Axie Infinity (AXS) sustainability report

Axie Infinity is present on the following networks: Binance Smart Chain, Ethereum, Harmony One, Ronin.

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

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

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

Ethereum reaches agreement through proof of stake, adopted in September 2022 when the original mining-based chain was retired in favor of a validator-driven consensus layer. The protocol family is usually referred to as Gasper. A fork-choice rule named LMD-GHOST selects the head of the chain by following the branch carrying the greatest accumulated weight of validator votes, while a separate finality gadget, Casper FFG, periodically justifies and then finalizes checkpoints, so that reversing them would require destroying an enormous quantity of bonded value.

Time is divided into slots of twelve seconds, and thirty-two slots form an epoch. For each slot the protocol pseudo-randomly designates one active validator to assemble and publish a block, and assigns the rest to committees that vote on what they believe is the correct head and the correct checkpoints. Under healthy conditions a checkpoint becomes final two epochs after it is proposed, a little under thirteen minutes, after which everything beneath it is treated as settled.

Joining the validator set requires a deposit of no fewer than 32 units of the native asset. Since the protocol upgrade of May 2025 a single validator may hold a far larger balance, up to 2,048 units, and earn on the whole of it, which lets an operator running many minimum-sized validators consolidate them into fewer; the activation floor itself did not change. Entry and exit are rate-limited by a queue measured in staked weight rather than in validator headcount, which bounds how fast the composition of the set can turn over.

Security rests on voting power being bonded. A validator that signs contradictory messages can be proved to have done so and is penalized, and the size of that penalty scales with how much other stake was penalized at the same time, so a coordinated attack is punished far more severely than an isolated fault. Should the chain stop finalizing altogether, a separate mechanism gradually erodes the balances of validators that are not participating until the remainder again represents a large enough majority to finalize. Upgrades during 2024 and 2025 changed how large data payloads are distributed and sampled between nodes, without altering this underlying agreement process.

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

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

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

Ronin began as a sidechain built to carry the transaction volume of a single gaming application that the Ethereum main chain could not absorb cheaply. It launched in 2021 under an authority-based model in which a hand-picked group produced every block, and moved in April 2023 to a delegated staking model: twenty-two slots, twelve held by institutional operators confirmed through governance and ten open to any candidate that bonded enough of the native asset and attracted enough delegated stake to rank into the set. A 2024 change rotated the block-producing subset each epoch so that every member of the set, not only the largest, took turns producing and earning. Blocks arrived every three seconds and were treated as final once two-thirds of the set had signed.

That architecture no longer runs. In May 2026, following a vote of the operator set, the network hard-forked into a Layer 2 that settles on Ethereum and is built on the OP Stack. Block production passed from the rotating set to a single sequencer operated under contract. Batched transaction data goes to an external data availability service rather than onto Ethereum, with only commitments recorded on the settlement chain, which places the design in the optimium category rather than among rollups proper. State roots are proposed to Ethereum and remain open to dispute for a challenge window of several days. The proposer and challenger roles are currently permissioned, so the correctness of settled state depends on the honesty of designated parties, with a dispute game backed by validity proofs planned to remove that dependency. A user facing censorship can force a transaction in through the Ethereum contracts.

The bridge was rebuilt after the 2022 compromise, in which an attacker obtained enough validator signing keys to authorize withdrawals directly. The signer set was widened well beyond its original size and spread across independent organizations, approval thresholds were raised to a large supermajority, and daily withdrawal ceilings and anomaly monitoring were introduced. Asset transfers were later moved onto an external cross-chain messaging protocol and the original gateway deprecated; the canonical bridge contracts deployed during the Layer 2 migration do not yet hold the network's bridged liquidity.

Axie Infinity is present on the following networks: Binance Smart Chain, Ethereum, Harmony One, Ronin.

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

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

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

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

Payment inside the protocol flows to validators, the only participants the consensus layer compensates directly. A validator earns newly issued units of the network's native asset for voting promptly and correctly on the head of the chain and on the checkpoints being justified, for serving its turn in the committee that signs headers for light clients, and, when selected to propose, for the block itself. The proposer additionally keeps the priority portion of the fees in that block, together with whatever it receives from the separate market through which many proposers outsource block assembly. There is no delegation inside the consensus rules: stake is either operated directly or entrusted to an operator through arrangements that sit outside the protocol.

Users pay for execution in gas, metered per operation, with writes to persistent state priced far above arithmetic. Every transaction carries a base fee per unit of gas that the protocol sets algorithmically from how full recent blocks have been, and that amount is destroyed rather than paid to anyone, so sustained demand withdraws native asset from circulation. On top of it a user adds a voluntary tip, which goes to the proposer and governs how quickly the transaction is picked up. Data posted on behalf of Layer 2 networks is priced in a second, independent market whose fee is likewise destroyed; a December 2025 upgrade tied the floor of that market to ordinary execution costs so it cannot collapse to a negligible level, and capped the gas any one transaction may consume.

Penalties mirror the rewards. Failing to vote, or voting late or incorrectly, costs a validator roughly what correct behavior would have earned it. Provable equivocation is treated far more harshly: the offender is scheduled for ejection, forfeits part of its balance immediately, and later incurs an additional correlated penalty computed from how much other stake was penalized nearby in time. Prolonged absence while the chain is failing to finalize drains balances until finality can resume. Stakers may take out accumulated rewards without leaving the set, and since 2025 may also trigger a full exit from the execution layer rather than only from the consensus client.

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

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

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

Fees are paid in the network's native asset, which was kept as the gas asset through the Layer 2 migration. A transaction carries an algorithmically set base fee that tracks demand for block space and an optional priority fee buying earlier inclusion. Smart contract execution is metered in gas and priced by the same components, and stored state attracts no recurring rent beyond the gas cost of writing it. Behind the fee a user sees lie two costs the network bears itself: publishing batched transaction data to the external availability service, and submitting state roots and commitments to the settlement chain. These are met from network revenue rather than itemized to the user, and sequencer revenue net of them accrues to a protocol treasury.

The incentive model changed with the migration, and it changed direction. Under the sidechain, operators earned newly issued native asset and a share of fees for producing and validating blocks, while holders who delegated to an operator received a proportional share of that operator's rewards after commission. Bonded stake was exposed to slashing for signing conflicting blocks and to lesser penalties for downtime, so the choice of operator carried real risk for a delegator. Duty on the bridge was compensated separately from a dedicated allocation.

Since block production is now a sequencer's responsibility, rewards for passively bonded stake are being wound down and the issuance that funded them redirected to the treasury. In their place the protocol pays applications rather than infrastructure. Contracts register to be measured, and rewards are allocated according to observed on-chain contribution: principally the gas an application generates, the value it holds and the user activity it brings. Governance, formerly exercised through the institutional subset of the operator set, is moving toward voting weighted by holdings of the native asset over treasury allocation and protocol decisions. Operators retain roles in governance and in administering delegated stake, but payment for simply occupying a validator slot is not the model the network is built on going forward.

Axie Infinity is present on the following networks: Binance Smart Chain, Ethereum, Harmony One, Ronin.

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

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

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

The figure reported for this network is assembled machine by machine, treating the computers that run the protocol as the thing that draws electricity. The starting point is an estimate of how many independent nodes are operating, built from crawlers that walk the peer-to-peer layer and record every peer they can reach, supplemented by public listings of infrastructure and staking providers and by the protocol's own visible record of how much stake is active and how it is spread across operators.

A representative hardware profile is then inferred for those machines. The client software publishes what it requires in processor, memory and disk terms, and operators have little reason to provision far beyond that, so the profile is derived from those stated requirements rather than from a survey of individual operators. Power draw for the resulting device classes comes from measurement on representative equipment under controlled laboratory conditions, capturing both the load validating places on a machine and the draw of a machine that is powered on but momentarily idle, which for a network of this kind accounts for a large share of the total. Multiplying measured per-device draw across the estimated population over the reporting period yields the network figure. Where a disclosure concerns one of the many assets issued on this network rather than the network itself, a portion of the network total is assigned to it in proportion to observed on-chain transfer volumes.

The limits deserve stating plainly. The node count records what is reachable, not a census, and machines behind restrictive network configurations are missed. The hardware profile is a reasoned inference from published software requirements, not a record of what any particular operator bought. Nothing here is metered at the wall. Where the evidence runs out, the assumptions chosen are those that push the estimate upward rather than downward, so the result is more likely to overstate consumption than to understate it, and it is revised as observation improves. The network's own account of its energy profile is published at Ethereum energy consumption.

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

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

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

This network settles on another chain, so its estimate has more than one part. The first is the infrastructure it runs itself: the sequencer that orders and executes transactions, the batching software that submits transaction data to the external availability service, the component that proposes state roots to the settlement chain, and the population of full nodes, archive nodes and public endpoint servers that hold state and serve applications. That population is estimated from crawlers walking the peer-to-peer layer, from public node directories, and from what the protocol records on chain. A representative machine profile is inferred from the stated requirements for running the client software, and a power figure attached to it from laboratory measurement of equivalent hardware, counting the idle floor as well as draw under load.

The second part is the share of Ethereum's consumption attributable to what this network posts there. Ethereum is the settlement chain, so commitments, state-root proposals and any dispute traffic occupy a slice of its validator capacity, apportioned by the footprint those submissions take up. The bulk of transaction data goes instead to a separate availability layer, whose operator set is treated as a third component and estimated on the same per-node basis. Where disputes are resolved by generating cryptographic proofs, that proof generation is itself a compute cost and is counted where it occurs.

One caveat is specific to this network and material. Its architecture changed during 2026, from an independent sidechain secured by its own validator set to a Layer 2 settling on Ethereum. A reporting period that spans that change necessarily blends two different models, the earlier one counting a bonded validator set producing blocks locally and the later one counting sequencing infrastructure plus an allocated share of another chain. The generic limits apply as well: node counts and hardware mixes are inferences from public observation and stated software requirements rather than metered readings, conservative assumptions are preferred where evidence is missing, and figures are revised as the picture improves.

Axie Infinity is present on the following networks: Binance Smart Chain, Ethereum, Harmony One, Ronin.

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

That distribution is then matched against national electricity statistics. Each country's share of generation coming from renewable sources is taken from Share of electricity generated by renewables, compiled and processed by Our World in Data from Ember's yearly electricity datasets and the Energy Institute's Statistical Review of World Energy. Weighting those country-level shares by the portion of estimated node capacity sitting in each gives a single renewable percentage for the network.

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

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

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

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

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

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

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

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

Each jurisdiction in the distribution is then matched to published statistics on how its electricity is generated, and the renewable proportion for the network is the share-weighted average across those jurisdictions. Those statistics are taken from Share of electricity generated by renewables, compiled by Our World in Data from Ember and the Energy Institute's Statistical Review of World Energy. The figures are annual and national, so they cannot capture an individual facility's supply arrangements or the hour of day at which load actually fell.

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

The renewable share is derived from where the network's machines are, not from meters on them. Locating them means covering several distinct populations: the sequencing and proposing infrastructure the network operates, the full nodes and public endpoint servers that hold state and serve applications, the operator set of the external data availability layer, and the portion of the settlement chain's validator base carrying this network's activity. Locations are inferred from publicly observable network data, chiefly the addresses peers advertise and the hosting providers and regions to which those addresses resolve. Coverage is partial by nature; machines behind relays or content delivery networks cannot be placed.

Where a significant share of a population cannot be located directly, the geographic distribution of a network with a comparable operator profile and comparable hosting economics is used as a proxy, on the reasoning that similar incentives produce similar siting. The migration from an independent chain to a Layer 2 shifted the weighting between these populations considerably, moving a substantial part of the footprint from a locally operated validator set onto shared settlement infrastructure whose distribution is observed separately.

Each location is then matched to statistics for the electricity grid serving it, and the reported renewable share is the consumption-weighted average across those regional shares. Regional mixes move seasonally and year to year, so the figure shifts with the underlying statistics even when nothing about the network changes.

Energy intensity means something specific here: 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 an allocation of shared overhead, not a physical property of a single transaction, and on a network whose infrastructure draws power largely independently of load it falls as activity rises without any machine using less electricity. Regional generation data is 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.

Axie Infinity is present on the following networks: Binance Smart Chain, Ethereum, Harmony One, Ronin.

Emissions for BNB Smart Chain are derived from the same geographic picture used for the energy mix, then converted using regional carbon factors. Node locations are approximated from peer-discovery data, public network observation and hosting attribution, and where coverage is insufficient the distribution of a structurally similar staked network stands in. Each location carries the carbon intensity of its national grid, drawn from Carbon intensity of electricity generation, processed by Our World in Data from Ember's yearly electricity data and the Energy Institute's Statistical Review of World Energy and published under a CC BY 4.0 license. Multiplying the electricity attributed to each region by that region's grams of carbon dioxide equivalent per kilowatt-hour, and summing across regions, gives the annual emissions figure.

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

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

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

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

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

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

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

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

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

Carbon intensity figures come from Carbon intensity of electricity generation, compiled by Our World in Data from Ember and the Energy Institute's Statistical Review of World Energy and published under the Creative Commons Attribution 4.0 license. These are annual national averages, so they smooth over the hourly and sub-national variation that in reality determines what a given machine's electricity caused.

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

Emissions are built on the geographic picture assembled for the energy figures, with grid carbon intensity replacing renewable share. Locations are inferred for each population that contributes: the sequencing and proposing infrastructure, the full node and public endpoint tier, the operators of the external data availability layer, and the part of the settlement chain's validator base carrying this network's activity. The inference rests on publicly observable network data, and where a population cannot be placed directly the distribution of a structurally comparable network stands in for it, with the resulting uncertainty carried into the estimate rather than concealed.

Each location is matched to the carbon intensity of the electricity on the grid serving it, expressed in grams of carbon dioxide equivalent per kilowatt-hour. The electricity estimated for that location is multiplied by the corresponding factor, and the products are summed across locations to give the network total.

What this produces is a scope 2 figure: the indirect emissions embodied in electricity bought from a grid. Scope 1 covers emissions from sources an operator controls directly, such as fuel burned on site; for a network running on general-purpose servers in commercial data centers, scope 1 is normally negligible and is reported as such unless something specific suggests otherwise. Emissions embodied in manufacturing and disposing of the hardware fall outside this boundary and are not counted, so the figure is an operational rather than a life-cycle measure.

Greenhouse gas intensity is the marginal emission attributable to one additional transaction, obtained by allocating the total across transactions confirmed in the same period. It distributes shared overhead rather than describing any individual transaction, and it moves with grid factors and hosting decisions independently of network activity. The 2026 change in architecture also reweighted which populations dominate the total, which matters for comparing periods. 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 released under the Creative Commons Attribution 4.0 license.