Sweat Economy (SWEAT) sustainability report

Sweat Economy is present on the following networks: Aptos Coin, Arbitrum, Avalanche, Base, Binance Smart Chain, Celo, Ethereum, Near Protocol, Optimism, Polygon, Solana, Sui.

Aptos combines proof of stake with a Byzantine fault tolerant agreement protocol in the lineage of pipelined, leader-based designs. Validators hold voting power in proportion to the stake bonded to them, and a committee is fixed for the duration of an epoch, which lasts two hours; changes to the set take effect only at those boundaries. Within an epoch, a leader is chosen for each round to propose the next block, and the choice is weighted not only by stake but by recent performance, so an operator that repeatedly fails to get its proposals committed is proposed less often. Agreement is deterministic: once a quorum of voting power certifies a block, it is final and cannot be reorganized, in contrast to systems where confidence accumulates probabilistically.

Transaction data is disseminated separately from ordering. Validators batch incoming transactions and circulate them ahead of time, collecting proofs of availability, so a proposal need only reference batches that the rest of the committee already holds rather than carry the payload itself. This separation is what allows proposals to stay small and the agreement path to stay short. Successive protocol revisions have compressed that path further, cutting the number of network round trips needed to commit and allowing a leader to propose once per network delay instead of waiting out two, which has brought block intervals down to a few tens of milliseconds and finality comfortably below a second.

Execution is parallel and optimistic. Rather than requiring developers to declare in advance which state a transaction will touch, the engine speculatively executes transactions concurrently across processor cores, detects at runtime where one transaction read state another wrote, and re-executes only the affected transactions. Because the serial order fixed by agreement is used as the reference, the outcome is identical to sequential execution and every validator derives the same result. The network tolerates faulty or malicious behavior by up to one third of voting power.

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.

Celo has not operated as a standalone Layer 1 since March 2025, when a governance-approved hard fork converted it into a Layer 2 network that settles on Ethereum. The current design is built on the OP Stack. Transaction ordering is the job of a sequencer, which assembles blocks at roughly one-second intervals and runs an execution environment equivalent to the Ethereum Virtual Machine, so contracts and tooling written for Ethereum behave identically here. One element of the former Layer 1 survives as a precompiled contract: the network's native asset can be moved either as the gas asset or through a standard fungible-token interface, without a wrapper.

Agreement on the canonical chain therefore no longer comes from a local committee of block-producing validators voting among themselves. Batched transaction data is published to an external data availability service, and only a short commitment to that data is recorded on Ethereum. Because the data itself lives off the settlement chain, the design is generally classified as an optimium rather than a full rollup, and it carries the extra assumption that the availability service will serve the data when it is asked for. State roots are proposed to contracts on Ethereum and become final only after a challenge window of about three and a half days, during which a permissioned group of challengers may dispute a proposal. A disputed proposal is resolved by generating a succinct cryptographic proof of the contested execution rather than by an interactive bisection game. A user who believes the sequencer is censoring them can force a transaction in through the Ethereum contracts, subject to a delay.

The elected operator set that once produced blocks still exists, but its function has changed: those operators now run public remote-procedure-call infrastructure serving reads and transaction submission, and election to the set remains an on-chain, stake-weighted process resolved once per epoch. Safety for funds leaving the network rests on Ethereum and the dispute process; ordering and liveness rest on a single sequencer; retrievability of transaction data rests on the external availability layer.

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.

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

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

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

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

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

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

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

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

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

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

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

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.

Sui runs a delegated proof-of-stake network whose validator committee is fixed for an epoch of roughly one day, with voting power proportional to the stake bonded to each member. Agreement uses a directed acyclic graph rather than a single chain of proposals: validators produce blocks each round that reference blocks from the previous round, and the resulting structure is read directly by every validator to work out which blocks are committed and in what order. Because the commit rule is evaluated over the graph itself, no separate round of explicit certification is needed, which removes network round trips from the critical path and brings commit latency down to a few hundred milliseconds. A more recent revision folded transaction validation into the same process and routes each submitted transaction through a single coordinating validator, cutting duplicated signature work.

The distinctive part of the design is that not every transaction has to pass through that machinery. State is modeled as discrete objects, each either owned by a single address, shared, or immutable, and a transaction declares the objects it will read and write before it executes. A transaction touching only objects owned by its sender cannot conflict with anything another party might submit, so it does not need a global ordering: a quorum of stake signing it by reliable broadcast is enough to settle it, and it finalizes on this fast path in roughly the time of two network round trips. Transactions that touch shared objects, which is what most decentralized finance activity involves, do require the graph-based protocol to sequence competing accesses, and carry slightly higher latency and cost as a result, rising further when many transactions contend for the same popular object.

Execution takes advantage of the same declarations: transactions whose object sets do not overlap run concurrently. The protocol remains safe provided faulty or malicious validators hold less than a third of voting power.

Sweat Economy is present on the following networks: Aptos Coin, Arbitrum, Avalanche, Base, Binance Smart Chain, Celo, Ethereum, Near Protocol, Optimism, Polygon, Solana, Sui.

Validators are paid from newly issued units of the native asset at a rate set by on-chain governance, and the payment is conditioned on output rather than mere presence. The reward for an epoch scales with the stake bonded to a validator multiplied by the proportion of its block proposals that were actually committed, so an operator that is offline, slow, or frequently skipped earns proportionally less. Rewards are added to the staked balance at each epoch boundary and compound from there. Holders who do not run hardware delegate into a validator's staking pool and receive their share after the operator's commission; stake locks and unlocks on epoch boundaries rather than on demand.

Penalties are exclusionary rather than confiscatory. The protocol does not currently implement slashing: there is no mechanism that destroys a validator's bonded stake for double-signing or for prolonged inactivity. What an underperforming operator loses is reward, through the proposal-success term, and eventually its place, since a validator whose stake falls below the minimum required to remain in the active set is moved to inactive status at an epoch transition, and governance can remove a validator directly. Delegators exert the remaining pressure by moving stake elsewhere.

Users pay for transactions in gas units priced in a small subunit of the native asset. The charge separates two things. Execution and input-output gas covers computation and propagation, and its unit price floats with congestion, which is also how transactions are prioritized: validators select from the pending pool by offered gas unit price, banded into discrete tiers rather than compared continuously, so raising a bid within a tier changes nothing. Storage is billed separately and at a price fixed in the native asset rather than in gas units, so the cost of occupying state does not swing with congestion, and the charge is refunded when the storage is released, meaning a transaction that frees more state than it creates can settle as a net credit. Under the fee parameters governance currently sets, collected gas is burned in full rather than routed to the proposing validator.

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.

Fees on this network are unusual in one respect: gas need not be paid in the native asset. A dedicated transaction type lets an account settle fees in any currency an on-chain allowlist admits, which in practice means several dollar-denominated stablecoins, so an account can transact holding none of the gas asset at all. The pricing itself follows the mechanism Ethereum introduced with EIP-1559. Each transaction carries an algorithmically determined base fee that rises and falls with demand for block space and an optional priority fee paid for earlier inclusion, with a floor under the base fee to deter spam and uncontrolled state growth. Because transaction data goes to a low-cost external availability layer rather than onto the settlement chain, the surcharge many Layer 2 networks pass on for settlement data is configured at zero here, and users are not billed separately for it. Smart contract execution is metered in gas and priced by the same components; there is no recurring rent on stored state.

Fee revenue accrues to the sequencer, and a protocol contract routes a configured share onward, destroying part of it and allocating the rest to beneficiaries that governance nominates, among them a fund that purchases carbon offsets. The allocation is a governance parameter rather than a fixed constant.

A separate reward stream runs each epoch, an epoch being a period of at least a day whose processing is triggered by an on-chain call rather than emitted by a special block. A treasury contract releases the native asset to four groups: the operators running public endpoint infrastructure, the holders whose locked stake voted for the elected groups, a community fund, and the carbon offsetting fund. An operator's share is scaled by a score reflecting the service it actually provides, and the group it belongs to takes a commission before passing the remainder to its voters. Penalties changed with the architecture: the automatic slashers for downtime and double-signing were retired along with local block production, while a governance-controlled slashing path remains, and an operator that stops serving without deregistering sees its score, and eventually its rewards and those of its voters, fall to nothing.

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.

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

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

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

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

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

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

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

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

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

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

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

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.

Stake is bonded by wrapping the native asset in a stake object delegated to a validator's pool; the holder keeps custody of that object and the pool's exchange rate appreciates as rewards accrue, so redeeming it later returns principal plus accumulated reward net of the validator's commission. Rewards are settled at epoch boundaries and come from the computation fees collected during the epoch together with issuance intended to support the validator set in the network's early years. Stake counts toward rewards only for epochs it was active throughout.

Penalties bite on reward, not on principal. Validators grade each other over the epoch, and if holders of more than two thirds of voting power report a given validator for poor operation, that validator's rewards for the epoch are reduced or removed entirely, and the holders who delegated to it forfeit their reward for that epoch as well. The bonded principal itself is not destroyed. A validator whose stake falls below the threshold for committee membership simply leaves the committee. The same reward multiplier enforces the fee market: at the start of each epoch validators submit the lowest price at which they will process transactions, the reference price for the epoch is the stake-weighted two-thirds percentile of those quotes, and validators that quote a low price and then honor it are rewarded relative to those that do not.

Users pay two components. Computation is metered in units placed into coarse buckets, so similar transactions cost the same and developers are not pushed into micro-optimization, and the bucket is priced at the epoch's reference rate. Storage is charged per byte at a price fixed by governance rather than set by congestion, and the amount paid is routed into a storage fund instead of to the validators of the day. Deleting data returns almost all of the storage charge as a rebate, so a transaction that frees more state than it occupies can settle at a net credit. The storage fund is the unusual element: it is counted alongside user stake when rewards are computed, most of the return it earns is paid to whichever validators are currently storing historical data, the remainder is reinvested, and its principal is never paid out, so it cannot be drained by the validators it compensates.

Sweat Economy is present on the following networks: Aptos Coin, Arbitrum, Avalanche, Base, Binance Smart Chain, Celo, Ethereum, Near Protocol, Optimism, Polygon, Solana, Sui.

Consumption is built up from the population of machines that operate the network. The first input is a count of those machines, split into the validators that take part in agreement, the fullnodes that operators run beside them to distribute state, and the public fullnodes that serve reads to applications. One part of this population is unusually well observed: the active validator set is recorded on chain and reconstituted at every epoch transition, so its size is known rather than estimated. The wider fullnode population is not, and is inferred from network crawlers and publicly reachable peer information.

Each class of machine is matched to a hardware profile taken from the resources the node software is documented to require. Because the execution engine is built to occupy many processor cores at once, validator profiles assume multi-core server equipment with substantial memory and fast persistent storage rather than a modest configuration. Power draw for a profile comes from bench measurement of equivalent hardware, recorded both under load and at rest, with idle draw included because a node is expected to stay available continuously. Aggregating draw across the estimated population over the hours in the reporting period yields consumption for the network. Where a portion of that total is attributed to an individual asset issued on the network, the share is based on that asset's observed proportion of on-chain transfer activity.

Several qualifications apply. The fullnode population is inferred rather than counted; hardware is deduced from documented requirements rather than reported by the operators themselves; and facility overheads for cooling and power conversion are approximated using typical data-center factors instead of being metered at each site. Where the available evidence leaves a question open, the assumption taken is the one that tends to produce the higher consumption figure, so the disclosure is not made flattering by accident. Estimates are revised as observation improves and after protocol changes that alter the work a node has to do.

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 energy figure for this network is assembled from the layers its operation actually spans, because it no longer runs a consensus of its own. The first layer is the infrastructure the network operates directly: the sequencer that orders and executes transactions, the software that batches transaction data and submits it to the external availability service, the component that proposes state roots to the settlement chain, and the population of full nodes and public endpoint servers that serve reads and relay user transactions. The size of that population is estimated from public network information, from crawlers that walk the peer-to-peer layer, and from the operator set recorded on chain. A representative machine specification is inferred from the published requirements for running the client software, and a power draw is attached to that specification from laboratory measurement of comparable equipment, counting idle consumption as well as consumption under load.

The second layer is the share of Ethereum's consumption attributable to what this network posts there. Ethereum is the settlement chain, so the commitments, state-root proposals and any dispute traffic submitted to it occupy a slice of that chain's validator capacity, and that slice is apportioned according to the footprint those submissions take up. Because the bulk of transaction data is sent to a separate availability layer instead, the operator set of that layer is treated as a third component and estimated on the same per-node basis. Dispute resolution depends on generating succinct cryptographic proofs, and proof generation is itself a compute cost, so it is counted where it arises rather than assumed away.

The limits of this are worth stating plainly. Node counts and hardware mixes are inferences drawn from observable network behavior and from stated software requirements, not metered readings taken from the machines. Where evidence is thin, the assumption chosen is the one more likely to overstate consumption than understate it. Figures are revised as observation improves, and the architecture described here is itself recent, so earlier periods rest on a different operating model.

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 infrastructure that runs it, not a metered reading. It works upward from the population of machines taking part, and from what each of those machines can be expected to draw.

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

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

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

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

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

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

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

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

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

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 starts from the machines that operate the network and works upward. Its first input is the population of nodes, separated into the validators that form the committee, which is recorded on chain and therefore countable rather than guessed, and the far larger and less visible set of full nodes that replicate state, index it and serve application traffic. The size of that second group is inferred from network crawlers and publicly reachable peer information, and is the main source of uncertainty in the count.

Each group is matched to a hardware profile derived from the resources the node software is documented to need. Validators here are expected to execute transactions in parallel across cores and to maintain the object store and its indexes, so their profiles assume multi-core server hardware with generous memory and fast solid-state storage rather than modest equipment. Power draw for each profile is taken from bench measurement of comparable devices under load and at rest, and idle draw is counted, because a node is expected to remain available continuously whether or not transactions are arriving. Multiplying draw by the estimated population across the hours of the period gives consumption for the network. Where a share of that total is attributed to an individual asset issued on the network, that share is based on the asset's observed proportion of on-chain transfer activity.

The limits of the method should be read alongside the result. Node counts outside the committee rest on what is observable from the public network, and operators need not be observable; hardware is inferred from documented requirements rather than collected from operators; and the overhead of cooling and power conversion in hosting facilities is approximated from typical factors rather than metered. Where the evidence does not resolve a question, the assumption chosen is the one that tends to raise rather than lower the reported figure, and estimates are revised as observation improves and as protocol changes alter what a node must do.

Sweat Economy is present on the following networks: Aptos Coin, Arbitrum, Avalanche, Base, Binance Smart Chain, Celo, Ethereum, Near Protocol, Optimism, Polygon, Solana, Sui.

Establishing where the network's electricity comes from begins with establishing where its machines are. Node addresses observable through crawlers and public peer information are resolved to a country or region, giving partial geographic coverage; it is only partial, because operators frequently sit behind hosting providers whose announced location need not match the facility actually running the hardware. Where the spread cannot be pinned down directly, the observed distribution of a structurally similar network is used in its place, selected because its staking economics and agreement protocol place comparable demands on operators and so tend to draw them toward comparable hosting markets.

Each located node is then assigned the generation mix of the grid supplying it. Those regional mixes are taken 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 proportion by the consumption estimated to sit there produces a figure for the network overall. The figure describes the grids the infrastructure physically draws on rather than any contractual arrangement: renewable purchase agreements held by individual operators are not reflected, since they cannot be observed from outside the network.

Energy intensity is a separate quantity and is narrower than dividing total consumption by transaction count. It expresses the additional energy associated with processing one further transaction. The distinction carries weight for a network of this design, where validators run continuously at a broadly constant power level and parallel execution absorbs additional transactions using capacity that is already switched on, so the marginal figure is small while the standing consumption of the validator set is not. Both numbers move with two independent inputs: the size, role mix and location of the node population, and the grid statistics for the years covered, which are themselves restated as national energy reporting is revised.

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 for this network is derived geographically rather than measured at the socket. The first step is to locate the machines doing the work: the sequencing and proposing infrastructure, the full nodes and public endpoint servers, and the operators of the external data availability layer the network depends on. Locations are inferred from publicly observable network data, principally the addresses peers advertise and the hosting providers and regions those addresses resolve to. Coverage is never complete; operators sitting behind relays or content delivery networks cannot always be placed. Where a meaningful part of the population cannot be located directly, the geographic distribution of a network whose operator profile and hosting economics resemble this one is used as a stand-in, on the reasoning that similar incentives produce similar siting decisions.

Once a distribution of locations exists, each is matched to statistics for the electricity grid serving it, and the reported renewable share is the consumption-weighted average across those regional shares. The same procedure is applied to the portion of the settlement chain's operator base carrying this network's activity, so the figure reflects settlement as well as the network's own infrastructure. Regional generation mixes shift seasonally and from year to year, so the result moves with the underlying statistics even in periods when nothing about the network has changed.

Energy intensity carries a specific meaning here. It is the marginal energy attributable to one additional transaction, obtained by dividing estimated consumption over a reporting period by the transactions confirmed in that period. It is not a physical measurement of any individual transaction, and on infrastructure that draws power largely independently of how busy it is, the number is best read as an allocation of shared overhead: it falls as activity rises without any machine using less electricity. Regional generation data is drawn from Share of electricity generated by renewables, compiled by Our World in Data from Ember and the Energy Institute's Statistical Review of World Energy.

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

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

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

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

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

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

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

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

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

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

Those country weights are applied to published figures for the renewable proportion of each country's electricity generation, taken from Share of electricity generated by renewables, compiled by Our World in Data from Ember and the Energy Institute's Statistical Review of World Energy. What comes out is a consumption-weighted average across the inferred footprint, reflecting the grids the infrastructure most likely draws on. It does not capture procurement: renewable supply contracts, certificates and behind-the-meter generation cannot be seen in network data and are not credited.

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

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

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

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

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

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 the renewable share means first working out where the machines sit. Addresses visible through network crawlers and public peer information are resolved to a country or region, which gives incomplete coverage: many nodes sit behind hosting providers or relays, and the announced location of an address need not match the facility housing the hardware. Where the geographic spread cannot be observed directly, the distribution of a structurally similar network is substituted, chosen because its staking economics and agreement protocol impose comparable operating demands and so tend to concentrate operators in comparable hosting markets.

Located nodes are then assigned the generation mix of the grid that supplies them, using 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 proportion by the consumption estimated to sit in that region produces a share for the network as a whole. That share describes the physical grids the infrastructure draws from, not contractual sourcing: an operator holding renewable supply agreements is treated the same as any other operator on the same grid, because such arrangements are not visible from outside the network.

Energy intensity is reported as a separate quantity and means the additional energy associated with handling one further transaction, not the annual total divided by the number of transactions. The distinction is material for this network, where the committee runs continuously at a broadly constant power level and much of the traffic settles over a path that adds little incremental work, so the marginal figure is small while the standing consumption of the node population is not. Both the renewable share and the intensity respond to two separate inputs: the size, mix and placement of the node population, and the grid statistics for the years covered, which are themselves restated as national energy reporting is revised.

Sweat Economy is present on the following networks: Aptos Coin, Arbitrum, Avalanche, Base, Binance Smart Chain, Celo, Ethereum, Near Protocol, Optimism, Polygon, Solana, Sui.

The emissions calculation reuses the geographic picture built for energy sources and applies a different body of grid statistics to it. Node addresses visible through crawlers and public peer information are resolved to regions; where that resolution is incomplete, the distribution of a structurally comparable network is substituted, chosen because its incentive design and agreement protocol impose similar operating requirements and therefore a similar hosting footprint.

Each region is paired with the carbon intensity of its electricity, drawn 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 published under the CC BY 4.0 licence. Multiplying the electricity estimated to be drawn in each region by that region's intensity, then summing across regions, gives the emissions attributable to operating the network over the period.

Two scopes are distinguished. Scope 1 captures emissions from sources under the direct control of those running the infrastructure, such as fuel combusted on site. For a network whose nodes are servers in third-party facilities, this is normally nil or immaterial, and a zero entry records the absence of such sources rather than a gap in the data. Scope 2 captures the indirect emissions embodied in the electricity those machines buy, and accounts for effectively the entire footprint reported here.

Greenhouse gas intensity mirrors its energy equivalent in being marginal rather than average: it is the emission associated with one additional transaction, not an annual total divided by throughput. Because validators consume power at a fairly steady rate irrespective of how full their blocks are, that marginal figure stays small and should not be read as a per-transaction apportionment of the network's whole footprint. Both the absolute emissions and the intensity are sensitive to the underlying grid data, which is updated as national energy statistics are revised, and to any protocol change that alters the hardware a validator needs.

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 same geographic picture used for the energy figures, with carbon intensity substituted for renewable share. Machine locations are inferred from publicly observable network data covering the sequencing and proposing infrastructure, the full node and public endpoint population, the operators of the external data availability layer, and the portion of the settlement chain's operator base that carries this network's traffic. Where part of that population cannot be placed directly, the distribution of a structurally comparable network is used in its stead, and the resulting uncertainty is carried into the estimate rather than hidden in it.

Each location is then matched to the carbon intensity of the electricity supplied by the grid serving it, expressed in grams of carbon dioxide equivalent per kilowatt-hour, and the electricity estimated for that location is multiplied by the corresponding factor. Summing across locations produces the total for the network.

What results is overwhelmingly a scope 2 figure: the indirect emissions embodied in electricity purchased from a grid. Scope 1 covers emissions from sources the operators control directly, such as fuel burned on site; for infrastructure of this kind, which is general-purpose server hardware drawing grid power in commercial data centers, scope 1 is normally negligible and is reported as such unless something specific indicates otherwise. Emissions embodied in manufacturing, shipping and disposing of that hardware fall outside this boundary and are not included.

Greenhouse gas intensity is the marginal emission attributable to one additional transaction, obtained by allocating the total across the transactions confirmed in the same period. Like its energy counterpart it is an allocation of shared overhead rather than a property of any single transaction, and it moves when grid factors move or when the composition and location of the infrastructure change, not only when network behavior does. Grid carbon intensity values come from Carbon intensity of electricity generation, compiled by Our World in Data from Ember and the Energy Institute's Statistical Review of World Energy and made available under the Creative Commons Attribution 4.0 license.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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 by taking the geographic picture assembled for energy sourcing and applying carbon statistics to it instead of generation-mix statistics. Node addresses observable through crawlers and public peer information are resolved to regions, and where that resolution is incomplete the distribution of a structurally comparable network stands in, chosen because its incentive structure and agreement protocol create similar operating requirements and therefore a similar hosting pattern.

Every region is then paired with the carbon intensity of the electricity generated there, 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 published under the CC BY 4.0 licence. The electricity estimated to be consumed in each region is multiplied by that region's intensity and the products are summed to give the emissions attributable to operating the network over the reporting period.

The disclosure keeps two scopes apart. Scope 1 covers emissions from sources directly controlled by those running the infrastructure, for instance fuel burned on site. For a network whose nodes are servers in third-party data centers this is normally nil or immaterial, and a zero figure records the absence of such sources rather than missing data. Scope 2 covers the indirect emissions carried in the electricity those machines purchase, and accounts for substantially the whole footprint reported for this network.

Greenhouse gas intensity is marginal in the same sense as its energy counterpart: it is the emission associated with one additional transaction rather than an average produced by dividing an annual total by throughput. Because the committee draws power at a fairly steady rate regardless of how busy the network is, that marginal figure stays small and should not be treated as each transaction's share of the network's overall footprint. Both the absolute emissions and the intensity shift with the grid data underneath them, which is revised as national energy statistics are updated, and with any protocol change that alters the hardware a node requires.