Cosmos (ATOM) sustainability report

Cosmos is present on the following networks: Binance Smart Chain, Cosmos, Kava, Mantra, Osmosis.

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.

The Cosmos Hub is a sovereign chain built with the Cosmos SDK and driven by CometBFT, the Byzantine fault tolerant engine that was published for several years under the name Tendermint Core and was renamed in 2023. Agreement is reached in rounds rather than by mining. One validator drawn from the active set proposes a candidate block, the set broadcasts a prevote and then a precommit on it, and the block is committed once precommits representing more than two thirds of bonded voting power have been gathered. Finality is therefore deterministic and arrives with the commit itself: there is no confirmation depth to wait out and, in normal operation, no competing tip to resolve. The safety guarantee holds so long as validators controlling less than one third of bonded voting power depart from the protocol. Beyond that threshold the chain stops producing blocks rather than splitting into two histories, which is the trade the engine deliberately makes in favor of consistency over continuous availability.

Anyone may declare a validator, but the set is capped. Candidates are ranked by the stake bonded to them, counting the operator's own bond together with stake delegated by holders of the network's native asset, and only the highest ranked occupy the active slots, of which there are currently one hundred and eighty. Voting weight inside a round is proportional to bonded stake, so delegation is how holders influence who validates without running infrastructure themselves. Stake withdrawn from a validator stays locked for three weeks, which keeps it answerable for faults committed before the withdrawal began.

For several years the Hub's distinguishing feature was that it lent this validator set to other chains, whose operators ran an additional node alongside the Hub node. That arrangement has ended. A software upgrade adopted through on-chain governance and executed in September 2026 removed the provider module from the protocol, closed the communication channels it used, and resized the active set to match. Hub validators now secure the Hub alone, and the chain's position in the wider ecosystem rests on routing messages and assets between independent chains rather than on renting out consensus.

Kava is a layer 1 network built with the Cosmos SDK that reaches agreement through CometBFT, the Byzantine-fault-tolerant engine previously released under the Tendermint Core name. The design is proof of stake: voting power is allocated in proportion to the quantity of the network's native asset bonded to each validator, either committed by the operator directly or delegated to it by other holders, and only the hundred highest-weighted operators sit in the active set that produces blocks at any given height.

What separates this network from a conventional single-environment chain is its co-chain arrangement. One validator set and one consensus process secure two execution environments that sit side by side: an Ethereum-compatible environment in which Solidity contracts run, and a Cosmos SDK environment whose state changes are typed module messages rather than contract bytecode and which speaks the Inter-Blockchain Communication protocol to other Cosmos networks. A translator component moves value and calls between the two. Because both environments advance inside the same block, they share a single transaction ordering and a single security budget, rather than being separate chains joined by a bridge.

Block production follows the round structure usual to this consensus family. A proposer is drawn for each height with a frequency weighted by bonded stake, and the remaining validators move through a pre-vote and then a pre-commit round. Once more than two thirds of voting power has pre-committed, the block is committed and treated as final at that moment; there is no confirmation depth to wait out and no reorganization of committed history while fewer than one third of voting power behaves adversarially. On the Ethereum-compatible side blocks arrive roughly every six seconds and are final at the first block. Accountability is economic: operators that sign conflicting blocks at the same height, or that miss too large a share of recent blocks, forfeit part of their bonded stake and are excluded from the active set until they are reinstated.

MANTRA is a sovereign layer-one chain built with a widely used modular framework and driven by its Byzantine fault tolerant consensus engine, aimed specifically at assets that carry regulatory obligations. Blocks are committed in rounds rather than mined: one validator from the active set proposes a block, the set exchanges a round of prevotes and a round of precommits, and the block commits once precommits representing more than two thirds of bonded voting power have been gathered. Finality is deterministic and arrives with the commit in a few seconds, with no confirmation depth to wait out and no competing chain tip to resolve in normal operation. Safety holds while validators controlling less than a third of bonded power deviate from the protocol; past that threshold the chain stops producing blocks rather than splitting, which is the deliberate trade this family of consensus makes.

Validators are ranked into a capped active set by the total stake delegated to them, and holders who do not run infrastructure delegate to an operator of their choice and share in its rewards. Penalties are real rather than nominal: signing two conflicting blocks at the same height results in confiscation of a proportion of bonded stake, applied to the validator and its delegators alike, together with permanent removal from the set, while sustained failure to sign incurs a smaller confiscation and temporary suspension. Unbonding takes a fixed waiting period during which stake remains exposed to penalty.

Two features distinguish the chain from others in its framework family. Execution is not limited to one environment: a native contract environment sits alongside an Ethereum-compatible one, sharing state through interface layers so that contracts written for either can call the chain's staking, governance and asset modules directly. And compliance is enforced at the protocol level rather than in individual contracts, so an address that has not satisfied the applicable identity requirements cannot receive a token representing a regulated asset. Interoperability runs over the framework's native inter-chain messaging and, for external networks, through a separate messaging layer.

Osmosis is a sovereign chain built with the Cosmos SDK, reaching agreement through CometBFT, the Byzantine fault tolerant engine that carried the name Tendermint Core until its rename in 2023. Blocks are committed in rounds: a validator from the active set proposes, the set votes in a prevote stage and then a precommit stage, and the block is finalized the moment precommits representing more than two thirds of bonded voting power are collected. Nothing is probabilistic about this, so a committed block cannot be reorganized away and no confirmation depth needs to be observed. The guarantee the engine provides is that honest validators never commit conflicting blocks while fewer than one third of bonded voting power misbehaves; past that point the chain halts instead of forking.

Validators are ranked by the stake bonded to them, self-bonded and delegated counted together, and the highest ranked fill a fixed number of active slots, which on this network is seventy, a deliberately tighter set than several of the chains it interoperates with. Delegation lets holders of the native asset assign their weight to an operator and share in that operator's rewards, and it carries the same downside the operator carries. Bonded stake takes a fortnight to unwind, roughly half the period used elsewhere in the ecosystem.

What sets this network apart from a general-purpose Cosmos chain is that its exchange lives inside the state machine rather than in contracts deployed on top of one. Pool creation, routing a swap across several pools, concentrated liquidity positions, and the accounting of trading fees are all protocol modules that validators execute as part of processing a block, so a trade is a consensus-level state transition carrying exactly the same finality as a transfer. The protocol also runs an arbitrage module of its own, which inspects a proposed block for price discrepancies its pools have opened and captures the correction for the protocol rather than leaving it to outside searchers. A number of recurring operations, issuance and reward distribution among them, are processed once per daily epoch instead of every block.

Cosmos is present on the following networks: Binance Smart Chain, Cosmos, Kava, Mantra, Osmosis.

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.

Block production is paid for from two sources: newly issued units of the network's native asset, and the fees attached to the transactions in each block. Issuance is not a fixed schedule. The protocol aims at a target proportion of total supply being bonded, set at two thirds, and moves the annual issuance rate up or down inside a governance-defined band whenever the bonded proportion drifts away from that target, so the reward for bonding strengthens when too little stake is committed and weakens when the target is passed. Everything issued in a block is pooled with the fees collected in it, a small fixed slice is diverted to a community fund that governance spends, and the remainder is divided among active validators in proportion to bonded stake. Each validator retains a commission, which the protocol requires to be at least five percent, and the balance accrues to its delegators for them to withdraw when they choose.

Penalties are graded by how damaging the fault is. A validator that signs two conflicting blocks at the same height forfeits five percent of the stake bonded to it, delegators included, since delegation is genuine economic exposure rather than a vote of confidence, and it is permanently barred from the active set rather than merely suspended. Failing to sign enough blocks across a long measurement window costs a far smaller fraction and results in temporary jailing, from which the operator returns by submitting an unjail transaction after a short wait. Because unbonding runs for three weeks, stake that has started to leave a validator remains liable for faults committed before it left.

Users pay for execution in gas, denominated in the native asset. The Hub prices gas with an adaptive base rate that climbs as blocks fill and decays as they empty, resting on a governance-set floor, and the base portion of each fee is retained by the protocol instead of being passed through to validators; a sender may attach a tip above the base rate to compete for inclusion when demand is heavy. Smart contract calls are metered by the resources they consume, and there is no separate recurring rent charged on stored data.

Validators and the holders who delegate to them are the paid participants. Fee revenue from each block is spread across the active set in proportion to voting power, with an additional share to the proposer, and each operator passes on what remains to its delegators after deducting a commission rate it sets and publishes. Delegation lets holders contribute stake weight without running infrastructure, and it carries the matching downside: stake delegated to a penalized operator is reduced alongside the operator's own.

The material change in how all this is funded is that the protocol no longer issues new units to pay for security. Emissions were switched off at the start of 2024, the last inflationary issuance having been minted in the final block of 2023, and the protocol retains no mechanism to create further supply — only to destroy it. Rewards consequently come from two places: transaction fees collected in the ordinary course, and distributions out of a community-controlled on-chain treasury whose balance was set aside rather than printed. Ecosystem incentive programs that were once paid from new issuance are funded the same way, with governance deciding allocations and deciding whether any surplus is retired or redeployed. The reward budget is therefore a finite and governed pool rather than an open-ended subsidy, and the security of the network rests on fee revenue over the long run.

Users pay for execution in gas, denominated in the network's native asset on both co-chains. Contract execution in the Ethereum-compatible environment is metered per operation on the familiar opcode schedule, so computation, storage writes and contract deployment cost in proportion to the work they impose on every node; module messages on the Cosmos side are metered on an equivalent gas basis. Validators enforce a minimum acceptable gas price, and transactions offering more than that floor are ordered ahead of those that do not. Penalties form the counterweight: a fraction of bonded stake is confiscated for equivocation, a smaller penalty and temporary exclusion from the set apply to sustained unavailability, and stake withdrawn from bonding stays exposed through an unbonding period before it becomes transferable.

Validators are paid from issuance of the native asset together with the fees collected in the blocks they commit, distributed across the active set in proportion to bonded stake. Each validator declares a commission, taken before the remainder is shared among delegators in proportion to their contributions, with a ceiling on how fast that commission may be raised so delegators are not repriced without notice. Delegation is the principal route to participation, and it carries genuine risk rather than only opportunity cost, since the penalties described above apply to delegated stake as well as to the operator's own bond.

Users pay gas fees denominated in the native asset, metered by the computational and storage resources a transaction consumes, with validators able to set a minimum price they will accept. Transactions submitted through the Ethereum-compatible environment are priced through the same accounting rather than through a parallel fee market, so the choice of execution environment does not change what a given amount of work costs. Governance proposals require a deposit, refunded if the proposal reaches a vote and forfeited if it fails to attract sufficient support, which prices the chain's attention and discourages frivolous submissions. There are no recurring rents charged on holdings.

Two changes to the network's economics are worth recording because they affect how its history reads. Following a governance vote passed in late 2025, the native asset was redenominated, with holdings restated at a fixed ratio and a new ticker replacing the former one during 2026; the redenomination altered unit counts rather than any holder's proportional position. Separately, an acquisition of the entities operating the chain was announced in 2026, which changes the ownership of the organizations holding relevant licenses and operating infrastructure without altering the protocol's consensus or fee mechanics.

The compliance modules described above also shape the fee experience in one respect: transfers of regulated assets to addresses that have not cleared the applicable checks fail at the protocol level, and a failed transaction still consumes the gas spent attempting it.

Three groups are paid on this network, and the balance between them has shifted markedly. New units of the native asset are minted on a daily cadence along a schedule that steps down by a third every seven hundred and thirty days, and governance decides how each day's issuance is split. Liquidity provision was once the largest claim on that issuance; it has since been removed from the split entirely, on the argument that trading revenue rather than subsidy now sustains the pools. The staking share has been cut back as well, with most of each day's issuance now directed into the community fund alongside a fixed development allocation. Bonded stake is increasingly compensated from revenue instead: transaction fees collected in the native asset accrue to stakers, and fees paid in other accepted denominations are converted first.

That revenue comes from trading. Every swap pays a spread factor to the providers of liquidity in each pool it touches, and separately a taker fee to the protocol, set by default at a tenth of a percent and overridden route by route by a delegated fee committee that prices heavily traded pairs down and thin ones up. Taker fees collected in the native asset are split so that the larger part is destroyed and the remainder is paid to stakers. Taker fees collected in other assets are divided between the community fund and a buyback that acquires the native asset before splitting it the same way, which ties the rate of destruction directly to trading volume. Liquidity providers may bond their positions for additional incentives, and a superfluid arrangement lets the portion of a bonded position represented by the native asset be delegated to a validator at the same time, so one unit of capital both secures consensus and backs a pool.

Ordinary transactions pay gas at a base price that climbs when blocks fill and falls back when they empty, resting on a governance-set minimum, a mechanism intended to price out spam rather than to raise revenue; fees may be paid in a whitelisted set of denominations, not only the native one. Validators must charge at least five percent commission. Signing two conflicting blocks costs a validator and its delegators a share of bonded stake and permanent exclusion from the set, while missing too many blocks results in jailing rather than confiscation.

Cosmos is present on the following networks: Binance Smart Chain, Cosmos, Kava, Mantra, Osmosis.

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 reported for this network is an estimate assembled from the machines that run it, not a metered measurement. The starting point is the size and composition of the node population: the validators in the active set, the candidates bonded but outside it, and the full, archive, and relay nodes that serve queries and forward packets between chains. That population is approximated from peer discovery on the public network, from the information operators publish about themselves, and from the chain's own records, since validator identity and bonded stake are recorded on-chain even when the machine behind them is not.

Each node is then given a hardware profile. The client software states the processor, memory, disk, and bandwidth a node needs to keep pace with block production, and a machine consistent with those requirements stands in for the node. Electrical draw for such a machine is taken from controlled measurement of comparable equipment across its load range, idle draw included, because a validator is powered continuously whether blocks are full or nearly empty. Multiplying representative draw by the estimated population across the hours of the reporting period gives a network total, and the share attributed to an individual asset is apportioned from observed on-chain activity.

Two limitations should be stated plainly. The population count and the hardware mix are inferences drawn from public observation and published requirements, not from disclosure: operators are not obliged to say what they run, several may sit in one facility, and rented virtual capacity is difficult to separate from dedicated hardware. Where evidence runs out, the assumption chosen is the one that raises the estimate rather than lowers it, so the reported figure is more likely to sit above the true value than below it. One component that appeared in earlier assessments of this network no longer applies. Because the Hub used to secure other chains, a proportion of their consumption was attributed to it and apportioned by gas usage; the provider arrangement was removed from the protocol in 2026, and the estimate now covers the Hub's own infrastructure alone. Figures are restated as observation improves.

The figure is built from the network's node population rather than from any metered reading taken across the network, which is the appropriate treatment for a stake-weighted Byzantine-fault-tolerant chain where the right to produce a block is not won by computational effort. The estimation approach used here begins by establishing how many machines take part. The active and standby validator set is enumerated from public chain state, and the wider population of full, archive and public endpoint nodes is approximated using network crawlers alongside publicly listed infrastructure. A representative hardware profile is then inferred from the specifications the client software states for running a node that can keep pace with the chain, and the electrical draw of machines matching that profile comes from laboratory measurement, recorded both under load and at rest. Aggregating that draw across the estimated population over the reporting period, with idle hours counted rather than assumed away, gives the network total.

The co-chain arrangement matters to this calculation. Since one validator set and one consensus process advance both the Ethereum-compatible and the Cosmos-side environments inside the same block, there is no second node population to add for the contract environment. Counting the validator and node set once captures both, and treating the two environments as though they were distinct networks would double the result.

Several limits should be read alongside the output. The node count and the hardware mix are inferences drawn from public observation and from stated software specifications, not readings taken from the machines themselves, and operators are under no obligation to disclose what they run. Where the evidence is thin, the assumptions chosen sit at the cautious end, so the result is likelier to overstate consumption than to understate it. Figures are restated as observation of the node population improves or as client specifications change. Where an asset is issued across more than one network, the portion attributed to each is derived from observed on-chain transfer volumes rather than divided evenly between them.

The estimate is a node-level one, and this network offers an unusually firm starting point for it. The active validator set is capped by an on-chain parameter and its membership is readable directly from the chain's staking module, so the count of consensus-participating machines is a known quantity rather than something inferred from crawling anonymous peers. That removes the largest single uncertainty affecting most estimates of this kind and shifts the question to what sits behind each of those registered identities.

Beyond the active set, three further populations consume power and must be sized separately. Candidates outside the active set run infrastructure while waiting to be elected, and their machines draw power whether or not their operator is currently validating. Full nodes operated by exchanges, explorers, applications and the chain's own service infrastructure hold complete copies of the ledger without participating in consensus, and are estimated from public listings and network observation. Nodes serving remote procedure calls to applications carry query load that can exceed a validator's own work at busy times, and are counted within that population.

Representative hardware is derived from the published requirements for running the node software, which state processor, memory, storage and bandwidth expectations. Measured power draw for machines of that description, taken under load and at idle, is applied across each population and weighted heavily toward idle, because a node in a chain of this size spends the great majority of its time validating and gossiping rather than working hard. Supporting an additional execution environment raises the computational demand per transaction somewhat but does not change the shape of the estimate.

The limitations are stated plainly. Operators commonly run redundant and backup machines that are invisible from outside, so the count behind each registered identity is an assumption rather than an observation. Hosting arrangements obscure how many physical devices underlie a given endpoint. Where evidence is thin the conservative assumption is preferred, more likely to overstate than understate, and figures are revised as observation improves.

The reported consumption for this network is a modeled estimate rather than a measurement taken from a meter, and it is built from the machines that keep the chain running. The first task is to size that machine population. It comprises the seventy validators in the active set, the bonded candidates waiting outside it, and the full, archive, and indexing nodes that serve the trading interfaces, routing services, and relayers connecting this chain to its neighbors. The count is approximated from peer discovery on the public network, from what operators publish about their own deployments, and from the chain's own on-chain register of who is bonded.

Each machine is then represented by a hardware profile. The client software publishes the processor, memory, disk, and bandwidth a node needs to stay in sync, and a machine meeting those requirements stands in for the node. This chain sits at the demanding end of the range for its family, because validators execute swap routing, concentrated liquidity accounting, and the protocol's own arbitrage checks inside block processing rather than delegating them to a contract layer, and because several recurring tasks are batched into a daily epoch that produces a pronounced load spike. Electrical draw for the representative machine comes from controlled measurement of comparable equipment across its load range, idle draw included, since nodes are powered continuously. Draw multiplied by population over the reporting period gives the network total, from which a per-asset share is apportioned using observed on-chain activity.

Two caveats matter. The population and the hardware mix are inferred from public observation and stated software requirements, not from operator disclosure, and where evidence is missing the assumption chosen raises rather than lowers the estimate, so the figure is likelier to overstate than understate. Second, a correction: earlier assessments attributed to this network a share of another chain's consumption on the grounds that the other chain contributed to its security. That is not how this network is secured. It has its own validator set, its own bonded stake, and its own penalties, and the estimate here covers only the machines that run it. Figures are restated as observation improves.

Cosmos is present on the following networks: Binance Smart Chain, Cosmos, Kava, Mantra, Osmosis.

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 follows from where its machines sit, because electricity is not generated the same way everywhere. Node locations are approximated from publicly observable network data: the addresses peers advertise when they connect, the hosting ranges those addresses belong to, and whatever operators choose to disclose about their facilities. What this yields is a distribution of the node population across countries and regions, not a street address for any individual machine. Where that distribution cannot be resolved with enough confidence, the observed distribution of a network built and rewarded along similar lines is used in its place, on the reasoning that comparable economics tend to place infrastructure in comparable locations.

Each region in the distribution is matched to published statistics describing how its electricity is generated, and the network's estimated consumption is weighted across those regions to give the proportion supplied from renewable sources. The generation data is drawn from Share of electricity generated by renewables, compiled by Our World in Data with major processing from Ember's yearly electricity data and the Energy Institute's Statistical Review of World Energy.

Energy intensity is a separate quantity and is easy to misread. It is not total consumption divided by the number of transactions. It is a marginal figure: the additional energy the network draws when one more transaction is included in a block. On a chain of this design the distinction matters a great deal, because the validator set is powered continuously and commits a block on a fixed cadence whether that block is full or nearly empty, so the great majority of the draw is a fixed cost that an extra transaction does not move.

The weak point of the method is the geolocation step. Address-based location is approximate and identifies the hosting provider rather than the customer; operators sitting behind commercial cloud regions are attributed to the advertised region rather than to any particular building; and grid statistics are national or regional averages that take no account of supply contracts a specific facility may hold. Where a stand-in distribution is used, its representativeness is itself an assumption rather than an observation.

The renewable share is derived geographically. Node locations are inferred from publicly observable network data — the addresses peers advertise, the hosting ranges those addresses fall within, and operator disclosures that are already public — and each located node is assigned to the electricity grid of the region it sits in. Aggregating those assignments produces a weighted picture of which grids the network's infrastructure actually draws on, which is the input the renewable calculation needs.

Coverage is never complete. A meaningful share of nodes sits behind hosting arrangements or privacy configurations that reveal nothing dependable about physical location. The co-chain arrangement does not complicate this, because the same machines serve both execution environments and so are located once. Where a network's own geographic spread cannot be observed to a usable standard, the spread of a structurally similar network is substituted as a stand-in — one chosen for a comparable validator economy, a comparable cost of entry for node operators, and therefore a comparable hosting pattern. That substitution is a source of uncertainty in its own right and is applied only to the portion that cannot be resolved directly.

Grid assignments are then matched against published statistics on how electricity is generated in each region, yielding the proportion of the network's electricity that comes from renewable generation. Those statistics are taken from Share of electricity generated by renewables, compiled by Our World in Data from Ember and from the Energy Institute's Statistical Review of World Energy.

Energy intensity is a separate quantity and should not be read as a per-transaction bill. It is defined at the margin: the additional electricity drawn as a consequence of one further transaction being processed, given the infrastructure already running. On a network of this kind, where validators run continuously and commit blocks on a fixed cadence whether or not demand is present, that marginal quantity is small next to the standing consumption, and it moves inversely with throughput — the busier the network, the lower the intensity attributed to each transaction.

Locating this network's machines rests on a mixture of disclosure and observation, weighted toward disclosure more than is typical. Validators publish identity information because delegation depends on reputation, and a chain built for regulated assets attracts operators who are themselves regulated or institutional and who therefore disclose where they operate as a matter of course. That gives a geographic distribution for the capped active set founded on stated fact rather than inference. Candidates outside the active set are generally visible on the same basis. Full nodes and query-serving infrastructure are located through network observation and hosting provider address ranges, with materially less confidence.

Each located machine is matched to published statistics for the grid supplying its region, and the renewable share reported is the average across those grids weighted by the consumption attributed to each location rather than by machine count.

Two limitations bear particularly on this network. First, a capped and partly institutional validator set is small, so the distribution is built from few observations and a single operator relocating can move the result perceptibly, where a network of thousands of nodes would absorb the same change without notice. Second, institutional operators favor commercial hosting facilities that do not publish their supply arrangements, so a regional grid average substitutes for the actual supply of a specific building. Grid statistics are annual and regional and conceal daily and seasonal variation, and contractual renewable purchases are not counted because the method describes the physical grid mix a machine draws from.

Energy intensity per transaction is period consumption divided by transactions settled in the period. Because the validator set is capped, consumption is close to fixed with respect to throughput: an additional transaction causes almost no additional energy, and the intensity figure falls as the network is used more without any change in the underlying energy use. It should be read as an average across the period rather than as the marginal energy cost of one further transfer. Source data is processed by Our World in Data from Ember and the Energy Institute's Statistical Review of World Energy: Share of electricity generated by renewables.

Because electricity is generated differently from one grid to the next, the renewable share reported for this network depends on establishing where its machines are. Locations are inferred from publicly observable network data: the addresses validators and other nodes advertise to their peers, the hosting ranges into which those addresses fall, and whatever operators choose to publish about their own infrastructure. The output is a distribution of the node population over countries and regions, never a precise site for a given machine. Where the distribution cannot be established with confidence, the observed distribution of a network with a comparable consensus design and comparable rewards is substituted, on the assumption that similar economics lead operators to similar places.

Every region in that distribution is then paired with published statistics on the composition of its electricity generation, and the network's estimated consumption is weighted across the regions to yield the proportion met from renewable sources. The generation statistics are taken from Share of electricity generated by renewables, compiled by Our World in Data with major processing from Ember's yearly electricity data and the Energy Institute's Statistical Review of World Energy.

Energy intensity is reported as a marginal quantity and should not be read as consumption divided by transaction count. It answers a narrower question: how much additional energy the network draws when one further transaction is included in a block. That distinction is particularly sharp here. Validators run continuously and commit blocks on a fixed cadence whether or not there is trading to process, so nearly all of the draw is a standing cost, and the incremental cost of one more swap, which is executed by the same validator process as any other message, is very small by comparison.

The method's weakest link is geolocation. An address identifies a hosting provider rather than a customer, machines behind large commercial cloud regions are assigned to the advertised region rather than to a physical building, and the grid statistics are averages over a country or region that ignore any supply arrangement an individual facility has made. Where a stand-in distribution has been used, its representativeness remains an assumption.

Cosmos is present on the following networks: Binance Smart Chain, Cosmos, Kava, Mantra, Osmosis.

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 two inputs as the energy figures, an estimate of how much electricity the network's machines draw and an estimate of where those machines are, combined with the carbon intensity of the electricity supplied in each of those places. The geographic distribution is built from publicly observable network data and, where it cannot be resolved, from the distribution of a structurally comparable network. Each region's estimated consumption is multiplied by the emissions released per unit of electricity generated on that region's grid, and the products are summed to a network total.

The intensity data is drawn from Carbon intensity of electricity generation, compiled by Our World in Data with major processing from Ember's yearly electricity data and the Energy Institute's Statistical Review of World Energy, and made available under the Creative Commons CC BY 4.0 license.

The two reported scopes describe different things. Scope 1 covers emissions released by sources the operators of the infrastructure control directly, which in practice means combustion on site, such as a generator burning fuel. A network of this kind runs on ordinary servers in data centers drawing from public grids, so there is generally no such combustion to account for and the scope 1 figure is reported at or near zero rather than left out. Scope 2 covers the emissions embodied in the electricity those machines purchase, and effectively the whole footprint sits there. Greenhouse gas intensity is computed in the same marginal way as energy intensity: the additional emissions attributable to including one further transaction, rather than the network total divided by a transaction count.

Every uncertainty in the consumption and location estimates carries straight through into these numbers, and the intensity data contributes one of its own. Grid figures are annual and regional; they smooth over the hours of the day when consumption actually falls and over any generation a facility has contracted for directly. Where evidence is thin the more conservative assumption is applied, so the reported footprint is more likely to overstate than to understate the network's impact, and the figures are restated as the underlying observation improves.

Emissions are derived from the same geographic work that supports the energy figures, applied against a different coefficient. Once the node population has been located and assigned to regional grids, each assignment is matched to the carbon intensity of electricity generation in that region — the mass of carbon dioxide equivalent released per unit of electricity delivered — and the network's estimated consumption is apportioned across those regions and converted. Regional variation is wide enough that two networks consuming identical amounts of electricity can differ substantially in emissions, which is why locating the infrastructure carries as much weight in the result as sizing its draw.

The two scopes are treated differently. Scope 1 covers emissions from sources the operators control directly, such as fuel burned on site for backup generation, and for a network of this design it is negligible or zero, since validators run commodity servers on purchased electricity rather than any combustion process of their own. Scope 2 covers the indirect emissions embodied in that purchased electricity and accounts for effectively the whole of the result. Where a node's location cannot be established with confidence, the regional profile of a structurally comparable network stands in for it, and that substitution carries into the emissions result exactly as it does into the energy one.

Carbon intensity coefficients are taken from Carbon intensity of electricity generation, compiled by Our World in Data from Ember and the Energy Institute's Statistical Review of World Energy and published under the Creative Commons Attribution 4.0 license.

Greenhouse gas intensity mirrors the definition used for energy intensity: the additional emissions attributable to one further transaction beyond those already being processed, rather than total emissions divided by a transaction count. Because the infrastructure runs continuously regardless of load, that marginal quantity is modest and falls as utilization rises. Results are restated when regional grid statistics are updated or when better observation of the node population becomes available, and the direction of any assumption made under uncertainty favors the higher estimate.

Emissions are derived from the consumption estimate by applying a grid carbon intensity to the electricity drawn at each location, reusing the distribution established for the renewable share and summing across validators, waiting candidates, full nodes and query-serving infrastructure. The boundary covers operational electricity only; manufacture of the hardware and construction of the facilities housing it are outside it, and no factor is applied for either.

Scope 1 covers emissions from sources under the direct control of node operators, which for servers in commercial hosting facilities means occasional backup generation during outages. It is a negligible contributor here and is reported as such rather than modeled in detail. Scope 2 covers emissions embodied in purchased electricity and accounts for effectively the entire footprint.

One structural point shapes this network's emissions profile. Because the active validator set is capped by protocol parameter, total emissions are bounded by a number the protocol fixes rather than by market conditions. Growth in usage adds work to machines that are already running rather than adding machines, and an increase in the value of participation does not draw additional hardware into consensus the way it does on networks where security scales with expenditure. Emissions move when the cap changes, when the pool of waiting candidates grows, or when operators relocate.

Greenhouse gas intensity per transaction is period emissions divided by transactions settled in the period, and inherits the caveat given for energy intensity: consumption is close to fixed with respect to throughput, so the quotient describes an average rather than the emissions caused by one further transaction. Uncertainty compounds through the calculation, since an error in the machines assumed behind each registered identity propagates into consumption and from there into emissions, and grid intensities are annual averages concealing substantial variation across a day and a year, which a small and geographically concentrated node population is less able to average out. Figures are restated each period as observation improves. Carbon intensity data is processed by Our World in Data from Ember and the Energy Institute's Statistical Review of World Energy, and is made available under a Creative Commons BY 4.0 license: Carbon intensity of electricity generation.

Emissions figures are constructed from the energy estimate and the geographic estimate together. The distribution of nodes across regions is inferred from publicly observable network data, with the distribution of a structurally comparable network standing in where direct observation is insufficient. Each region's share of estimated consumption is then multiplied by the emissions released per unit of electricity generated on that region's grid, and the results are summed to give the network total.

Carbon intensity values are taken from Carbon intensity of electricity generation, compiled by Our World in Data with major processing from Ember's yearly electricity data and the Energy Institute's Statistical Review of World Energy, and published under the Creative Commons CC BY 4.0 license.

The distinction between the two reported scopes is worth spelling out. Scope 1 captures emissions from sources the infrastructure's operators control directly, essentially combustion happening on their own premises. Validators and supporting nodes for this network are conventional servers housed in data centers and supplied from public grids, so there is ordinarily nothing of that kind to record and the scope 1 figure is reported at or close to zero rather than omitted. Scope 2 captures the emissions embodied in the purchased electricity that those machines consume, and in practice the entire footprint falls under it. Greenhouse gas intensity mirrors energy intensity in construction: it expresses the emissions attributable to one additional transaction at the margin, not an average obtained by dividing a total by a count.

Every uncertainty already present in the consumption and location estimates propagates into these figures, and the intensity data introduces another. Published grid intensities are annual averages across a region; they cannot reflect the hours at which a node's consumption actually falls, nor any generation a particular operator has contracted for directly. Where the evidence is thin, the conservative assumption is preferred, meaning the reported emissions are more likely to sit above the true value than below it, and every figure is revised as the underlying observation improves.