gas, and deploying or calling a contract is priced purely by the computation and storage it consumes. The minimum acceptable gas price is a coordinated parameter that operators and infrastructure providers have revised downward several times, keeping ordinary transfers and contract calls inexpensive in absolute terms.

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.

Fees on Linea are paid in ether, the asset used on the settlement layer; no separate asset needs to be held in order to transact. The network runs no staking system, issues no rewards to a validator set and has no delegation inside its protocol, because it has no permissionless set of block producers to pay.

A user's fee has two parts in substance even where it is quoted as a single number. The first covers executing the transaction on the Layer 2, metered in gas on the same schedule Ethereum uses and priced by an algorithmic base fee plus a tip. The second covers what the network must spend on Ethereum, which for a validity rollup is of two kinds: publishing the batch's compressed transaction data, and paying for the on-chain verification of the proof that covers it. Verification is a fixed cost per submission and is spread across every transaction in the batch, so the fuller the batch the less each transaction bears. Data publication has used Ethereum's dedicated data market since 2024, priced separately from ordinary execution and destroyed rather than paid out, and compression improvements on the Layer 2 side reduce how much of that space a batch needs.

Fee revenue first pays the cost of running the sequencer, coordinator and prover and of settling to Ethereum. What remains after those costs is destroyed rather than kept: approximately one fifth as ether, which removes supply on the settlement layer, and the remainder used to acquire and destroy the network's own asset on Ethereum. This arrangement has been in force since late 2025 and ties the economics of both assets to how heavily the network is actually used rather than to a fixed schedule.

Penalties in the usual sense do not exist here, because no participant posts a bond that misbehavior could forfeit. Correctness is not encouraged economically but enforced cryptographically: an operator cannot finalize an invalid state, because no proof of one can be produced. What that leaves exposed is liveness and transaction ordering rather than validity, and it is those risks that the move toward multiple independent sequencers and provers is meant to address.

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.

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

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

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

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

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

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

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

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

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

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

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

WOO is present on the following networks: Arbitrum, Avalanche, Base, Binance Smart Chain, Ethereum, Fantom, Linea, Near Protocol, Polygon, Solana, Zksync.

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

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

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

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 energy figure for BNB Smart Chain is built upward from the node population rather than downward from operator revenue, which is the appropriate treatment for a staked network where block production is not a computational race. Nothing about the fee model or the value of the native asset determines how much hardware is deployed: the size of the validator set is fixed by protocol, and the wider population of non-validating nodes is driven by demand for chain access.

The estimate has three inputs. The first is the number of machines. The elected validator set is known from the chain itself, while the surrounding population of full and archive nodes is approximated from peer-discovery crawls, public node listings and network scans, all of which observe only nodes willing to accept inbound connections and therefore tend toward undercounting. The second input is a representative hardware profile per node, inferred from the client software's published requirements, which on this chain are demanding relative to slower networks of the same family, since sub-second block intervals and rapid state growth push operators toward high core counts, large memory and fast solid-state storage. The third is the electrical draw of such a machine, taken from measurement of comparable configurations on the bench, both under sustained load and at idle, because a validator idles between its assigned turns and that baseline draw is a real part of the total. Aggregating the per-machine figure across the estimated population, with an allowance for the overhead of the facilities housing it, gives the network total.

Several qualifications belong with the result. It is a modeled estimate resting on observed node counts and stated software requirements, not metered consumption at the socket. Where evidence is thin, the assumptions chosen lean toward overstating rather than understating consumption. Figures are revised as crawler coverage and hardware information improve. Finally, apportioning a share of the network total to any single asset issued on the chain is done from observed on-chain transfer volumes, which measures how heavily an asset is used rather than the energy it uniquely causes.

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 for this network is assembled from two components: the machines the network runs itself, and the share of the settlement layer's consumption that its activity causes.

The first component is unusual among Layer 2 designs because proving dominates it. The sequencer, the coordinator and the replica and archive nodes that third parties operate are conventional server workloads, sized from the published requirements of the node software and assigned power figures measured on representative equipment under controlled laboratory conditions, with idle draw counted alongside load. The prover is a different matter. Producing a succinct proof that a whole batch of transactions executed correctly is an intensive computation, run on large multi-core machines and accelerators, and it is performed for every batch the network settles rather than only when something is contested. That makes proving a recurring, throughput-linked energy cost with no counterpart in an optimistic design, and it is modeled as a distinct workload whose draw scales with the volume of transactions proved rather than with elapsed time alone. Efficiency gains in the proving software reduce this component directly, which is one reason the figure moves between reporting periods.

The second component is the settlement layer, which is Ethereum. Its consumption is estimated from its validator population using the node-level method described for that network, and a share is attributed here in proportion to what this network occupies there: the data space its batches consume and the gas its verification and settlement contracts use.

All of this rests on estimation rather than metering. The number and specification of proving machines are inferred from the operator's published architecture and from what the proving workload demands, not read from a meter, and the replica node population is observed through crawlers and public listings and is necessarily incomplete. Where the evidence does not settle a question, the assumption chosen is the one that raises the estimate rather than lowers it, so the result is more likely to overstate than understate, and it is corrected as observation improves. The settlement layer'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 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.

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

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

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

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

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

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

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

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

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

WOO is present on the following networks: Arbitrum, Avalanche, Base, Binance Smart Chain, Ethereum, Fantom, Linea, Near Protocol, Polygon, Solana, Zksync.

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

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

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

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

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 for BNB Smart Chain follows from where its machines physically run, so the method begins with locating them. Node addresses visible through peer discovery and public network observation are resolved to hosting providers, autonomous systems and countries, producing an approximate geographic distribution of the validator and full-node population. Where that observation is too sparse to stand on its own, the distribution of a network with a comparable staking design and operator economics is substituted, on the reasoning that similar incentives attract similar operators into similar hosting markets.

That distribution is then matched against national electricity statistics. Each country's share of generation coming 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 those country-level shares by the portion of estimated node capacity sitting in each gives a single renewable percentage for the network.

Energy intensity is a separate quantity and is defined marginally: the additional electricity associated with one further transaction being processed, rather than the annual total divided by the transaction count. On a chain that produces blocks on a fixed schedule whether or not they are full, the marginal figure is far smaller than a simple average would suggest, and the two should not be used interchangeably.

Three limits are worth stating plainly. An observed hosting location identifies a grid but not a procurement arrangement, so an operator buying renewable power on a carbon-heavy grid is indistinguishable from one that is not. Cloud and proxy infrastructure can place a node's apparent location away from the hardware actually running it. And national annual averages smooth over the hourly and seasonal variation in generation mix that a continuously running machine actually draws from.

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.

Most of the electricity behind this network is drawn by proof generation, and proof generation does not happen everywhere. It runs on clusters of high-core-count machines and accelerators in a handful of sites, which gives the geographic weighting an unusual shape: a small number of grids carry most of the weight, and the renewable figure shifts noticeably if the proving fleet is enlarged, retired or moved to a different hosting region. A network of many small scattered nodes averages such changes away. This one does not.

Establishing locations therefore starts with those sites. Hosting regions chosen for the proving fleet, and for the sequencing and coordination services running alongside it, are visible in the operator's published technical material and in the routing of the endpoints it exposes. Independent replica and archive operators form a second, dimmer tier, counted through peer crawling and through directories of infrastructure providers, with a country attached to each by looking up which allocation an announced address falls under. That lookup misfires often enough on any single machine that only the aggregate shape is trusted. A third tier lies outside the network altogether, since part of what is reported here is a slice of the settlement layer, whose validators occupy a far wider spread of grids; that spread is characterized separately and blended in at the weight the slice carries. When a tier resists observation, a stand-in profile is borrowed from a network whose operators choose hosting on comparable commercial grounds.

Generation statistics do the rest. Every country in the weighted distribution carries a published figure for the fraction of its electricity produced from renewable sources, and the network's renewable share is that fraction averaged over the consumption weights. It is a statement about grids rather than about procurement: a proving site buying wind power directly and a neighboring rack on default tariff are indistinguishable under this treatment, and an annual national series cannot describe the hour at which a load actually fell.

Energy intensity means the additional electricity one further transaction brings, with the deployed hardware held as it is. Here that quantity is not close to nothing, because every extra transaction enlarges the work a prover must perform. Generation figures come from Share of electricity generated by renewables, a series Our World in Data maintains from Ember's electricity data and from the Energy Institute's Statistical Review of World Energy.

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.

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

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

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

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

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

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

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

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

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

Those country weights are applied to published figures for the renewable proportion of national electricity generation, taken from Share of electricity generated by renewables, compiled by Our World in Data from Ember and the Energy Institute's Statistical Review of World Energy. The output is a consumption-weighted average across the inferred footprint. Procurement is outside what this can see: renewable supply contracts, certificates and on-site generation leave no trace in network data and are not credited.

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

WOO is present on the following networks: Arbitrum, Avalanche, Base, Binance Smart Chain, Ethereum, Fantom, Linea, Near Protocol, Polygon, Solana, Zksync.

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

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

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

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 for BNB Smart Chain are derived from the same geographic picture used for the energy mix, then converted using regional carbon factors. Node locations are approximated from peer-discovery data, public network observation and hosting attribution, and where coverage is insufficient the distribution of a structurally similar staked network stands in. Each location carries the carbon intensity of its national grid, drawn 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. Multiplying the electricity attributed to each region by that region's grams of carbon dioxide equivalent per kilowatt-hour, and summing across regions, gives the annual emissions figure.

The split between scopes matters for interpretation. Scope 1 covers emissions from sources the network's operators control directly, such as on-site fuel combustion, which for a population of general-purpose servers in rented facility space is generally negligible and is reported as such. Scope 2 covers the indirect emissions embodied in the electricity those machines purchase from the grid, and that is where effectively the whole footprint sits. Emissions upstream of operation, in the manufacture and eventual disposal of the hardware, fall outside this accounting boundary.

Greenhouse-gas intensity mirrors the energy definition: the incremental emissions associated with one additional transaction, not the annual total divided by throughput.

Uncertainty in the emissions figure compounds the uncertainty in the two inputs behind it. Any error in the estimated electricity total propagates directly into the result, and the geographic attribution adds error of its own, since grid carbon intensity varies by more than an order of magnitude between countries and a misplaced share of node capacity moves the answer substantially. Annual national averages also mask the hourly variation in grid intensity to which a machine running around the clock is fully exposed.

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.

Where the boundary falls is not obvious here. Two pools of electricity are in scope: the machines the network runs for itself, chiefly the proving cluster together with sequencing, coordination and the replica and archive servers third parties keep; and a slice of the settlement layer, sized by how much of Ethereum's capacity the posted batches and proof verifications occupy. Fabricating the accelerators and constructing the halls that house them lie outside.

Within the boundary nothing is metered for carbon. A coefficient is applied instead: for each country in which consumption has been placed, published statistics give the average greenhouse gas released per unit of electricity generated there, expressed in carbon dioxide equivalent. Estimated electricity is multiplied through and the products added. What makes the calculation distinctive is how lopsided the weights are. Proving concentrates the bulk of the draw into a few hosting sites, so the coefficient of one or two grids governs the answer, and a decision to prove somewhere else would move reported emissions further than most protocol changes could. Those sites are placed from the operator's public technical material; replica nodes by resolving announced addresses to countries, with a borrowed profile from a commercially comparable network covering whatever will not resolve. Settlement-layer validators are placed on their own terms and folded in at the weight their slice carries.

Scope 1 would capture combustion under the operators' own control, an on-site generator being the plain case. In leased facilities full of general-purpose servers there is normally none, and the zero that appears is a finding rather than a gap. Scope 2 is the indirect burden riding on purchased electricity, and effectively the whole figure sits there. National average coefficients mean a site on a specific low-carbon contract scores no differently from a neighbor on default supply.

Greenhouse gas intensity per transaction is marginal rather than averaged: what one additional transaction adds, with the installed fleet unchanged. On this network that increment is real rather than nominal, since one more transaction is one more transaction to prove. It inherits the uncertainty of both the consumption estimate and the grid coefficients. Those coefficients are taken from Carbon intensity of electricity generation, a series Our World in Data builds from Ember's electricity data and from the Energy Institute's Statistical Review of World Energy and releases under the CC BY 4.0 license.

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.

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

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

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

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

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

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

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

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

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

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

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