JUL

Network Assessment& Methodology

CardanoSustainability Indicatorsof the Cardano Network

EXECUTIVE SUMMARY
Network Assessment
CardanoJuly 2024

The analyses underlying this report were commissioned by the Cardano Foundation.

•

Cardano employs an energy-efficient consensus protocol. In comparison to Proof of Work (PoW)-based
protocols such as Bitcoin, Cardano consumes significantly less electricity.

•

We find a total annualized electricity consumption of 704.91 MWh for the Cardano network, as of May 2024.

•

Furthermore, we calculated the carbon emissions associated with the annual electricity consumption via
location-specific emission factors, taking into account the locations of the validator nodes.

•

For the Cardano network, we find a total annualized carbon footprint of 250.73 tCO2e. The carbon intensityof the consumed electricity sits at 356 gCO2 per kWh.

•The marginal power demand per TPS (transactions per second) in the Cardano network amounts to 0.192 W.

•

In addition to electricity consumption and carbon footprint, we provide sustainability metrics in line with thedraft regulatory technical standards (RTS) provided by the European Securities and Markets Authority (ESMA)in the second consultation package of the Markets in Crypto-Asset (MiCA) regulation.

SELECTED BENCHMARKING RESULTS

Carbon Intensity[gCO2/kWh]

0

50

100

150

200

250

300

350

400

450

500

Power per Node
[W]

0

50

100

150

200

250

300

350

1

1.2

Marginal Power
Demand per TPS[W]

0Legend:0.2

indicates the range,

0.4

0.6

0.8

Cardano’s performance and median value of the peer group*.

*Peer group consists of the networks that were assessed in the latest CCRI PoS Benchmarking Study. Available here:
https://carbon-ratings.com.
dfdf

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A. NETWORK ASSESSMENT METHODOLOGY
1 PREAMBLE
CCRI follows a standardized assessment process for Proof of Stake networks (CCRI, 2022)1, making
reasonable assumptions if specific knowledge is not available outside in as described in the standard
methodology. In this assessment of the Cardano network, CCRI is able to access details about network
specifics provided by the Cardano Foundation, allowing an adjustment of the methodology and key
metrics. For transparency, any deviations from the standardized approach in (CCRI, 2022)1 arehighlighted in blue.

2 STRUCUTRE OF THE MODEL
The model generates several key sustainability metrics of the Proof of Stake network, namely electricity
consumption and carbon footprint of the network. For this, the model considers the nodes in the networkand their individual power demands under different transaction throughput rates. The model contains
following steps:
1.

We calculate the power of the entire network (𝑃𝑁 ), which we obtain by multiplying the number ofnodes in the Cardano network (𝑁𝐶) by the power consumption of a representative node in the
network (𝑃𝐵𝐺 ). To determine the power demand of the single best guess node, we leverage themeasured metrics: we multiply the transactions per second (𝑇𝑃𝑆) with the marginal power
demand per one TPS (𝑃𝑚 ), and add the node’s base power demand (𝑃𝑏 ), i.e., the power demand if
the Cardano node software is executed at zero transaction throughput, to it:

𝑃𝑁 = 𝑃𝐵𝐺 ∗ 𝑁𝐶
𝑃𝐵𝐺 = 𝑃𝑏 + 𝑃𝑚 ∗ 𝑇𝑃𝑆
2.

We derive the electricity consumption of the Cardano network (𝐸𝑁 ) over a specific time period bymultiplying the network’s total power demand (𝑃𝑁 ) by the considered time (𝑡[ℎ]):𝐸𝑁 = 𝑃𝑁 ∗ 𝑡[ℎ]3.

Third, we calculate the carbon footprint of the Cardano network (𝐶𝐹𝑁 ) by multiplying the network’s
electricity consumption over the regarding time period (𝐸𝑁 ) by the carbon intensity factor of the
network’s grid (𝐶𝐼):

𝐶𝐹𝑁 = 𝐸𝑁 ∗ 𝐶𝐼

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3 NETWORK ASSESSMENT
To generate the required parameters (namely 𝑃𝑏 , 𝑃𝑚 , 𝐶𝐼, and 𝑁𝐶), we follow the process as described in(CCRI, 2022)1. This standard methodology builds upon five steps to generate data on the electricity
consumption and carbon footprint of a PoS system.
3.1

HARDWARE SELECTION
To come up with a representative hardware set of the network, we investigate Cardano’s
minimum hardware requirements, as these are an indicator of the hardware composition of
the network. We use this information and additional hardware data from PassMark to selectand obtain hardware that we use to measure a single node's electricity consumption. For
measuring the Cardano network, we select devices 4-6 from our hardware (see Appendix forthe devices’ specifications).
Generally, it is difficult to obtain the individual hardware composition of a network from an
outside perspective. Therefore, we usually rely on a binomial distribution, assuming that entities
always adhere at least to the minimum hardware requirements, but also might run hardwarethat is more potent. For Cardano, we were able to access a recent survey of Stake Pool
Operators (SPOs) that allows us to deviate from a binomial distribution and rather seek a more
representative distribution, as highlighted in the Appendix (“Hardware specific measurement
result” -> “Assumed node distribution”).

3.2

HARDWARE MEASUREMENT
We measure the electricity consumption of a single node and provide upper and lower bounds
for nodes in the Cardano network. We start by running the software required for participatingin the network (cardano-node v8.9.2) on all selected hardware devices and measure theirelectricity consumption while running the network and while idling. To be able to evaluate
additional metrics, we capture further data points during the execution, such as CPU utilization,
temperature, and processed blocks.
When measuring the nodes, our measurement system periodically retrieves the average power
demand over the last 30 seconds. To match the block time of Cardano, we set this variable to
20 seconds.

3.3

ELECTRICITY CONSUMPTION
We estimate the electricity consumption of the entire Cardano network. First, we collectinformation about the size of the network, i.e. the number of active SPOs and potential relay
nodes, as the node count significantly influences the total amount of electricity consumed.
Second, we use the weighting mentioned in 3.1 between the single measured hardware
devices to best fit the composition of the Cardano network. Third, we multiply the electricity
consumption of the weighted nodes by the number of nodes in the network.
The number of nodes in the network is crucial for a proper assessment. CCRI analyzed theactivity of SPOs and their block production rate, including their expected block production rate.
We observed that not all SPOs that have an active stake (eligible to produce blocks) participate
in the network, as they produce zero blocks (while expected to produce a sufficient number ofblocks), meaning that they are offline. Given that there are no slashing or other mechanisms to

CCRI (2022). Determining the electricity consumption and carbon footprint of Proof-of-Stake networks.
https://carbon-ratings.com/dl/whitepaper-pos-methods-2022
1

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discourage such behavior (except opportunity costs), we do not consider such SPOs actively
participating in the network and thus do not include them in the overall calculation of theelectricity consumption (resp. the carbon footprint) of the network. Instead, we consider theactive producing SPOs for our model.
The number of devices per SPO is also a key variable in the model. In this assessment, we
assume that one SPO operates three devices on different machines, namely one block
producer and two relays. This decision is based on following observations within the network:•

SPO questionnaire: The questionnaire shared by Cardano Foundation included aquestion on how much relays a SPO uses for its block producing node, whereas themean number of relays was 2.03 (n=239), resulting in a total of three devices.

•

Network data observation: The data generated by Cardano’s blockperf2 allows anobservation of how much relays a block producer is using. Over the last epochs (475 –
483) the mean number of relays ranged from 1.97 to 2.15 nodes, confirming ourassumption.

•

Best practice: If no other data is available, we usually rely on an official node setup
description, that highlights how much devices the node operator should deploy.
Cardano has a guideline for operating large stake pools, which explicitly mentions thethree-device setup “it is recommended that an SPO maintains at least two and an
additional relay node(s) per a stake pool.”3

3.4

PERFORMANCE METRICS
We analyze additional data, such as transaction and block information, to develop further
metrics to explore the energy efficiency of transaction throughput for each network. We takesamples of the nodes’ electricity consumption periodically and examine the number oftransactions that the single nodes handled during the respective time periods. This allows us
to describe the marginal influence of the number of transactions on the electricity
consumption of a node in the Cardano network. As a result, we establish a model to estimate
a best guess node’s power demand based on the number of transactions. This enables us tomodel the electricity consumption of the Cardano network over time, as node count andtransaction volume change.

3.5

CARBON FOOTPRINT
We estimate the CO2 emissions arising from the operation of the Cardano network. To do so,
we use our data on network electricity consumption and multiply it by the carbon intensity of
the network. We derive an appropriate carbon intensity of the network by leveraging therespective grid emission factors of the regions where the SPOs are located.
We use location data that is generated by Cardano’s blockperf2 monitoring tool. The tool allows
us to observe the location of the block producing SPOs.

2
3

Cardano blockperf, https://github.com/cardano-foundation/blockperf
See https://docs.cardano.org/operating-a-stake-pool/guidelines-for-large-spos/#provisioning

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4 ADDITIONAL TRANSACTION-BASED LEDGER METRIC
Cardano employs a transaction-based ledger (in contrast to an account-based ledger in most other PoS
networks). It therefore relies on unspent transaction outputs (UTXO) 4, that contain the funds of therespective address. If users want to spend the funds associated with an UTXO, they need to provide a newtransaction with a transaction input (TXin), that refers to the respective UTXO. The transaction itself creates
new UTXOs that allow for further spending. The signature of the transaction is contained within the TX in.
Below you see an example transaction in which User A sends User B and User C funds in one transaction.
Example Tx
User A

User B
TXout

TXin

User C
TXout

TXout

Example transaction in a transaction-based ledger

The TXout sent back by User A to itself is required, as UTXOs cannot be spent partially. Therefore, theremaining funds of a UTXO need to be sent to a new UTXO, belonging to the same user. This UTXO is referred
to as a change address.
The transaction-based ledger allows a single user to execute multiple payments in a single transaction,
leading to a higher efficiency compared to creating a transaction for each user individually. Theoretically,
this is also possible for account-based ledgers, but comes with downsides5.
Therefore, we introduce a new metric for transaction-based Proof of Stake networks called marginal
power demand per UTXO per second. For the time period of our measurement, we additionally gather
UTXOs (excluding change addresses) and calculate the marginal power demand per UTXO per second
metric. The table below summarizes the results of our assessment.
Marginal Power Demand per UTXO per second [W]0.106

24h-analysis-period transaction count [#]53,507

TXout (excluding change addresses) [#]89,014

Average TXout per Transaction [#]Observed Block Range [#]1.66
10,227,951 – 10,232,272

Transaction-based ledger metrics

This metric provides an interesting perspective on the inner workings of transaction-based ledgers andits efficiencies. For the results, updated results published in CCRI’s API, and the proposed sustainability
indicators under MiCA, we adhere to the standard definition of a transaction.

4

UTXO refers to an unspent transaction output. We refer to transaction outputs irrespective of their spending as TX out.
In an account-based ledger, smart contracts and messages can be leveraged to execute multiple payments within one transaction
but are associated with additional costs and setup efforts.
5

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B. RESULTS
Cardano: Electricity Consumption and Carbon Footprint(all metrics as of April 25, 2024)Po

KEY NETWORK METRICS
Name
SymbolConsensus mechanism
Network type
Node count24h-analysis-period transaction count
Annualized transaction countCardanoADA
PoS (Ouroboros)
Layer 1
3,147 (1,049 SPOs with three devices each)
53,507
19,530,055

KEY FINANCIAL METRICS
Market Capitalization (Rank) [USD]Market Price [USD]

16,784,282,077.33 (#10, CoinMarketCap)
0.471

Circulating Supply [ADA]

35.633.902.492

24 hours Trading Volume [USD]

348,258,880.11

KEY ELECTRICITY METRICS
Average electrical power per node [W]

25.576

Electrical power of network [KW]

80.47

Annualized electricity consumption [MWh]704.91Marginal power consumption per TPS [W]0.191975

KEY CARBON METRICS
Annualized CO2 emissions [t]Marginal CO2 emissions per tx [g]Applied CO2 emission intensity [kg/kWh]6

250.730.000018987
0.356

Value for a representative node assuming the node distribution among hardware configurations as shown in the Appendix.

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C. METHODOLOGY FOR SUSTAINABILITY INDICATORSUNDER MICA
1 PREAMBLE
The Markets in Crypto-Assets (MiCA) Regulation entered into force in June 2023. Crypto-asset issuers aswell as service providers are required to disclose information on the principal adverse impacts on the
climate and other environment-related adverse impacts of the consensus mechanism used to issue therespective crypto-asset. The European Securities and Markets Authority (ESMA), which has been
mandated to develop draft regulatory standards related to sustainability disclosure, has proposed ten
mandatory climate and other environment-related indicators in their 2nd consultation package which
was released on 5th October 2023. The ten indicators cover the areas of energy, GHG emissions, waste
production, and natural resources.

Art 6 (1): Content and form of the crypto-asset white paper:(j) information on the principal adverse impacts on the climate and other environment-related adverse
impacts of the consensus mechanism used to issue the crypto-asset.
Market in Crypto-Assets Regulation (MiCA) for token issuers

Art 66 (5): Obligation for all crypto-asset service providers:(j) Crypto-asset service providers shall make publicly available, in a prominent place on their website,
information related to the principal adverse impacts on the climate and other environment-related
adverse impacts of the consensus mechanism used to issue each crypto-asset in relation to which they
provide services (...).
Market in Crypto-Assets Regulation (MiCA) for CASPs

CCRI has published a methodology document7 to assess the 10 mandatory sustainability indicators
as proposed by the ESMA in the second consultation package for any type of consensus mechanism,
on which the following sections build on. For the calculation of the indicators of the Cardano network,
we highlight all sources used in this document. Our live data feeds might use different sources in the
future. For an up-to-date list of sources, please refer to CCRI’s API documentation 8.

78

https://carbon-ratings.com
The documentation of CCRI’s API can be found here: https://docs.api.carbon-ratings.com

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2 INDICATOR OVERVIEW
The following table shows the ten sustainability indicators as proposed by the ESMA in the second
consultation package and their description.
Type

Adverse Sustainability
Indicator

MetricEnergyEnergy consumption

Total amount of energy used, expressed in kilowatt-hours (kWh) per
calendar year, for the validation of transactions and the maintenanceof the integrity of the distributed ledger of transactions

Non-renewable energy
consumption

Share of energy used generated from nonrenewable sources,
expressed as a percentage of the total amount of energy used per
calendar year, for the validation of transactions and the maintenanceof the integrity of the distributed ledger of transactions

Energy intensity

Average amount of energy used, in kWh, per validated transaction

Scope 1 - Controlled

Scope 1 GHG emissions, expressed in tonnes (t) carbon dioxide
equivalent (CO2e) per calendar year for the validation oftransactions and the maintenance of the integrity of the distributed
ledger of transactions

Scope 2 – Purchased

Scope 2 GHG emissions, expressed in tCO2e per calendar year forthe validation of transactions and the maintenance of the integrity ofthe distributed ledger of transactions

GHG intensity

Average GHG emissions (scope 1 and scope 2) per validated
transaction, expressed in kilogram (kg) CO2e per transaction (Tx)Generation of waste
electrical and electronicequipment (WEEE)

Total amount of WEEE generated for the validation of transactions
and the maintenance of the integrity of the distributed ledger oftransactions, expressed in tonnes per calendar year

Non-recycled WEEE ratio

Share of the total amount of WEEE generated for the validation oftransactions and the maintenance of the integrity of the distributed
ledger of transactions, not recycled per calendar year, expressed asa percentage

Generation of hazardous
waste

Total amount of hazardous waste generated for the validation oftransactions and the maintenance of the integrity of the distributed
ledger of transactions, expressed in tonnes per calendar year

Impact of the use of
equipment on natural
resources

Description of the impact on natural resources of the production, theuse and the disposal of the devices of the DLT network nodes

GHG
emissions

Waste
production

Naturalresources

Source: ESMA, Consultation Package 2, Annex II, Table 1

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3 ENERGY-RELATED SUSTAINABILITY INDICATORSThe first three indicators are energy consumption related. Indicator 1 captures the total energy used forthe validation of transactions and the maintenance of the integrity of the distributed ledger. Indicator 2
quantifies the non-renewable share, and Indicator 3 the per transaction energy usage.
We described the methodology for assessing the electricity consumption of the network in detail in
A. NETWORK ASSESSMENT METHODOLOGY. The results from this methodology can directly be used forindicators 1, 2, and 3.
4 GHG EMISSION RELATED SUSTAINABILITY INDICATORSTo derive the GHG emissions for the validation of transactions and the maintenance of the integrity of the
distributed, two components are required: (1) the energy consumption and (2) the emission intensity ofthe energy consumed. Both components have already been established and serves as a direct input forthis section.
Indicator 4 – Scope 1 - Controlled
For the fourth indicator, ESMA asks for scope 1 GHG emissions for the validation of transactions and themaintenance of the integrity of the distributed ledger of transactions. However, special attention needs tobe paid to the different scopes of the emissions.
The distinction of the emission in different scopes has been introduced by the GHG Protocol which provides
guides for carbon accounting at the corporate level 9. Scope 1 is defined as direct GHG emissions fromsources that are owned or controlled by the company. As a crypto-asset is not a company, the distinctionin emission scopes may seem somehow misleading in this context. We argue that a reasonableinterpretation would be to think of the GHG emissions that are owned or controlled by the ones who
validate transactions and maintain the integrity of the distributed ledger transactions (i.e., Staking Pool
Operators). As the GHG emissions for the validation of transactions and the maintenance of the integrityof the distributed ledger occur during the production of the electricity that is consumed, the GHG
emissions would only be owned or controlled by the validators in case they are producing the electricity
themselves. Given the amount of energy required for running an SPO, we would argue that it should beassumed that SPOs are purchasing the electricity they use (which represents scope 2 – see indicator 5),
unless there is clear evidence that a power plant is owned or controlled by the validator itself. The
associated emissions would then be calculated by taking the electricity consumed by the owned orcontrolled power plant and multiplying it by the emission intensity of the respective plant (i.e., largely
driven by the type of power plant, for example solar PV vs. wind. vs. gas). As the MiCA regulation foresees
sustainability disclosures on the level of a crypto-asset and not on company-level, any information on
potentially independently operated or controlled power plants must be taken from public reports fromvalidators.
We are not aware of any SPOs running their own power plants for their operations; therefore we assume
zero Scope 1 emissions.

9

https://ghgprotocol.org/sites/default/files/standards/ghg-protocol-revised.pdf.

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Indicator 5 – Scope 2 - Purchased
For the fifth indicator, ESMA asks for scope 2 GHG emissions for the validation of transactions and themaintenance of the integrity of the distributed ledger of transactions. Scope 2 is defined as indirect GHG
emissions from emissions from the generation of acquired and consumed electricity 10.

In line withindicator 4, we would argue that a reasonable interpretation would be to think of the indirect GHG
emissions of the acquired and consumed electricity of SPOs. Similar to most other industries, we would
argue that the majority of the validators purchase the electricity they consume rather than producing itthemselves. The GHG Protocol presents two complementary methods to report scope 2 emissions:•

Location-based method: It reflects the average emissions intensity of grids on which energy
consumption occurs (using mostly grid-average emission factor data). Therefore, the method
requires the amount of electricity consumed at each location as well as the respective gridaverage emission factors which are often published by state authorities (e.g., by the United States
Environmental Protection Agency for U.S. states).

•Market-based method: It reflects emissions from electricity that companies have purposefully
chosen (or their lack of choice). It derives emission factors from contractual instruments, which
include any type of contract between two parties for the sale and purchase of energy bundled withattributes about the energy generation, or for unbundled attribute claims. As such, the marketbased method does not only require information on the contractual instrument used (as well as
associated credible claims) but also emission factors representing the untracked or unclaimed
energy and emissions (termed the “residual mix”) for the share of electricity for which there is nocontractual information that meets the Scope 2 Quality Criteria.

The GHG Protocol requires both methods to be reported separately if one decides to start calculating
scope 2 emissions with the market-based method (termed “dual reporting”).
For the Cardano network, we use only the location-based method to report scope 2 emissions as
detailed information on renewable energy claims is currently unavailable to calculate market-based
scope 2 emissions and perform “dual reporting”.

Indicator 6 – GHG intensity
For the sixth indicator, ESMA asks for the average GHG emissions (scope 1 and scope 2) per validated
transaction. This metric has already been derived in A. NETWORK ASSESSMENT METHODOLOGY.
5 WASTE PRODUCTION RELATED SUSTAINABILITY INDICATORS
Similar to the energy-related indicators, the first waste production related indicator captures the total
amount of electrical and electronic equipment waste for the validation of transactions and themaintenance of the integrity of the distributed ledger. Indicator 8 then quantifies the non-recycled share,
and Indicator 9 the hazardous waste fraction. Further details are provided below for each of the indicators.

10

https://ghgprotocol.org/sites/default/files/2023-03/Scope%202%20Guidance.pdf.

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Indicator 7 – Generation of waste electrical and electronic equipment (WEEE)
For the seventh indicator, ESMA asks for the total generation of waste electrical and electronic equipment.
The generation of electronic waste is dependent on the hardware usage of the network and how fastdevices are replaced – either because of hardware depreciation, performance issues, or implications on
the revenue. Depending on the specifics of the hardware replacement, the calculation of the total
electronic waste can be conducted in two consecutive steps:
1.

Understand the hardware composition and weight of devices: For the electricity consumption of
the Cardano network, we made assumptions on the hardware composition, both on device typeand distribution between devices. This hardware composition provides a solid basis for the
calculation of the number of total devices in the network. We collected the respective hardware
weights.

2.

Define the depreciation time frame: If we know the hardware composition for a given day in the
network, we are able to calculate the WEEE generated on that day with a given depreciation timeframe.11 As previously mentioned, deciding on the time frame can be complex and research in the
regard of depreciation is sparse.

For the depreciation time frame of Cardano, we observe that the Infrastructure Survey 2024 conducted by
the Cardano Foundation draws a mixed picture of depreciation times, ranging from three to five years. To
be on the conservative side of an estimate, we assume a hardware depreciation of three years.
Indicator 8 – Non-recycled WEEE ratio
For the eighth indicator, ESMA asks for the share of non-recycled WEEE. To calculate this metric, one needs
to consider the location of validators as well as the local recycling rates for WEEE at the respective
locations. Similarly, to energy sources and emission factors by country or region, local recycling rates canbe obtained from state authorities or research institutions specialized in the field (e.g., United Nations
Institute for Training and Research (UNITAR), UNU-ViE Sustainable Cycles (SCYCLE), The International
Telecommunication Union (ITU) jointly publishes reports monitoring e-waste production and recycling).
Indicator 9 – Generation of hazardous waste
For the ninth indicator, ESMA asks for the hazardous waste generated by the network. As we calculated thewaste component of the network already in indicator 7, we are able to build upon that figure and calculate
the hazardous waste as a share of the total electronic waste and provide a respective value expressed intonnes per calendar year.
Hazardous waste is a term that is linked to European Union Guidelines “Waste Electrical and Electronic
Equipment Directive (WEEE Directive)” (2012/19/EU) and Restriction of Hazardous Substances Directive(2011/65/EU, RoHS 2) which properly defines contents of electronic devices as hazardous substances, suchas lead, mercury, cadmium, and others.
The calculation of the share of the hazardous substances is merely a question of proper data sources anddiligence. Ideally, for every device considered in the hardware composition described in Indicator 7, one
11

Example: If a hardware device is deprecated over 5 years, then the generated WEEE of devices is calculated by the devices'weight divided by the total days the devices are in use (5 years = 1,825 days).
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obtains a “Restriction of Hazardous Substances Directive Report” (RoHS Report), which needs to be
published by respective vendors. The contents of these documents need to be summed up for eachdevice. With that information and the hardware depreciation, one is able to calculate the total hazardous
waste generated by the network. If the ROHS Report is not available for every type of device in the network,
similar device types for which the report is available can be used as a proxy.

6 NATURAL RESOURCES RELATED SUSTAINABILITY INDICATORSThe last category aims to capture lifecycle impacts on natural resources beyond the aspects capturedby the previous indicators. For the tenth indicator, ESMA asks for a description of the impact on natural
resources of the production, the use and the disposal of the devices of the DLT network nodes. While ESMAasks for very concrete metrics for the other indicators by defining exact time periods and units, thisindicator is only loosely defined as of now. Thus, there is reason to assume that this indicator will be moreclosely defined as the ESMA publishes its final requirements for mandatory indicators (expected by theend of June 2024).
Indicator 10 – Impact of the use of equipment on natural resources
For the tenth indicator, we provide a description of the general impact of the devices of DLT network nodes
on natural resources, such as water, fossil fuels, and critical raw materials during the production, use, anddisposal phase. Particularly, water consumption during the use phase has already been discussed in thecontext of Bitcoin. Water consumption is heavily driven by the amount of energy consumed by the network
as well as the regional water intensity of the electricity consumption. Thus, the electricity consumption, thelocation of validators as well as regional electricity water footprint may serve as an input to assess thewater consumption of a crypto-asset during the use phase following the approach which is taken byresearch papers investigating the water consumption of Bitcoin12.

12

de Vries, Alex. "Bitcoin’s growing water footprint." Cell Reports Sustainability 1.1 (2024).

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D. MiCA INIDCATORSThe following table shows the 10 mandatory sustainability indicators as proposed by the ESMA in thesecond consultation package published in October 2023, annualized as of the measurement date. The
indicators will be updated once the ESMA publishes the final regulatory requirements.

Type

Adverse Sustainability Indicator

EnergyEnergy consumption
Non-renewable energy consumption
Energy intensity

GHG emissions

69.12 %
0.000168 kWh0t

Scope 2 – Purchased

244.448 t

Generation of waste electrical and electronic equipment(WEEE)
Non-recycled WEEE ratio
Generation of hazardous waste

Natural resources

687,238.04 kWh

Scope 1 - Controlled

GHG intensity
Waste production

Results

Impact of the use of equipment on natural resources

0.0000597 kg
8.26 t51.93 %
0.004237 t
Textualdescription13

Cardano MiCA Sustainability Indicators, CC-BY 4.0 License

Natural resources may include water usage, fossil fuels, or critical raw materials. Water usage is relevant for data center operations
directly for cooling and indirectly through electricity consumption which is not based on wind or solar (Mytton 2021). Consequently,
electricity consumed which is not based on wind or solar may also cause water usage during the production and disposal of
hardware. Similarly, fossil fuel usage is relevant for the production, use and the disposal of hardware whenevery electricity is usedsince electricity consumption from fossil fuels still accounts for over 60% of global electricity production (IEA 2023). Critical raw
materials are specifically relevant in the production of hardware as electrical and electrical and electronic equipment typicallydepend on technology metals that are classified as critical (Chancerel et al 2015). Extensive data collection is required to quantify
the impact on water usage, fossil fuel usage, and critical raw materials of the devices of DLT network nodes. Thus, the impact onnatural resources, such as water, fossil fuels, and critical raw materials of the production, the use and the disposal of the devices of
the DLT network nodes is influenced by the amount of energy consumed, by the type of sources used to generate electricity and by
the amount of hardware required by the network. For instance, the water consumption during the use phase of the Cardano network
amounts to 3,646.37 kiloliters.
13

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Appendix
HARDWARE-SPECIFIC MEASUREMENT RESULTS
Hardware1

configuration
CPU
Ram
Storage
Configurations
selected
Mean electrical
power in idle

Broadcom
BCM27112Intel i3-8109U

3

4Intel i5-Intel i5-

8400T

1135G7

5

6Intel i5-10400

AMD 3970X

8 GB

8 GB

8 GB

16 GB

64 GB

256 GB

128 GB SD

512 GB SSD

256 GB SSD

2 TB SSD

2 TB SSD

2 TB SSD

no

no

No

yes

yes

yes

3.031

2.688

2.893

3.675

25.30480.464

-

-

-

5.313

26.912110.668---

45.0 %

45.0 %

10.0 %[W]
Mean electrical
power of node
[W]
Assumed node
distribution
Measurement
periodSoftwareversion2024-04-24 13:57:14. 752 CET to 2024-04-25 13:57:15.346 CET
cardano-node 8.9.2, cardano-cli 8.20.3.0

MICA INDICATORS-RELATED SOURCES•

Indicator 2: We use electricity generation mix data from IRENA (2023), Renewable energy statistics
2023, International Renewable Energy Agency, Abu Dhabi.

•

Indicator 5: We use emission factors from the Environmental Protection Agency for U.S. states, fromthe Environmental Energy Agency for European countries and from Climate Transparency for allother G20 countries.

•

Indicator 8: We use e-waste recycling rates from The Global E-waste Monitor 2024 by UNI/UNITAR
and ITU.

•

Indicator 10: We build on the methodology and data presented in de Vries, A. (2024). Bitcoin’s
growing water footprint. Cell Reports Sustainability, 1(1). to derive the water consumption of the usephase for the Cardano network. In the description, we refer to following citations:oMytton, D. (2021). Data centre water consumption. npj Clean Water, 4(1), 11.

o

IEA (2023). Electricity. https://www.iea.org/energy-system/electricity#sources-ofelectricityoChancerel, P., Marwede, M., Nissen, N. F., & Lang, K. D. (2015). Estimating the quantities ofcritical metals embedded in ICT and consumer equipment. Resources, conservation andrecycling, 98, 9-18.

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About CCRI

CCRI – Crypto Carbon Ratings Institute – is a research-driven
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blockchainonandsustainability
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The

interdisciplinary team has built a multi-year research track record
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methods based on formerly peer-reviewed studies published inrenowned scientific journals. CCRI provides insights that help theirclients to understand and manage crypto-related ESG exposure. They
serve a broad range of clients including institutional investors,
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© Crypto Carbon Ratings Institute, 2024
Crypto Carbon Ratings Institute (CCRI) is a brand of CCRI GmbH based in Dingolfing, Germany.

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