Midnight (NIGHT) sustainability report

Midnight is present on the following networks: Binance Smart Chain, Cardano.

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.

Cardano runs on Ouroboros Praos, a proof-of-stake protocol whose security argument rests on a published line of peer-reviewed research rather than on computational work. Time is cut into epochs of five days, and each epoch into one-second slots. For every slot a private lottery decides whether a given stake pool is entitled to produce a block: the pool evaluates a verifiable random function against the epoch's randomness seed and its share of delegated stake, and a winning pool can prove its entitlement to everyone else without any prior announcement. Because the outcome is private until the block appears, an adversary cannot know in advance which operator to attack. Most slots pass empty, which is expected rather than a fault.

Stake is delegated, not locked. A holder registers a staking credential and points it at a pool of their choosing, and the funds stay in their own wallet, spendable at any moment, with no bonding period and nothing to wait out when they change their mind. Pools are registered on-chain with a declared margin, a fixed cost and a pledge of the operator's own holdings, all publicly readable. Selection of a slot leader is weighted by the stake sitting behind a pool at the epoch snapshot.

The protocol has no mechanism for confiscating stake. There is no slashing of either delegated funds or an operator's pledge: a pool that is offline, misconfigured or simply unlucky forfeits the rewards it would have earned, and that forgone reward is the entire penalty the system imposes. Settlement is probabilistic and strengthens with chain density as blocks accumulate, so the longer a block has been buried the more expensive it becomes to displace. Governance decisions now run on-chain through delegated representatives, an elected constitutional committee and pool operator voting, and a successor consensus design intended to lift throughput by separating transaction distribution from block confirmation is in development, with a fallback to the present behavior when the network is congested or under attack.

Midnight is present on the following networks: Binance Smart Chain, Cardano.

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 from two pools of value. A reserve releases a fraction of its remaining balance each epoch, and transaction fees collected during the epoch are added to it. The combined amount is shared among pools in proportion to the stake that produced blocks, with a portion diverted to the treasury that funds on-chain proposals. Within a pool, the operator's declared fixed cost is taken first, then the operator's percentage margin, and whatever remains is divided among delegators in proportion to the stake each contributed. Everything about that split is published on-chain before anyone delegates, so the terms are visible in advance.

Two design parameters shape where stake settles. A saturation threshold caps the rewards any single pool can earn once the stake behind it passes a share of the total, so oversized pools return less per unit of stake and delegators have a standing reason to move elsewhere. A pledge influence factor pays slightly more to pools whose operators have committed their own funds, which raises the cost of running many pools with nothing at stake. Neither mechanism takes anything away; both work by making one choice more rewarding than another.

Users face a transaction fee calculated from a published formula: a constant plus a per-byte charge on the serialized transaction, so cost follows size and is predictable before submission. Every output must carry a minimum quantity of the native asset in proportion to the size of the entry it creates, which prices the long-term burden a transaction places on the state that every node must hold. Script execution is metered separately in two dimensions, memory and computational steps, each with its own price, and the submitter sets a budget that is charged whether or not the script succeeds. Registering a staking credential and submitting a governance action both require deposits, refundable when the registration is retired or the action concludes.

Midnight is present on the following networks: Binance Smart Chain, Cardano.

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 is built from the bottom of the network upward rather than inferred from the value of block rewards, because a stake-based protocol gives block production no incentive to consume more electricity as rewards rise. The starting point is the population of machines that actually run the chain. Pool registration is itself an on-chain record, so the number of registered producing pools is directly readable rather than guessed at, and each production node is customarily fronted by relay nodes that carry network traffic, which has to be accounted for as a multiplier on the count that registration alone reveals. Public node listings and network crawlers supply an estimate of that multiplier and of the additional full nodes run by exchanges, explorers and applications that hold a copy of the ledger without ever producing a block.

Representative hardware follows from the published requirements for running the node software, which state memory, storage and processor expectations. Measured power draw for machines of that description, taken under load and at idle, is applied across the estimated population, and idle draw matters disproportionately here: a node spends the overwhelming majority of slots doing nothing but validating and gossiping, so average consumption sits much closer to the idle figure than to the peak.

Several honest limitations come with that approach. The node population is observed from the outside and cannot be audited; operators running several nodes on shared hardware, or several chains on one machine, are counted in ways that no external observer can fully resolve. The hardware mix is a distribution approximated by a representative device. Where evidence is thin the assumptions chosen are the conservative ones, more likely to overstate the total than to understate it, and estimates are revised as observation improves. Throughput does not change the answer much, since a node's draw is dominated by being switched on rather than by the transactions it happens to process.

Midnight is present on the following networks: Binance Smart Chain, Cardano.

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 is not measured at any node; it is inferred from where the machines are and what the electricity in those places is generated from. Location evidence for this network is better than for many, because stake pools advertise themselves to attract delegation: registration metadata, operator-published relay endpoints and public network crawls together give a usable picture of the countries and regions in which producing and relaying nodes sit. That picture is incomplete, as hosting providers obscure the physical location of a share of nodes and some operators deliberately publish nothing, so the observed distribution is treated as a sample and extended to the unobserved remainder rather than assumed to cover everything.

Each located node is then matched to published statistics for the grid that supplies it, and the renewable proportion reported here is the weighted average of those grid figures across the estimated node population, weighted by the consumption attributed to each location rather than by node count alone. Grid statistics are annual and regional, so short-term variation in how a particular facility is supplied is invisible to this method. Contractual arrangements such as renewable supply agreements are not counted, because the method describes the physical grid mix a node draws from, and a purchase contract does not change the electrons delivered at a given moment.

Energy intensity expresses the consumption attributable to one additional transaction. It is computed as total consumption over the period divided by the transactions settled in the same period. For a stake-based chain that quotient should be read with care in one specific respect: the denominator moves with demand while the numerator barely does, since a node draws close to the same power whether the chain is busy or idle. Intensity therefore falls as the network is used more, and the figure describes an average cost of throughput rather than the incremental energy a single extra transaction actually causes. 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.

Midnight is present on the following networks: Binance Smart Chain, Cardano.

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 observed directly, by attaching a carbon intensity to each unit of electricity according to where that electricity was drawn. The node locations established for the renewable calculation are reused, each is matched to a published carbon intensity for the relevant grid, and the product of consumption and intensity is summed across the estimated node population. No emissions factor is applied to the manufacture of hardware or to the construction of the facilities housing it; the boundary is operational electricity only, and embodied emissions from equipment production are outside it.

The two scopes behave very differently for a network of this shape. Scope 1 covers emissions from sources under the direct control of the operators — in practice, on-site combustion such as a backup generator running during an outage. Across a population of ordinary servers in commercial hosting facilities this is a negligible contributor and is reported as such rather than modeled in detail. Scope 2 covers the emissions embodied in purchased electricity, and for this network it is effectively the whole of the footprint, because the activity being measured is machines drawing power from public grids.

Greenhouse gas intensity per transaction is formed the same way as energy intensity, by dividing period emissions by transactions settled in the period, and carries the same caveat: consumption is close to fixed with respect to throughput, so the quotient describes an average rather than the marginal emissions of one more transfer. Uncertainty compounds across the chain of estimates, since an error in the node population propagates into consumption and from there into emissions, and grid intensity is itself an annual average that smooths over substantial daily and seasonal variation in how a grid is supplied. Figures are restated each period as node observation and grid data are updated. 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.