OCTREPORT

Energy Efficiency andCarbon Footprint of
PoS Blockchain Networks and Platforms

PoS Benchmark Study 2023

© Crypto Carbon Ratings Institute, 2023
Crypto Carbon Ratings Institute (CCRI) is a brand of CCRI GmbH based in Dingolfing, Germany.

https://carbon-ratings.com

Energy Efficiency and Carbon Footprint of PoS Blockchain Networks and Platforms

PoS Benchmark Study 2023:
Energy Efficiency and Carbon Footprint of PoS
Blockchain Networks and Platforms
October 2023
CCRI
carbon-ratings.com

Executive summary•

Proof of Stake (PoS) protocols are more energy-efficient compared to Proof of Work (PoW)-based
networks such as Bitcoin.

•

This report assesses the electricity consumption and carbon footprint of the PoS protocols Algorand,
Avalanche, Cardano, Cosmos, Ethereum, Polkadot, and Solana.

•

For the networks assessed in this study, we find a variance in total annualized electricity
consumption of 37x - ranging from 154,202.1 kWh to 5,750,351.6 kWh.

•Electricity consumption can be translated into carbon emissions via emission factors of electricity
generation to gauge climate impacts, taking the node locations into account.

•

For the networks assessed in this study, we find a variance in total annualized carbon footprint- ranging from 70.8 tCO2e to 2,088.4 tCO2e.

•

The result ranges of the networks assessed equal about the annual electricity consumption of
15 to 542 U.S. households and carbon emissions of 12 to 342 roundtrip flights from MUC to SFO.

•

The marginal power demand per TPS (transactions per second) within the assessed blockchain
networks ranges from 0.01 W to 0.81 W.

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Energy Efficiency and Carbon Footprint of PoS Blockchain Networks and Platforms

1

Introduction

The electricity consumption and related carbon footprint of cryptocurrencies remain subject to extensive
discussion in public, academia, and industry. Various estimations exist, for instance, comparing Bitcoin's
carbon footprint to mid-sized countries (CBECI, 2023; De Vries, Gallersdörfer, Klaaßen, & Stoll, 2022). The
consensus family of Proof of Stake (PoS) is superior regarding the electricity requirements compared to the
Proof of Work (PoW) consensus mechanisms as applied, for example, by Bitcoin. Yet electricity requirements
of individual PoS systems may differ significantly.
Instead of requiring computational power to solve mining puzzles for securing PoW networks, PoS networks
require validators to lock in funds to propose or vote on new blocks. Due to the nature of the software
engineering process and network architectures, PoS networks differ in terms of hardware requirements,
programming language, network size, transaction throughput, and transaction complexity. Besides nodecount and transaction volume, these factors influence the network electricity consumption and carbon
footprint.
Despite the relatively low absolute electricity consumption and carbon emission level of PoS networks andplatforms benchmarked in this study, it remains essential to continue monitoring and disclosing energy
efficiency and carbon emissions of PoS chains. Key reasons to do so include:•

Transparency: It is vital to inform users, regulators, policymakers, and the public about the climate
costs and benefits of blockchain solutions.

•Differentiator: Sustainability indicators are a decision criterion for some developers, corporates, NFT
creators, etc. to select an energy efficient chain.

•High hardware requirements: Carbon footprint calculations are the essential data basis to reachcorporate climate goals via energy consumption reduction strategies and/or carbon offsetting.

•

Regulation: Compliance with upcoming regulatory requirements entails disclosure ofclimate impact.

Compared to last year’s CCRI PoS Benchmark study, we have developed a methodology to assess Avalanche
Subnets, the Cosmos Ecosystem, and Polkadot Parachains. The results of the PoS Benchmark Study 2023 aresummarized in Figure 1.

Figure 1: Total annualized network carbon emissions determined by 2023 PoS Benchmarking Study.

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2

Aim and scopeThis report aims to provide insights into the electricity consumption and carbon footprint of PoS networks. It isnoteworthy that the approach applied in this report is a helpful tool to benchmark the total electricity
consumption and the carbon emissions as well as the relative performance, however, any PoS network is
associated with uncertainties that impede deriving exact numbers. Factors such as network size, varying
hardware configurations, and network infrastructure influence the results. Nonetheless, we deem this reportto produce precise electricity consumption and carbon footprint estimates for the analyzed systems, as wemeasure the electricity consumption of single hardware components and use those as a proxy for the overall
network or platform of networks. The establishment of representative hardware, network sizes, and electricity
measurements form the basis for future research, such as comparing further networks and their respective
requirements and properties. In our analysis, we investigate the PoS networks Algorand, Cardano, Ethereumand Solana, as well as the network platforms Avalanche, Cosmos and Polkadot. For platform networks, we
differentiate between the network base layer and side chains. Network characteristics are summarized inTable 1.

AlgorandAvalanche-Mainnet(platform base layer)

Avalanche-DFK(platform upper bound)Avalanche-DOS(platform lower bound)

CardanoCosmos-Hub(platform base layer)

Cosmos-Injective(platform upper bound)

Cosmos-Bitsong(platform lower bound)

Ethereum
Polkadot-Relaychain(platform base layer)

Polkadot -Astar(platform upper bound)

Polkadot -Composable(platform lower bound)

SolanaSymbolRank1

Market capitalization [$]

ALGO

42

1,088,622,236

AVAX

16

4,941,980,762

JEWEL

823

8,693,655

DOS

-

-

ADA

7

13,001,485,360

ATOM

19

3,662,295,329

INJ

66

652,961,622

BTSG

5741

1,189,280

ETH2

227,212,821,253

DOT

13

6,269,855,108

ASTR

136

220,657,549

LAYR

-

-

SOL

10

220,657,549

Table 1: Key figures of measured networks. Values are taken from
Coinmarketcap.com as of June 02, 2023.

1

Rank in regard of market capitalization.

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3

Methodology

Our methodology builds upon five steps to generate data on the electricity consumption and carbon footprintof a PoS system. Moreover, we develop metrics to enable a valid comparison between other PoS systems.
Further details regarding our methodology can be found in (CCRI, 2023a).
In the first step, we analyze the selected PoS networks and their minimum hardware requirements. The
hardware requirements are an indicator of the hardware composition of the network. We use this information
and additional hardware data from PassMark (Passmark Software, 2021) to select and obtain hardware that
we use to measure a single node's electricity consumption.
In the second step, we measure the electricity consumption of a single node and provide upper and lower
bounds for each network. We start by running the software required by the respective network on all selected
hardware devices and measure their electricity consumption while running the network and while idling. We
also capture further data points, such as CPU utilization and processed blocks, to be able to evaluate
additional metrics.
In the third step, we estimate the electricity consumption of the entire networks. Firstly, we collect information
about the size of the network, as the node count significantly influences the total amount of electricity
consumed. Secondly, we develop a weighting between the single hardware devices for each network. Lastly,
we multiply the electricity consumption of the weighted nodes by the number of nodes in the network.
In the fourth step, 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 take samples of thenodes’ electricity consumption periodically and examine the number of transactions that the single nodes
handled during the respective time periods. This allows us to describe the marginal influence of the numberof transactions on the electricity consumption of a node in a specific network. As a result, we establish a model
to estimate a node’s power consumption based on the number of transactions. This also enables us to trackthe electricity consumption of a network over time, as node count and transaction volume change.
In the fifth step, we estimate the CO2 emissions arising from the operation of the PoS networks. To do so, we
use our data on network electricity consumption and multiply it by carbon emission factors. In case the
distribution of nodes in a network is available, we use the respective carbon intensity factors of the regions
where the nodes are located to calculate the network’s carbon footprint. Otherwise, we rely on an average
global carbon intensity factor. For each network, we provide a best guess as well as an upper and a lower
bound for the carbon footprint.
Note: To analyze the electricity consumption and carbon footprint of platform networks such as Avalanche,
Cosmos, and Polkadot, we expand the above-introduced methodology as those networks consist of a baselayer and side chains: For each platform, we first select a side chain that has a rather low transaction volume
as a lower bound reference, and one with a high transaction volume as an upper bound reference for theelectricity consumption of blockchains within the platform. We adhere to our standard approach to measure
the electricity consumption of these two reference bounds. By relying on a linear approximation, we candetermine the electricity consumption for networks of arbitrary transaction volumes within the corresponding
platform. Since there are technical differences in how the platform networks are designed, we tailor our
methodology for each platform network. For example, Avalanche requires all validators of Subnets to also
participate in the base layer (Mainnet), which is not the case for the Cosmos ecosystem. The exact procedure
we apply for each platform is described in Chapter 5.4.2.

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4

Hardware requirements and test environment

In this chapter, we first establish a hardware pool for carrying out analyses of PoS networks. Secondly, wesummarize the hardware requirements for a node participating in a respective network. For each network inscope of this study, we derive a hardware selection that satisfies the network-specific requirements. Thirdly,
we provide details of the infrastructure required to measure the nodes’ electricity consumption and describe
our test environment.

4.1

Hardware selection

For analyses of PoS networks, we generally define three different categories of hardware requirements fornodes participating in a network:•

Low hardware requirements: For PoS networks with rather low hardware requirements, we assume
that computational power is not a concern for the systems, and users should be comfortable running
the software on any system they have available. Typically, such networks recommend using lowenergy hardware for running nodes, as for example the well-known Raspberry Pi. In today's average
consumer desktop PC, 4-8 GB RAM and 200 GB of storage (even an SSD) are not uncommon
anymore.

•Specific hardware requirements: Some networks specify quite precise hardware requirements, forinstance stating the exact CPU type as well as RAM and storage. For such networks, we normally aim
for using hardware that satisfies the requirements, but we also test hardware that does not meet therecommendations if they are able to run a node reliably and include these tests in our calculation.
Nonetheless, hardware requirements typically give users who intend to run a node an indication
about what to expect regarding demand, influencing their final choice of hardware.

•High hardware requirements: Some few PoS systems exhibit surprisingly high hardware
requirements. The CPU, RAM, and storage requirements can be at the highest level of standard
desktop computers (besides servers). Graphic cards can be required in such networks, which hints at
the immense processing power required.

We define a hardware pool that covers the above-mentioned categories in order to ensure a high degree of
hardware diversity. For the analysis of specific networks, it is important to decide on a case-by-case basis
which hardware configurations to use. Based on the hardware requirements, both an upper and a lower
bound of hardware are evident.
For the lower bound, we select a Raspberry Pi 4 Model B with 8 GB RAM and 128 GB SD-card given that the
popularity of the Raspberry Pi computers is high within all communities. We opt for an official Raspberry Pi fullkit, including fan and power supply.
As an upper bound, we opt for an average system within the Threadripper specifications consisting of an AMD
Ryzen Threadripper 3970X, 32C/64T, 256GB RAM (DDR4-3600), a MSI GeForce RTX 4090 graphics card, and a
Samsung 970 Evo Plus 2TB in order to address high hardware requirements. As the processor does not havean onboard graphics processor, we need a graphics card. We select an appropriate mainboard as well as a
power supply.
The upper and lower bounds highly deviate from each other in terms of computational power and electricity
consumption. Further, the two computers may not capture the complete picture of the hardware used within

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Energy Efficiency and Carbon Footprint of PoS Blockchain Networks and Platforms

networks to be analyzed. Therefore, we decided to add four additional computers to ensure a well-balanced
set of hardware for electricity consumption measurements.
As there are millions of different computer configurations, thousands of variables, and other factors that
influence the electricity consumption of devices, we opt for one key variable and derive other specificationsof the system from it: The central processing unit (CPU). Nonetheless, the CPU also has several variables suchas the number of cores, threads, speed, turbo speed, thermal design power (TDP), and others. Further, identicalvariables do not necessarily lead to the same computational power or electricity consumption. To get an indepth view and understanding of the CPU landscape, we obtain a data set from PassMark. PassMark provides
a software suite able to benchmark varying types of hardware, including CPUs. The obtained data set contains
over 3,100 CPU models as well as over 1 million results of their benchmarking suite (Passmark Software, 2021).
Based on this data set, we select four CPUs to derive our final configurations. We thereby aim at three
categories of performance (high, mid, and low) and select one or more CPUs with the average efficiency for
their class. A detailed description of our approach to select CPUs can be found in Appendix A.
For the high-tier (configuration 5), we identified the Intel Core i5-10400F as being closest to the average
efficiency. As Intel's F-models only have a deactivated onboard graphics chip, we decided to opt for the nonF variant, as otherwise, a dedicated GPU would add unnecessary electricity consumption to the system. The
non-F variant is almost identical to the F variant regards to benchmarking results. We opted for 64 GB DDR4
RAM and a Samsung 970 Evo Plus 2 TB NVMe SSD to complement the system. Mainboard, power supply unit,
and case have been selected appropriately.
Regarding the mid-tier section, we have extended our hardware selection with an additional devicecompared to our previous measurements (CCRI, 2022a), as we assume that most standard users apply
hardware from this range. Since the Intel NUC series is becoming increasingly popular for running blockchain
nodes, we decided on an Intel NUC with medium equipment (configuration 4). We chose an Intel Core i51135G7 laptop processor with included graphics chip, which represents the upper mid-range of typically useddevices quite well. This additional mid-tier computer is equipped with a 32 GB DDR4 RAM and a 2 TB NVMe
SSD. Furthermore, we still stick to the Intel Core i5-8400T since it has the best fit for the average electricity
consumption in the mid-tier section (configuration 3). The T-model means the CPU has a "power-optimized
lifestyle", resulting in lower performance and less electricity consumption. We could not directly obtain the
CPU in the market and instead opted for a completed build: The Lenovo ThinkCentre M720q Tiny 10T8S3KD00.
Besides the processor as mentioned above, it includes a 256 GB NVMe SSD as well as 8 GB RAM.
In the low-tier section (configuration 2), we identify the Intel Core i3-8109U as the processor with an averageenergy efficiency for its class. The U-label refers to a "Mobile power-efficient" CPU but is nonetheless includedin MiniPCs. To our knowledge, this CPU was never sold separately on the consumer market but is available inIntel's NUC series. We obtain the Intel NUC Kit NUC8i3BEK2 Barebone and augment it with the Samsung 970 Evo
Plus 512 GB NVMe SSD as well as 8 GB RAM.
Compared to our PoS network benchmarking of last year (CCRI, 2022a), we largely stick to the same hardware
selection. However, some adjustments have been conducted to better fit evolving hardware requirements.
Besides including a further mid-tier setup into the hardware pool with configuration 4, configuration 5 wasenhanced regarding both RAM and storage capacity ensuring a significant difference to the new
configuration 4 not only in terms of the CPU. Configuration 6 was equipped with a graphics card.
We consider our selection as representative to provide a balanced set of hardware for electricity
measurements with these six computers. As an operating system, we use for all our devices Ubuntu Server
20.04, except for configuration 5. Due to driver issues, we had to opt for Ubuntu Server 21.

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Energy Efficiency and Carbon Footprint of PoS Blockchain Networks and Platforms

Table 2 displays an overview of the hardware configurations just introduced. Other factors than CPU are also
relevant for the electricity consumption of the systems. Nonetheless, this set of hardware yields a broad
overview of used hardware within such networks.
1
Broadcom

CPU

BCM2711

2

3

4

5

Intel i3-8109U Intel i5-8400T Intel i5-1135G7 Intel i5-10400

6
AMD 3970X

Cores/Threads

4/4

2/4

6/6

4/8

6/12

32/64

ArchitectureARM

x86/x64x86/x64x86/x64x86/x64x86/x64

RAM

8 GB

8 GB

8 GB

16 GB

64 GB

256 GB

Storage

128 GB SD

512 GB SSD

256 GB SSD

2 TB SSD

2 TB SSD

2 TB SSD

GPU

OnboardOnboardOnboardOnboardOnboard

PSU

USB-C

65 Watt

65 Watt

65 Watt

650 Watt

1000 Watt

Case

Integrated

Integrated

Integrated

Integrated

CustomCustomOS

Ubuntu 20.04 Ubuntu 20.04

Ubuntu 20.04

Ubuntu 20.04

Ubuntu 21

Ubuntu 20.04

MSI GeForce
RTX 4090

Table 2: Overview of selected hardware configurations from lowest to highest requirement.

4.2 Hardware requirements of selected PoS networks
The PoS systems to be analyzed reveal different requirements for the hardware of their network participants.
While some networks are precise regarding their minimal or recommended hardware requirements, others
are partly unspecific: For the Cosmos-Hub network, as an example, no information on the CPU required to run
their software is specified. Solana has the highest requirements in our sample, requiring 12 cores / 24 threads,
128 GB RAM, and a 2 TB NVMe SSD. Solana also recommends a specific CPU type (AMD Threadripper Zen3) andstates that a node might profit from using a GPU in future. It is noteworthy that some networks (e.g., Polkadot
or Cosmos-Hub) do not provide minimum hardware requirements but rather link to a recommended setup.
Furthermore, there are PoS networks that introduce different roles among their nodes, each executing varying
tasks. These roles can lead to divergent hardware requirements within the network. In the Algorand network,
for instance, a node can assume the role of a relay node (i.e., it is responsible for communication routing), a
participation node (i.e., the node participates in the consensus mechanism), an archival node (i.e., the nodestores the entire leger), or various combinations of these roles. In such cases, we apply the hardware
requirements of a role that most closely resembles a normal full node of a standard PoS network.
Table 3 summarizes the hardware requirements at the time of our analysis (April 2023) for executing a nodeparticipating the selected PoS networks.

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Energy Efficiency and Carbon Footprint of PoS Blockchain Networks and Platforms

AlgorandAvalanche(all platform chains)

CPU

RAM

Storage

SSD/NVMe

Source> 8x v> 16 GB> 100 GB

NVMe

(Algorand,2023)> 8x v (AWS)> 16 GiB> 1 TiB

N/A> 2x1.6GHz> 16 GB> 75 GB

N/A

N/A

32 GB

> 500 GB

N/A

(Tendermint,
2023a)> 4x2GHz v> 32 GB> 1 TB

SSD(Injective Labs,
2023)

N/A

N/A

N/A

N/A

(BitSong, 2023)(Ava Labs,
2023a)(Cardano

CardanoCosmos-Hub(platform base layer)

Cosmos-Injective(platform upper bound)

Cosmos-Bitsong(platform lower bound)

Ethereum

Different configurations of client software possible, see detailed analysis in (CCRI, 2022b)

Polkadot-Relaychain(platform base layer)

Polkadot-Astar(platform upper bound)

Polkadot-Composable(platform lower bound)Foundation,
2023)

4x3.4GHz

32 GB

1 TB

NVMe

(Web3
Foundation,
2023b)

8x

16 GB

500 GB

NVMe(AstarDevelopers
Hub, 2023)> 2x> 6 GB> 600 GB

N/A

(Composable
Finance, 2023)

12x2.8GHz> 128 GB

1 TB + 500 GB

NVMe(Solana

SolanaFoundation,
2023)

Table 3: Hardware requirements of the selected PoS networks. Minimal requirements aremarked with a “>” sign. CPUs labeled with “v” denote virtual CPUs.
Applying the networks’ requirements to execute full nodes to our hardware pool presented in the previous
section, we deduce that configuration 6 as shown in Table 2 exceeds the hardware recommendations for all
clients listed. Thus, this hardware configuration is included into all measurements.
Apart from Solana, configuration 5 likewise meets all requirements and is therefore included in all the othermeasurements. Since we do not enforce hardware requirements to be a strict lower bound, we also include
configurations into a network’s measurements that are slightly lower than the official requirements. For thisreason, configuration 4 is involved in all experiments apart from Solana. Nodes representative for the low-tiercategory were only capable to run client software to measure a lower bound, an upper bound, and the baselayer for the platform networks Polkadot and Cosmos. It is noteworthy that software might not be available
for configuration 1 due to the Raspberry Pi’s architecture.
Table 4 summaries which configurations of our hardware pool were included as a foundation to derive theelectricity consumption of the different PoS networks selected.

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Energy Efficiency and Carbon Footprint of PoS Blockchain Networks and Platforms

Config 1

Config 2

Config 3

Config 4

Config 5

Config 6

Algorand

✗

✓

✓

✓

✓

✓

Avalanche-Mainnet

✗

✗

✗

✓

✓

✓

✗

✗

✗

✓

✓

✓

✗

✗

✗

✓

✓

✓

Cardano

✗

✗

✗

✓

✓

✓

Cosmos-Hub

✗

✓

✓

✓

✓

✓

✗

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

Ethereum

✗

✗

✗

✓

✓

✓

Polkadot-Relaychain

✗

✓

✓

✓

✓

✓

✗

✓

✓

✓

✓

✓

✗

✓

✓

✓

✓

✓

✗

✗

✗

✗

✗

✓

(platform base layer)

Avalanche-DFK(platform upper bound)Avalanche-DOS(platform lower bound)(platform base layer)

Cosmos-Injective(platform upper bound)

Cosmos-Bitsong(platform lower bound)(platform base layer)

Polkadot-Astar(platform upper bound)

Polkadot-Composable(platform lower bound)

SolanaTable 4: Overview of configurations out of our hardware poolselected for the different PoS networks selected.

4.3 Infrastructure for electricity measurements
For the measurement of the electricity consumption, we use a Mystrom WiFi Switch for each computer. These
switches measure the electricity consumption as well the room temperature and provide the values over a
REST interface. The electricity measurements are made in Munich, Germany in a separate server room withnear-constant room temperature.
All devices were equipped with the same software, a new Ubuntu server 20.04/21 installation, and themonitoring tool Glances (Hennion, 2021) that allows us to collect additional system information such astemperature or system load during the experiment.
A separate Raspberry Pi, equipped with a Python script, collected and monitored the systems during executing
the full nodes and analyzed the data generated during the runs. All computers are only connected to thepower outlet and LAN. All systems share an internet connection with 350 Mbit/s download and 110 MBit/s
upload.

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5

Benchmarking Results: Electricity consumption and carbon footprint

The hardware selection as described in the previous chapter allows us to establish single node electricity
measurements. With these measurements, we provide upper and lower bounds for the electricity
consumption of a single node running a specific network, and a best guess as a weighted average between
the selected hardware devices for the network. On that basis, we calculate the overall electricity consumptionof the respective PoS network or platform. Furthermore, we discuss additional metrics such as the electricity
consumption of a node assuming the network is running but no transactions are executed, and the marginal
power demand per transactions per second.

5.1

Single node electricity measurements

After defining and obtaining the hardware required for our analysis, we set up the hardware and install thenode software for each PoS network to be measured. We use the following process:•Hardware setup:
We install the node with the respective Linux version, configure Glances and configure remote access.

•Idle measurement:
We run the idle measurement for the devices without any additional software installed.

•

Node setup:
We download and install the software necessary for executing a specific PoS network and verify thecorrect installation.

•

Node bootstrap:
On each node, we run the respective software and wait for the synchronization to be completed since
we do not want to skew the electricity consumption of the devices during the bootstrapping phase.

•

Electricity measurement:
We shut down the nodes, start the electricity measurement and then start the nodes again. The nodes
run for 24 hours executing the respective network’s client software, as this covers an entire day cycle.
Appendix B contains an overview summarizing the result of every electricity measurement for eachnode and network.

To understand what exactly we are measuring, we need to describe a regular PoS network and its setup. In
addition, we need to investigate the architecture of PoS platform networks.
A PoS network consists of nodes, either validators (participating in the consensus protocol and producing new
blocks) or regular full nodes (broadcasting and verifying regular transactions). Validators are selected topropose new blocks based on their stake. We would differentiate between full nodes and validators in an ideal
setup, as they have slightly different roles and responsibilities within the network, however, on the Main
networks, usually significant stakes are required to run a validator. Furthermore, previous research suggeststhat participating in the PoS consensus mechanism has only a negligible effect on the device's electricity
consumption (Sedlmeir, Buhl, Fridgen, & Keller, 2020). Therefore, we conduct our electricity measurement on
regular full nodes running on the main networks of the selected PoS systems.
A platform network such as Avalanche, Cosmos, or Polkadot consists of multiple PoS networks that are
mutually linked via inter-blockchain communication protocols. The core of the platform is formed by a base

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Energy Efficiency and Carbon Footprint of PoS Blockchain Networks and Platforms

layer, i.e., a main network of the system, which is commonly responsible for managing the different networksof the platform and to allow for communication and transfers of assets between them. The terminology usedamong the different platforms is not standardized: In case of Avalanche, this base layer is referred to as the
Avalanche-Mainnet, for Cosmos, it is called Cosmos-Hub, and for Polkadot, it is named the PolkadotRelaychain. All other networks of the Avalanche network are denoted as Subnets, those of the Cosmos
platform as Zones, and those of the Polkadot platform as Parachains. Moreover, the role of validators withinthese platforms varies. For instance, validators on the Cosmos platform can join an arbitrary number of zones.
Unlike Avalanche, for which each validator of a Subnet also needs to join the Avalanche-Mainnet, validatorsof a particular Cosmos Zone do not necessarily have to participate in the base layer network. However, on
Polkadot, the individual networks have their distinct set of validators, although validators of the Relaychain
share responsibilities for the various Parachains among each other in order to enable communication acrossthe different Polkadot networks.

5.1.1

Electricity consumption in idle state

We measure the electricity consumption of the devices idle. Table 5 depicts the minimum, maximum, median,mean, and the first and third quartile of the electricity consumption for 24 hours. All values are rounded to two
decimals. Interestingly, setups 2 and 3 consume less electricity than the Raspberry Pi (configuration 1), which
we deemed the most energy-efficient solution beforehand.
1

2

3

4

5

6

Mean [W]

3.04

2.70

2.95

3.66

25.04

78.17

Min [W]

2.92

2.60

2.57

3.55

24.53

77.52

Q1 [W]

3.00

2.64

2.87

3.65

24.75

77.85

Median [W]

3.05

2.69

2.94

3.66

24.87

78.04

Q3 [W]

3.06

2.70

3.00

3.66

25.15

78.34

Max [W]

3.96

17.78

17.33

4.37

26.64

118.14

Table 5: Power demand in Idle measured in Watt [W] – hardware selection foreach of the six configurations can be found in Table 2.

5.1.2 Node electricity consumption
Due to the hardware requirements outlined in Chapter 4.2, we do not run all networks on all nodes. In Table 4,
we give an overview about which measurements take place on which machines.
We outline the mean (Table 6) and the median (Table 7) power demand of the nodes during themeasurement of all PoS networks selected. This includes the standard PoS systems Algorand, Cardano,
Ethereum and Solana on the one hand, and, on the other hand, an upper and lower bound network as well as
the base layer network for each of the platforms Avalanche, Cosmos, and Polkadot.

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AlgorandAvalanche-Mainnet(platform base layer)

Avalanche-DFK(platform upper bound)Avalanche-DOS(platform lower bound)

CardanoCosmos-Hub(platform base layer)

Cosmos-Injective(platform upper bound)

Cosmos-Bitsong(platform lower bound)

Ethereum
Polkadot-Relaychain(platform base layer)

Polkadot-Astar(platform upper bound)

Polkadot-Composable(platform lower bound)

Solana

1

2

3

4

5

6

N/A

23.25

25.07

30.86

56.79

160.30

N/A

N/A

N/A

7.29

27.22

109.61

N/A

N/A

N/A

13.86

34.00

120.67

N/A

N/A

N/A

7.73

27.73

112.61

N/A

N/A

N/A

4.57

26.36

83.91

N/A

12.95

17.02

13.37

34.40

125.69

N/A

15.71

18.32

19.35

42.24

132.89

3.63

5.69

7.70

10.18

30.90

120.43Different configurations of client software possible, see detailed analysis in (CCRI, 2022b)

N/A

6.07

9.27

21.46

44.32

123.16

N/A

6.88

9.92

22.66

45.07

125.10

N/A

4.73

6.76

14.63

36.85

122.36

N/A

N/A

N/A

N/A

N/A

319.60

Table 6: Mean power demand of nodes in Watt [W].

AlgorandAvalanche-Mainnet(platform base layer)

Avalanche-DFK(platform upper bound)Avalanche-DOS(platform lower bound)

CardanoCosmos-Hub(platform base layer)

Cosmos-Injective(platform upper bound)

Cosmos-Bitsong(platform lower bound)

CCRI 2023

1

2

3

4

5

6

N/A

23.07

24.90

29.94

56.74

160.25

N/A

N/A

N/A

7.12

27.12

109.31

N/A

N/A

N/A

13.73

33.86

120.535

N/A

N/A

N/A

7.58

27.64

112.54

N/A

N/A

N/A

4.18

25.81

82.63

N/A

12.81

18.10

10.39

31.10

121.91

N/A

15.62

18.36

19.35

42.09

132.84

3.59

5.42

7.58

9.93

30.72

120.0714

Energy Efficiency and Carbon Footprint of PoS Blockchain Networks and Platforms

1
Ethereum

2

3

4

5

6

Different configurations of client software possible, see detailed analysis in (CCRI, 2022b)

Polkadot-Relaychain(platform base layer)

Polkadot-Astar(platform upper bound)

Polkadot-Composable(platform lower bound)

Solana

N/A

6.01

9.21

21.44

44.32

123.09

N/A

6.86

9.93

22.74

45.30

124.87

N/A

4.64

6.86

14.05

37.25

122.29

N/A

N/A

N/A

N/A

N/A

320.33

Table 7: Mean power demand of nodes in Watt [W].

5.2 Calculation of bounds for electricity consumption
To calculate the electricity consumption of an overall network or platform, we need to understand the averagecalculation for a single node. We measured the electrical power demand for different computers dependingon the respective network’s hardware requirements. With these measurements for the electricity
consumption, we can provide upper bounds, meaning the highest electricity that a node consumes, lower
bounds, the least electricity a node consumes, and a best guess that captures the consumption of an averagenode best for the respective network. We apply this approach for each of the PoS networks as well as the three
networks for each platform (base layer network and upper bound as well as lower bound in terms oftransaction throughput) measured.

5.2.1

Upper and lower bound electricity consumption

The upper and lower bounds are measured by the least efficient and most efficient hardware, respectively.
The lower bound therefore is constituted by the Raspberry Pi (configuration 1) for Cosmos-Bitsong.
Configuration 2 is used for Algorand, Cosmos-Hub, Cosmos-Injective, Polkadot-Relaychain, Polkadot-Astar,and Polkadot-Composable. Configuration 3 does not represent any lower bound since each network that
could be executed on this hardware set has also worked on configuration 2. Configuration 4 forms the lower
bound for networks revealing more advanced hardware requirements, that is, all networks measured for the
Avalanche platform as well as Cardano and Ethereum. Configuration 6 serves both as the lower bound for
Solana and as an upper bound for all networks, since for Solana only one device is available. The respective
lower and upper bounds are summarized in Table 8.

AlgorandAvalanche-Mainnet(platform base layer)

Avalanche-DFK(platform upper bound)Avalanche-DOS(platform lower bound)

Cardano

CCRI 2023

Lower boundLower boundUpper boundUpper bound[W][kWh/year][W][kWh/year]

23.25

203.7

160.301,404.25

7.29

63.82

109.61

960.17

13.86

121.4

120.67

1,057.11

7.73

67.7

112.61

986.46

4.57

39.99

83.91

735.0315

Energy Efficiency and Carbon Footprint of PoS Blockchain Networks and Platforms

Cosmos-Hub(platform base layer)

Cosmos-Injective(platform upper bound)

Cosmos-Bitsong(platform lower bound)

Ethereum2
Polkadot-Relaychain(platform base layer)

Polkadot-Astar(platform upper bound)

Polkadot-Composable(platform lower bound)

SolanaLower boundLower boundUpper boundUpper bound[W][kWh/year][W][kWh/year]

12.95

113.46125.691,101.00

15.71

137.62132.891,164.10

3.63

31.79

120.43

1,055.00

20.00

175.19150.06

1,314.75

6.07

53.13

123.16

1,078.84

6.88

60.28

125.101,095.84

4.73

41.48

122.36

1,071.89

319.60319.60319.602,799.67

Table 8: Overview of lower and upper bounds of power demand andelectricity consumption per single node.

5.2.2 Best guess electricity consumption
The electricity consumption of an average node in the network is challenging to estimate. There is noempirical data on the concrete hardware that nodes are running on or indicating users’ preferences. For node
owners, several factors are relevant for their decision on which hardware to run their node on. First, owners
stake tokens to receive rewards and want their revenue to be stable, aiming for hardware designed for longterm operations. Second, due to the profit structure, they do not intend to spend all their revenue on hardware
and might rather opt for barely sufficient hardware within the hardware requirements. These thoughts might
influence their decision in one way or another but might not directly translate to a hardware selection.
Therefore, we opt for a binomial distribution for the hardware selection, based on a regular distribution for key
questions. The distribution for each hardware configuration for each network investigated is displayed inTable 9.

AlgorandAvalanche-Mainnet(platform base layer)

Avalanche-DFK(platform upper bound)Avalanche-DOS(platform lower bound)

1

2

3

4

5

6

N/A

6.25%

25.00%

37.50%

25.00%

6.25%

N/A

N/A

N/A

25.00%

50.00%

25.00%

N/A

N/A

N/A

25.00%

50.00%

25.00%

N/A

N/A

N/A

25.00%

50.00%

25.00%For Ethereum, different client software for both execution and consensus client are available, which impact the electricity consumption of
the single network’s nodes. CCRI has extensively analyzed the electricity consumption of the Ethereum network in (CCRI, 2022b) recently, the2upper and lower bounds provided here are taken from this study and take the shares of the different client software into account.

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CardanoCosmos-Hub(platform base layer)

Cosmos-Injective(platform upper bound)

Cosmos-Bitsong(platform lower bound)

Ethereum
Polkadot-Relaychain(platform base layer)

Polkadot-Astar(platform upper bound)

Polkadot-Composable(platform lower bound)

Solana

1

2

3

4

5

6

N/A

N/A

N/A

25.00%

50.00%

25.00%

N/A

6.25%

25.00%

37.50%

25.00%

6.25%

N/A

6.25%

25.00%

37.50%

25.00%

6.25%

3.125%

15.625%31.25%

31.25%

15.625%3.125%

N/A

N/A

N/A

25.00%

50.00%

25.00%

N/A

6.25%

25.00%

37.50%

25.00%

6.25%

N/A

6.25%

25.00%

37.50%

25.00%

6.25%

N/A

6.25%

25.00%

37.50%

25.00%

6.25%

N/A

N/A

N/A

N/A

N/A

100.00%

Table 9: Overview of node distribution for the measured networks.
With these distributions, we calculate the weighted power demand of an average best guess node(𝑃𝑏𝑒𝑠𝑡𝐺𝑢𝑒𝑠𝑠𝑁𝑜𝑑𝑒 ) for a specific network:
𝐻𝑎𝑟𝑑𝑤𝑎𝑟𝑒𝐶𝑜𝑛𝑓𝑖𝑔𝑢𝑟𝑎𝑡𝑖𝑜𝑛𝑠

𝑃𝑏𝑒𝑠𝑡𝐺𝑢𝑒𝑠𝑠𝑁𝑜𝑑𝑒 = ∑ℎ𝑐(𝑚𝑒𝑎𝑛𝐸𝑛𝑒𝑟𝑔𝑦𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛ℎ𝑐 ∗ 𝑠ℎ𝑎𝑟𝑒ℎ𝑐 )An average node in the different networks consumes from 15.18 watts for the Cosmos platform’s lower boundreference network in terms of transaction throughput (Cosmos-Bitsong) to 319.60 watts for the Solana
network. This difference of more than a factor of 21 implies that not only the number of nodes is relevant for
the electricity consumption of a PoS network, but also the underlying software and its requirements. Table 10
gives an overview about the best guess power demand in watts and electricity consumptions per year in kWh
for each network measured.

AlgorandAvalanche-Mainnet(platform base layer)

Avalanche-DFK(platform upper bound)Avalanche-DOS(platform lower bound)

CardanoCosmos-Hub(platform base layer)

CCRI 2023

Best guess

Best guess[W][kWh/year]

43.51

381.1442.83

375.2350.64

443.57

43.95

384.9

35.30

309.21

26.53

232.4217

Energy Efficiency and Carbon Footprint of PoS Blockchain Networks and Platforms

Cosmos-Injective(platform upper bound)

Cosmos-Bitsong(platform lower bound)

Ethereum3
Polkadot-Relaychain(platform base layer)

Polkadot-Astar(platform upper bound)

Polkadot-Composable(platform lower bound)

SolanaBest guess

Best guess[W][kWh/year]

31.68

277.5515.18

132.9962.44

547.01

29.52

258.6130.49

267.12

24.33

213.15319.602,799.67

Table 10: Overview of best guess estimates for power demandand electricity consumption per single node.
Additionally, we summarize the single node power demand estimates of the upper bound, lower bound, andbest guess for each network measured in Figure 2.

Figure 2: Overview of lower bound, upper bound and best guess power demand
estimates for a single node of the respective network in watts.

For Ethereum, different client software for both execution and consensus client are available, which impact the electricity consumption of
the single network’s nodes. CCRI has extensively analyzed the electricity consumption of the Ethereum network in (CCRI, 2022b) recently, the3best guess provided here is taken from this study and takes the shares of the different client software into account.

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5.3 Power per transaction:
Node base power demand and marginal power demand per TPS
Discussion on electricity consumption per transaction metric
An often-used metric in comparing electricity consumption between systems is the electricity consumption
per transaction, which is frequently calculated by dividing the total electricity consumption by the total
number of transactions within a specific time period. The underlying idea is to compare systems that havedifferent architectures, transaction throughput, and electricity requirements. However, such comparisons
are associated with several challenges and complexities (CCRI, 2023a).
First, underlying definitions may influence the results significantly. For instance, some systems provide atheoretical electricity consumption per transaction, simulating the network at full speed. Other calculations
are based on transaction rates measured in the networks, making comparisons skewed. Further, thedefinition of a transaction might vary from network to network.
Second, there is additional complexity caused by the attribution of the electricity consumption solely to thetransactions. The system requires a base electricity consumption to keep up with the consensus without
providing any transactions. Nonetheless, given the base load of a network, running a node in a "lowtransaction"-period might yield higher electricity per transaction costs than usually to be expected andvice versa. This may also distort comparisons across networks when electricity consumption pertransaction is used as the central metric. Consequently, albeit the metric may provide straightforward
insights into different protocols, its base assumptions need to be understood and its results must be treatedwith care and in context of other metrics.
Third, the electricity consumption per transaction is only one metric describing the sustainability of a
network. It is of utmost importance to understand that this metric needs to be seen in the context of othermetrics such as decentralization, security, transaction complexity, state size, and others. This metric alone
is not sufficient to decide whether a cryptocurrency is sustainable or if a cryptocurrency is worth investingin; in an extreme case, a network consisting of a single, high-performance computer, would be the mostsustainable cryptocurrency, however making nonsense of the decentralization idea.

To address the above-mentioned challenges, we measure the power demand of our nodes in a real-world
scenario and consider the transaction throughput that took place during the respective time period. We thenderive the marginal power demand per transactions per second on a statistical regression model (CCRI,
2023a).
With this approach, we aim to obtain more accurate results than merely considering average values.
Furthermore, it has the advantage that constant power demand, which is independent of the transaction
count, can be differentiated from the power demand that is driven by transactions. The regression model is
set up completely on the basis of our own measurements. We filter outliers and construct a regression line foreach of the hardware configurations from Table 2 selected for the considered PoS network, based on ourperiodically taken measurement samples that consist of the current power demand and the transactions per
second throughput at that time.
Based on this, we can establish a linear equation for a regression line to predict the power demand of a best
guess node operating in a specific PoS network (PBG) for a given transactions per second throughput (TPS). To

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Energy Efficiency and Carbon Footprint of PoS Blockchain Networks and Platforms

determine a general slope (mBG) for a network’s best guess node, which represents the electricity
consumption per TPS in watt seconds, we weigh the slopes of the individual regression lines of all hardware
configurations included in the network’s measurement. Likewise, we derive a weighted y-axis intercept(baseBG), which represents the best guess node’s power demand while executing no transactions but runningthe client software of the network. As a result, we obtain a linear regression equation to determine the power
demand of a best guess node in the considered network depending on the transactions per second the best
guess node processes:
𝑃𝐵𝐺𝑛𝑒𝑡𝑤𝑜𝑟𝑘 (𝑇𝑃𝑆) [𝑊] = 𝑚𝐵𝐺 ∗ 𝑇𝑃𝑆𝑛𝑒𝑡𝑤𝑜𝑟𝑘 + 𝑏𝑎𝑠𝑒𝑃𝑜𝑤𝑒𝑟𝐵𝐺
The annualized transaction counts, the transactions per second metric (both as of April 24, 2023), as well as
es the measured networks’ marginal power demands per TPS and base power demands of all networks
investigated in this study are summarized in Table 11.
We were not able to derive a meaningful relationship between the transaction volume and the nodes’ power
demand for the upper and lower bound Parachains of Polkadot (Polkadot-Astar and Polkadot-Composable).
This is presumed to be due to the architecture of the Polkadot platform (see Chapter 5.4.2): We assume thatthe majority of the load of a Parachain node is caused by the interconnection to the Polkadot-Relaychain andthe inter-chain communication mechanism. Only a smaller fraction of the nodes' load stem from thetransactions of the Relaychain itself. As a result, for the Polkadot Parachains, the relationship between thenumber of transactions and the electricity consumption of a node cannot be expressed meaningfully. The
same holds true for the lower bound chain Avalanche-DOS measured to assess the Avalanche platform. Since
each validator of an Avalanche Subnet is required to join the Avalanche-Mainnet entirely (see Chapter 5.4.2),
a reasonable relationship between the nodes’ power demand and the transaction volume of the considered
Subnet can only be expressed if the traffic on the Subnet significantly exceeds that of the Avalanche-Mainnet
itself. This applies to the measured upper bound Avalanche-DFK, but not for the lower bound Avalanche-DOS.

AlgorandAvalanche-Mainnet5(platform base layer)

Avalanche-DFK(platform upper bound)Avalanche-DOS(platform lower bound)

CardanoCosmos-Hub(platform base layer)

Cosmos-Injective(platform upper bound)

4

Transactions(annualized)

Transactions(TPS)

Marginal power
demand per TPS

Base power
demand[Tx/year]4[Tx/s]4[W][W]

180,527,540

5.72450.080742.9984

297,252

3.4404

0.109242.5741

651,463

7.54010.439547.8678394

0.0046--

16,534,135

0.5243

1.115834.6155

18,338,695

0.58150.809526.0707

222,166,375

7.04480.211230.4125

Transaction counts are as of April 24, 2023. For the Avalanche platform and its Subnet blockchains measured, transaction counts are as ofJune 01, 2023.
5
The Avalanche Mainnet is composed of three blockchains (C-Chain, X-Chain, and P-Chain). We refer to the C-Chain (contract chain) for
the transaction count since this chain is causes most of the traffic on the Avalanche-Mainnet.

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Energy Efficiency and Carbon Footprint of PoS Blockchain Networks and Platforms

Cosmos-Bitsong(platform lower bound)

Ethereum6
Polkadot-Relaychain(platform base layer)

Polkadot-Astar(platform upper bound)

Polkadot-Composable(platform lower bound)

SolanaTransactions

Transactions

Marginal power

Base power(annualized)(TPS)

demand per TPS

demand[Tx/year][Tx/s][W][W]

220,460

0.00700.7981

15.1185

343,393,460

10.8889

0.105561.9741

2,603,545

0.0826

0.359629.3472

732,1900.0232

N/A

N/A

105,485

0.0033

N/A

N/A

11,843,554,310

375.5566

0.0130313.9651

4

4Table 11: Overview of marginal power demands per TPS and base power demandsof a best guess node for each measured network.
Figure 3 illustrates the base power demand (left y-axis) and marginal power demand per one TPS (righty-axis) for each network that reveals a meaningful relationship between transaction volume and node power.

Figure 3: Base power demand and marginal power demand per TPS fora best guess single node of the respective network in watts.

6

For Ethereum, different client software for both execution and consensus client are available, which impact the electricity consumption of

the single network’s nodes. CCRI has extensively analyzed the electricity consumption of the Ethereum network in (CCRI, 2022b) recently, boththe marginal power demand per TPS and the base power demand are based on this study to take the shares of the different client software
into account (as of April 24, 2023).

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Energy Efficiency and Carbon Footprint of PoS Blockchain Networks and Platforms

5.4 Electricity consumption of PoS networks and platforms
Based on the per-node electricity consumption, we can derive the overall electricity consumption of a PoS
network or a PoS platform network. The composition of a network is an important factor when calculating its
electricity consumption. In the following, we first determine the electricity consumption of the regular PoS
networks Algorand, Cardano, Ethereum, and Solana. After that, we analyze the total electricity consumptionof the platforms Avalanche, Cosmos, and Polkadot, based on the results of the three networks measured foreach of these platforms (lower bound, upper bound, and base layer network). Since the platforms reveal
technical differences, we tailor our methodology to address the respective platform characteristics.

5.4.1

Electricity consumption of regular PoS networks
Algorand, Cardano, Ethereum, Solana

To determine the overall electricity consumption of a regular, non-platform PoS network, we multiply the best
guess node estimate with the number of validator nodes in the respective network:
𝑃𝑛𝑒𝑡𝑤𝑜𝑟𝑘 [𝑊] = 𝑃𝐵𝐺𝑛𝑒𝑡𝑤𝑜𝑟𝑘 ∗ 𝑣𝑎𝑙𝑖𝑑𝑎𝑡𝑜𝑟𝐶𝑜𝑢𝑛𝑡𝑛𝑒𝑡𝑤𝑜𝑟𝑘
We obtain the number of nodes from block explorers as specified in Appendix C. The results are depicted inTable 12. From the networks’ electrical power demands, we derive the daily and annualized electricity
consumption.

Nodes[# total]7

ElectricalElectricity

Electricity

power

consumption

consumption[W][kWh/day][kWh/year]

Algorand

2,013

87,585.63

2,102.01

767,234.82

Cardano3,177

112,148.10

2,691.40

982,360.17

Ethereum8

10,856685,257.33

16,446.16

5,750,351.57

Solana1,772

566,331.20

13,591.82

4,961,015.24

Table 12: Overview of electricity consumption of analyzed regular
PoS networks applying the respective best guess estimate.
Figure 4 summarizes the yearly electricity consumption of the analyzed PoS networks Algorand, Cardano,
Ethereum, and Solana in comparison with their respective node count. We find the consumption per year of astandard PoS system to amount from 767,235 kWh for Algorand to 6,003,585 for Ethereum.

Validator counts are as of April 24, 2023. Due to unavailability of data for Algorand at the time of analysis, we rely on an average value takenfrom January 17 to February 05, 2023.
7

For Ethereum, different client software for both execution and consensus client are available, which impact the electricity consumption of
the single network’s nodes. CCRI has extensively analyzed the electricity consumption of the Ethereum network in (CCRI, 2022b) recently. The
8results on network level shown are based on this study to take the shares of the different client software into account (as of April 24, 2023).

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Energy Efficiency and Carbon Footprint of PoS Blockchain Networks and Platforms

Figure 4: Electricity consumption per year [kWh] andnode counts of non-platform networks.

5.4.2 Electricity consumption of PoS platform networks
Avalanche, Cosmos, Polkadot
Platform networks such as Avalanche, Cosmos, and Polkadot are composed of multiple interconnected
blockchains. Thus, to determine the electricity consumption of a platform, we need to extend our approach
applied for standard PoS systems, that is, multiplying the electricity consumption of a best guess node in the
network by the overall node count. The power demand per node in a platform differs depending on thespecific network of the platform to which the node is connected to. This is the case since a platform’s networks
can differ significantly in terms of transaction throughput, transaction complexity, and state size. For some
platforms, it is common that a node validates multiple networks’ blockchains simultaneously. In this case, anode’s electricity consumption also depends on the number of networks in which it participates. To analyze
the total electricity consumption of a platform, we use a five-step approach:In the first step, we collect information about the platform’s topology. This means that we analyze of how
many blockchains the platform is composed, how these are interconnected, and how the individual
blockchains perform in terms of size, complexity, and transaction throughput.
In the second step, we analyze the nodes of the respective platform. This means that we capture data about
the overall number of nodes, a mapping of which node has joined which blockchain, and whether validatorsof the platform can participate in several blockchains simultaneously.
In the third step, we estimate the power demand per node for each of the platform’s blockchains. To do so,
we identify a blockchain with a rather high transaction volume as an upper bound for the power demand ofnodes within this platform, and one with a rather low transaction volume as a lower bound. We measure these
two blockchains adhering to our approach explained in Chapter 5.1, i.e., by running full nodes on representative
hardware configurations conforming to the requirements listed in Table 2. As a result, we obtain the power
demand of a best guess node for both chains investigated, that is, the upper and the lower bound blockchain
selected for the platform. We then establish a linear function model by fitting a line through the determined
best guess power demand per node of the upper and lower bound blockchains towards the respective

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Energy Efficiency and Carbon Footprint of PoS Blockchain Networks and Platforms

blockchain’s daily transaction volume. This linear model allows us to derive an estimate of the power demandof a best guess node for an arbitrary blockchain of the platform based on its daily transaction volume.
In the fourth step, we apply these estimates to calculate the power demand of a single best guess node foreach blockchain of the platform.
In the fifth step, we calculate the total electricity consumption of the entire platform. We iterate over all
validators of the platform and sum up their estimated power demand, which results in the total power
demand of the entire platform. Depending on the platform examined, a validator might participate in morethan one platform. In this case, we furthermore need to correct each validator’s total power demand by its
repeatedly counted idle power.
Table 13 summarizes the results of measuring the upper and the lower bound networks for the platforms
Avalanche, Cosmos, and Polkadot. Furthermore, for each of these platforms, we provide the measurement
results for the respective base layer chain, that is, the Avalanche-Mainnet for Avalanche, the Cosmos-Hub for
Cosmos, and the Polkadot-Relaychain for Polkadot. In the remainder of this chapter, we will determine thetotal electricity consumption of the three platforms based on these measurement results. For this, we tailor
our general methodology just introduced to address the specific characteristics of the respective platform
appropriately.
ElectricalpowerElectricity
consumption

Electricity
consumption[W][kWh/day][kWh/year]

1,247

53,409.01

1,281.95

467,911.81

8405.12

9.72

3,548.56

9395.559.49

3,464.10

175

4,642.75

111.4340,673.5060

1,900.80

45.62

16,653.00

99

1,502.82

36.07

13,166.01

297

8,767.44

210.4376,807.1775

2,286.75

54.89

20,034.00

4

97.32

2.34

852.60Nodes[# total]9Avalanche-Mainnet(platform base layer)

Avalanche-DFK(platform upper bound)Avalanche-DOS(platform lower bound)

Cosmos-Hub(platform base layer)

Cosmos-Injective(platform upper bound)

Cosmos-Bitsong(platform lower bound)

Polkadot-Relaychain(platform base layer)

Polkadot-Astar(platform upper bound)

Polkadot-Composable(platform lower bound)

Table 13: Overview of electricity consumption of analyzed platforms’ upper bound, lower bound,and base layer chain references applying the respective best guess estimate.

9

Validator counts are as of April 24, 2023. For the Avalanche platform and its Subnet blockchains measured, validator counts are as ofJune 01, 2023.

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Energy Efficiency and Carbon Footprint of PoS Blockchain Networks and Platforms

Figure 5 illustrates the relationship between the node count and the yearly electricity consumption of theexamined platforms’ lower bound, upper bound, and base layer blockchains.

Figure 5: Electricity consumption per year [kWh] and node count of analyzed
platforms’ lower bound, upper bound, and base layer chains.

Avalanche Platform
Since it has become possible to launch so-called Subnets in the Avalanche ecosystem, it can be regarded asa platform network. Nodes of one single Subnet can validate multiple blockchains at once, however, a
blockchain can only belong to exactly one Subnet. Subnets are independent networks, allowing to specify acustom membership regularity, define an own execution logic, and maintain a custom state. In contrast to
some other platform networks, Subnets running within the Avalanche platform do not rely on the security ofthe base layer, but rather need to maintain their own network security. Communication between Subnets is
facilitated by the Avalanche Warp Messaging protocol. The primary base layer network of Avalanche isreferred to as the Mainnet; it is compiled of three different blockchains: the P-Chain (platform chain
responsible for platform-level operations), the C-Chain (EVM-based contract chain for executing smart
contracts), and the X-Chain (exchange chain for operations on digital smart assets) (Ava Labs, 2023b).
Each validator of any Subnet on the Avalanche platform is required to additionally join the AvalancheMainnet. Furthermore, it is theoretically possible for a validator to participate in multiple Subnets
simultaneously, although this was not the case for any node at the time of our analysis. Hence, to calculate
the total electricity consumption of the Avalanche platform (𝑃𝐴𝑣𝑎𝑙𝑎𝑛𝑐ℎ𝑒 ), we need to iterate over all validator
nodes of the platform and sum up their power demands:
𝑉𝑎𝑙𝑖𝑑𝑎𝑡𝑜𝑟𝑠

𝑃𝐴𝑣𝑎𝑙𝑎𝑛𝑐ℎ𝑒 [𝑊] = ∑𝑉𝑎𝑙𝑖𝑑𝑎𝑡𝑜𝑟

𝑃𝑉𝑎𝑙𝑖𝑑𝑎𝑡𝑜𝑟

For each node, we determine its individual power demand (𝑃𝑉𝑎𝑙𝑖𝑑𝑎𝑡𝑜𝑟 ) by examining which Subnets this node
has joined. If the node is not participating in any Subnet, i.e., it only validates the Avalanche-Mainnet, its power
demand corresponds to the best guess for a node in Avalanche-Mainnet (𝑃𝑏𝑒𝑠𝑡𝐺𝑢𝑒𝑠𝑠𝑀𝑎𝑖𝑛𝑛𝑒𝑡 ) base layer, which

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Energy Efficiency and Carbon Footprint of PoS Blockchain Networks and Platforms

we have determined through a dedicated measurement (see Chapter 5.2.2). However, if the node participatesin one or more Subnets, we sum up the best guess power demand of the Avalanche-Mainnet on the one hand,
and, on the other hand, the additional power demand caused by running each blockchain of the respective
Subnet or Subnets (𝑃𝑛𝑒𝑡𝑤𝑜𝑟𝑘𝑀𝑎𝑟𝑔𝑖𝑛𝑎𝑙𝑆𝑢𝑏𝑛𝑒𝑡𝐶ℎ𝑎𝑖𝑛 ):∀ 𝑉𝑎𝑙𝑖𝑑𝑎𝑡𝑜𝑟:
𝑆𝑢𝑏𝑛𝑒𝑡𝐶ℎ𝑎𝑖𝑛𝑠

𝑃𝑉𝑎𝑙𝑖𝑑𝑎𝑡𝑜𝑟 [𝑊] = 𝑃𝑏𝑒𝑠𝑡𝐺𝑢𝑒𝑠𝑠𝑀𝑎𝑖𝑛𝑛𝑒𝑡 + (∑𝑠𝑐

(𝑃𝑛𝑒𝑡𝑤𝑜𝑟𝑘𝑀𝑎𝑟𝑔𝑖𝑛𝑎𝑙𝑠𝑐 𝑖𝑓 𝑉𝑎𝑙𝑖𝑑𝑎𝑡𝑜𝑟 𝑖𝑛 𝑠𝑐 𝑒𝑙𝑠𝑒 0))

To estimate the additional power demand ((𝑃𝑛𝑒𝑡𝑤𝑜𝑟𝑘𝑀𝑎𝑟𝑔𝑖𝑛𝑎𝑙𝑆𝑢𝑏𝑛𝑒𝑡𝐶ℎ𝑎𝑖𝑛 ) that a node incurs by joining a specificSubnet, we rely on a linear approximation. Through our measurements of the upper bound Subnet blockchain
Avalanche-DFK and the lower bound Avalanche-DOS, we can model the power demand of a best guess nodeparticipating in an arbitrary Avalanche-Subnet blockchain (𝑃𝑏𝑒𝑠𝑡𝐺𝑢𝑒𝑠𝑠𝑆𝑢𝑏𝑛𝑒𝑡𝐶ℎ𝑎𝑖𝑛 ) based on its daily transaction
volume. Since each node of a Subnet also joins the Mainnet as described above, we subtract the best guess
power demand of an Avalanche-Mainnet node to obtain a node’s additional power demand for participatingin the corresponding Subnet blockchain:∀ 𝑆𝑢𝑏𝑛𝑒𝑡𝐶ℎ𝑎𝑖𝑛:
𝑃𝑛𝑒𝑡𝑤𝑜𝑟𝑘𝑀𝑎𝑟𝑔𝑖𝑛𝑎𝑙𝑆𝑢𝑏𝑛𝑒𝑡𝐶ℎ𝑎𝑖𝑛 [𝑊] = 𝑃𝑏𝑒𝑠𝑡𝐺𝑢𝑒𝑠𝑠𝑆𝑢𝑏𝑛𝑒𝑡𝐶ℎ𝑎𝑖𝑛 (𝑡𝑥𝐶𝑜𝑢𝑛𝑡𝑆𝑢𝑏𝑛𝑒𝑡𝐶ℎ𝑎𝑖𝑛 ) − 𝑃𝑏𝑒𝑠𝑡𝐺𝑢𝑒𝑠𝑠𝑀𝑎𝑖𝑛𝑛𝑒𝑡
The linear models applied are shown in Figure 6. The x-axis indicates the daily transaction volume of Subnet
blockchains within the Avalanche platform, the y-axis the corresponding power demand in watts. The lower
bound measurement results are represented by the orange dots (Avalanche-DOS), the upper boundmeasurement results by the blue dots (Avalanche-DFK). The red function plot indicates the best guess power
consumption of a node running an arbitrary Subnet blockchain (𝑃𝑏𝑒𝑠𝑡𝐺𝑢𝑒𝑠𝑠𝑆𝑢𝑏𝑛𝑒𝑡𝐶ℎ𝑎𝑖𝑛 ) in parallel to the
Avalanche-Mainnet towards the daily transaction count of the respective Subnet blockchain. To finally derive
the additional power demand caused by joining an arbitrary Subnet blockchain (𝑃𝑛𝑒𝑡𝑤𝑜𝑟𝑘𝑀𝑎𝑟𝑔𝑖𝑛𝑎𝑙𝑆𝑢𝑏𝑛𝑒𝑡𝐶ℎ𝑎𝑖𝑛 ), we
subtract the power demand of the Avalanche-Mainnet represented by the green marker from the red line,
which results in the purple linear function. The gray markers on the purple plot show different Subnet
blockchains as of June 01, 2023.

Figure 6: Linear models built out of upper and lower bound measurements to estimate the power
demand of arbitrary Subnets of the Avalanche platform based on daily transaction volume.

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Table 14 lists the results of our calculations to estimate the total power demand and electricity consumptionof the Avalanche platform. Validator counts, transaction counts, as well as the mapping of which validator
participates in which Subnet blockchain are taken from the block explorer Avascan.info10 (as of June 01, 2023).
Some blockchains are not yet indexed by the block explorer at the time of our analysis; for these chains weassume the same daily transaction volume as for our lower bound Subnet blockchain (Avalanche-DOS)
selected, since we consider the unindexed chains to be rather small in size and low in throughput.

Nodes[# total]11
Avalanche

1,247(platform total)

ElectricalElectricity

Electricity

power

consumption

consumption[W][kWh/day][kWh/year]

53,630.41

1,287.13

469,802.39

Table 14: Electricity consumption of the Avalanche platform.

Cosmos Platform
In the Cosmos ecosystem, the base layer blockchain is called Cosmos-Hub, all other chains are referred toas Zones (Tendermint, 2023b). A characteristic of the Cosmos platform is that validators of a zone do notnecessarily have to join the Cosmos-Hub. Hence, for an individual node that has joined only one single Zone,a meaningful correlation between the electricity consumption and the transaction volume on the respective
blockchain can be established. For this reason, we enhance our methodology: Instead of merely estimating
the electricity consumption of a best guess node on a Cosmos Zone via a linear model, we can determine therelationship between transaction volume and power demand for each of our upper and lower boundblockchains measured (Cosmos-Injective and Cosmos-Bitsong, respectively). This allows us to calculate themarginal power consumption per TPS and the base load of a best guess node running the respective Zone as
described in Chapter 5.3. For both metrics, the marginal power consumption per TPS and the base load, weestablish a linear function through the two power demands we have obtained by measuring the upper andlower bound Zone towards the Zones' daily transaction counts (𝑧𝑜𝑛𝑒𝐵𝑎𝑠𝑒𝑃𝑜𝑤𝑒𝑟(𝑡𝑥𝐶𝑜𝑢𝑛𝑡𝑍𝑜𝑛𝑒 ) and𝑧𝑜𝑛𝑒𝑀𝑎𝑟𝑔𝑖𝑛𝑎𝑙𝑇𝑥𝑃𝑜𝑤𝑒𝑟(𝑡𝑥𝐶𝑜𝑢𝑛𝑡𝑍𝑜𝑛𝑒 )). We can thus determine the power demand of a best guess node of anarbitrary Zone at a given daily transaction volume (𝑃𝑏𝑒𝑠𝑡𝐺𝑢𝑒𝑠𝑠𝑍𝑜𝑛𝑒 ) for each Zone in the Cosmos ecosystem byadding these two functions while applying the Zone’s TPS metric:∀ 𝑍𝑜𝑛𝑒:𝑃𝑏𝑒𝑠𝑡𝐺𝑢𝑒𝑠𝑠𝑍𝑜𝑛𝑒 [𝑊] = 𝑧𝑜𝑛𝑒𝐵𝑎𝑠𝑒𝑃𝑜𝑤𝑒𝑟(𝑡𝑥𝐶𝑜𝑢𝑛𝑡𝑍𝑜𝑛𝑒 ) + (𝑧𝑜𝑛𝑒𝑀𝑎𝑟𝑔𝑖𝑛𝑎𝑙𝑇𝑥𝑃𝑜𝑤𝑒𝑟(𝑡𝑥𝐶𝑜𝑢𝑛𝑡𝑍𝑜𝑛𝑒 ) × 𝑇𝑃𝑆𝑍𝑜𝑛𝑒 )Many nodes of the Cosmos platform are validators of multiple Zones at the same time. For this reason, we
need to further adapt our methodology to estimate the power consumption of those nodes that validate
multiple blockchains. Merely counting their estimated best guess power demand for each Zone would lead toan overestimation, as the nodes’ idle consumption should only be included once. To address this, we calculate
the additional power demand a node might require as soon as it has joined a specific Cosmos Zone
(𝑃𝑛𝑒𝑡𝑤𝑜𝑟𝑘𝑀𝑎𝑟𝑔𝑖𝑛𝑎𝑙𝑍𝑜𝑛𝑒 ): We subtract the estimated idle power demand of the hardware of a best guess node in
the Zone (𝑖𝑑𝑙𝑒(𝑡𝑥𝐶𝑜𝑢𝑛𝑡𝑍𝑜𝑛𝑒 )) from the power demand of that Zone determined through our just introduced
linear model. For the idle power demand of the hardware, we also rely on a linear model through our two

10

https://avascan.info/ by AvascanExplorer

The total validator count of the entire Avalanche platform provided here equals the validator count of the Avalanche-Mainnet since eachvalidator of a Subnet must additionally join the Avalanche-Mainnet. This means a validator that participates in one or more Subnets is
11

counted only once. The value is determined as of June 01, 2023.

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points measured; we assume a linear distribution of the idle power demand between the hardware from ourlower bound to our upper bound Zone.
∀ 𝑍𝑜𝑛𝑒:𝑃𝑛𝑒𝑡𝑤𝑜𝑟𝑘𝑀𝑎𝑟𝑔𝑖𝑛𝑎𝑙𝑍𝑜𝑛𝑒 [𝑊] = 𝑃𝑏𝑒𝑠𝑡𝐺𝑢𝑒𝑠𝑠𝑍𝑜𝑛𝑒 − 𝑖𝑑𝑙𝑒(𝑡𝑥𝐶𝑜𝑢𝑛𝑡𝑍𝑜𝑛𝑒 )Based on the additional power demand that a node incurs when it joins a Zone, we can compute the total
power demand for each individual validator (𝑃𝑉𝑎𝑙𝑖𝑑𝑎𝑡𝑜𝑟 ). For this, we sum all additional network power demandsof the Zones the node runs on the one hand, and the average idle consumption of estimated for these Zones
on the other (𝑖𝑑𝑙𝑒𝑉𝑎𝑙𝑖𝑑𝑎𝑡𝑜𝑟 ):∀ 𝑉𝑎𝑙𝑖𝑑𝑎𝑡𝑜𝑟:
𝑍𝑜𝑛𝑒𝑠

𝑃𝑉𝑎𝑙𝑖𝑑𝑎𝑡𝑜𝑟 [𝑊] = 𝑖𝑑𝑙𝑒𝑉𝑎𝑙𝑖𝑑𝑎𝑡𝑜𝑟 + (∑𝑧

𝑖𝑑𝑙𝑒𝑉𝑎𝑙𝑖𝑑𝑎𝑡𝑜𝑟 =(𝑃𝑛𝑒𝑡𝑤𝑜𝑟𝑘𝑀𝑎𝑟𝑔𝑖𝑛𝑎𝑙𝑧 𝑖𝑓 𝑉𝑎𝑙𝑖𝑑𝑎𝑡𝑜𝑟 𝑖𝑛 𝑧 𝑒𝑙𝑠𝑒 0))∑𝑍𝑜𝑛𝑒𝑠(𝑖𝑑𝑙𝑒(𝑡𝑥𝐶𝑜𝑢𝑛𝑡) 𝑖𝑓 𝑣 𝑖𝑛 𝑧 𝑒𝑙𝑠𝑒 0)𝑧|𝑍𝑜𝑛𝑒𝑠𝑣 |To finally determine the total power demand of the Cosmos platform (𝑃𝐶𝑜𝑠𝑚𝑜𝑠 ), we iterate over all validatorsand sum up their individual power demands approximated:
𝑉𝑎𝑙𝑖𝑑𝑎𝑡𝑜𝑟𝑠

𝑃𝐶𝑜𝑠𝑚𝑜𝑠 [𝑊] = ∑𝑉𝑎𝑙𝑖𝑑𝑎𝑡𝑜𝑟

𝑃𝑉𝑎𝑙𝑖𝑑𝑎𝑡𝑜𝑟

We note that we have conducted an additional measurement of the base layer Zone Cosmos-Hub asprovided in Table 11. For those validators participating in the Cosmos-Hub, we rely on the real measurement
results to determine the base power demand and the marginal power demand per TPS instead of theestimation we would obtain through our linear models.
The results of our analysis of the Cosmos platform applying the tailored methodology just introduced aresummarized in Table 15. All underlying transaction counts, validator counts, and the mapping of which
validator is part of which Zone are taken from the block explorer Mintscan.io12 (as of April 24, 2023).

Nodes[# total]13
Cosmos1,914(platform total)

ElectricalpowerElectricity
consumption

Electricity
consumption[W][kWh/day][kWh/year]

66,401.5

1,593.64

581,677.14

Table 15: Electricity consumption of the Cosmos platform.
Figure 7 illustrates the linear functions that we have applied in our calculations for the Cosmos platform. Theorange data points result from measuring the lower bound Zone Cosmos-Bitsong, the blue ones frommeasuring the upper bound Zone Cosmos-Injective. The x-axis represents the absolute number oftransactions within 24 hours (as of April 24, 2023). The orange line indicates the base power demand of a Zone
for a given 24-hour transaction volume (𝑧𝑜𝑛𝑒𝐵𝑎𝑠𝑒𝑃𝑜𝑤𝑒𝑟(𝑡𝑥𝐶𝑜𝑢𝑛𝑡𝑍𝑜𝑛𝑒 )). The blue function shows the marginal
power demand per one transaction per second for a Zone (𝑧𝑜𝑛𝑒𝑀𝑎𝑟𝑔𝑖𝑛𝑎𝑙𝑇𝑥𝑃𝑜𝑤𝑒𝑟(𝑡𝑥𝐶𝑜𝑢𝑛𝑡𝑍𝑜𝑛𝑒 )), likewise for agiven 24-hour transaction volume. By multiplying the result of the blue function with the transactions per

12https://hub.mintscan.io/ by Cosmostation

A validator in the Cosmos platform can participate in multiple Zones simultaneously. The total validator count of the Cosmos platform
provided here is the overall number of machines in the entire platform. This means a validator that participates in more than one Zone is13

counted only once. The value is determined as of April 24, 2023.

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second (TPS) of the corresponding Zone, and adding the orange function to this result, we obtain the red line,
which represents the power demand of a best guess node for a Zone towards a daily transaction count(𝑃𝑏𝑒𝑠𝑡𝐺𝑢𝑒𝑠𝑠𝑍𝑜𝑛𝑒 ). We subtract the green idle function from this best guess to determine the additional power
demand for a node that joins a specific Zone (𝑃𝑛𝑒𝑡𝑤𝑜𝑟𝑘𝑀𝑎𝑟𝑔𝑖𝑛𝑎𝑙𝑍𝑜𝑛𝑒 ) as shown by the purple function. The graymarkers on the purple plot indicate the different Zones as of April 24, 2023.

Figure 7: Linear models built out of upper and lower bound measurements to estimate the power demandof arbitrary Zones of the Cosmos platform based on daily transaction volume.

Polkadot Platform
Similar to Avalanche and Cosmos, Polkadot is also a platform for multiple blockchains. In addition to the baselayer, which is referred to as the Relaychain, there are so-called Parachains. In contrast to the Subnets of
Avalanche and the Zones of Cosmos, Parachains are relying on the security of the Relaychain (sharedsecurity) since they are validated by the nodes of Polkadot’s base layer blockchain. Nodes maintaining a
Parachain are called collators. Unlike traditional validators, they do not provide security guarantees; instead,among other things, they are responsible for propagating the state of their Parachain to the Relaychain. In
addition, they are in charge of handling the communication between the different Parachains, which isaccomplished via the XCM (Cross-Consensus-Message) protocol (Web3 Foundation, 2023a).
For our analysis of the Polkadot platform, we opt for the Parachain Astar as the upper bound and the Parachain
Composable as the lower bound to estimate the power demand of a best guess collator node participating
in an arbitrary Parachain, based on the Parachains’ daily transaction volumes (as of April 24, 2023). As
mentioned in Chapter 5.3, we were unable to establish a meaningful relationship between the power demandof the collator nodes measured and the transaction volume on the corresponding Parachain for either bound.
This suggests that the activities of the collators associated with the Relaychain are more decisive for the
power demand than the transactions taking place on the actual Parachain. Consequently, we cannot
determine a marginal power demand per TPS nor a base power demand for the individual Parachains of the
Polkadot ecosystem.

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Therefore, to determine the total power demand of the Polkadot platform, we rely on the best guess power
metric from our measurements of the upper bound and the lower bound Parachain. We establish a linear
model through the two measured best guess power demands towards the 24-hour transaction counts of the
Parachains. This enables us to approximate the best guess power demand of an arbitrary Parachain node
based on the daily transaction volume. The resulting linear function is plotted in Figure 8. The orange dot
represents the best guess power demand of the lower bound Parachain Polkadot-Composable, the blue dotthat of the upper bound Parachain Polkadot-Astar. The gray markers on the function plot indicate the modelbased estimates of best guess power demands of the remaining Polkadot Parachains as of April 24, 2023.

Figure 8: Linear model built out of upper and lower bound measurements to estimate the power demandof arbitrary Parachains of the Polkadot platform based on daily transaction volume.

Finally, to obtain the total power demand of the Polkadot platform (𝑃𝑃𝑜𝑙𝑘𝑎𝑑𝑜𝑡 ), we iterate through each
Parachain and multiply the node count with the best guess power demand of that Parachain determined via
our linear model, and sum up the results:
𝑃𝑎𝑟𝑎𝑐ℎ𝑎𝑖𝑛

𝑃𝑃𝑜𝑙𝑘𝑎𝑑𝑜𝑡 [𝑊] = ∑𝑝(|𝑉𝑎𝑙𝑖𝑑𝑎𝑡𝑜𝑟𝑠𝑝 | × 𝑃𝑏𝑒𝑠𝑡𝐺𝑢𝑒𝑠𝑠𝑝 (𝑡𝑥𝐶𝑜𝑢𝑛𝑡))

The results of our analysis to estimate the total power demand and electricity consumption of the Polkadot
Platform are presented in Table 16. All transaction and validator/collator counts are taken from the block
explorer Subscan.io14 as of April 24, 2023. At the time of our analysis, no information about the node counts of
the Parachains Acala, Crust-Parachain, Darwinia2, Efinity, Equilibrium, Kilt-Spiritnet, Nodle, Unique, andZeitgeist was available on Subscan. For this reason, we assume the average node count of the other
Parachains as a proxy for the analysis of these.

14

https://www.subscan.io/ by Subscan TeamCCRI 2023

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Energy Efficiency and Carbon Footprint of PoS Blockchain Networks and Platforms

Nodes[# total]15
Polkadot(platform total)

624

ElectricalElectricity

Electricity

power

consumption

consumption[W][kWh/day][kWh/year]

17,602.98

422.47

154,202.10

Table 16: Electricity consumption of the Polkadot platform.

Comparison of Platforms Electricity Consumptions
Figure 9 compares the calculated yearly electricity consumption as well as the validator counts of theanalyzed platform networks Avalanche, Cosmos, and Polkadot. With 154,202.10 kWh per year, the Polkadot
ecosystem reveals the lowest electricity consumption among the analyzed platforms, however, the total
validator/collator count of Polkadot is remarkably below those of Avalanche and Cosmos.
Similar to the standard PoS networks analyzed in Chapter 5.4.1, the validator count of a platform appears to
be a crucial driver for the total electricity consumption. However, for platforms such as Cosmos, we note thatthe relationship between node count and electricity consumption must be treated with care: As discussed
above, it is common for a validator to participate in multiple networks of the platform simultaneously, which
increases the power demand per node on the one hand but decreases the overall number of single nodes
within the platform on the other hand. This results in additional complexity in determining the power demandof a single node and ultimately in calculating the total electricity consumption of the platform.

Figure 9: Electricity consumption per year [kWh] andnode counts of platform networks.

15

The total validator count of the entire Polkadot platform provided here equals the sum of the validator count of the Polkadot-Relaychain

and each Parachain’s collator count. The value is determined as of April 24, 2023.

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5.5 Carbon footprint of PoS networks and platforms
The electricity consumption of any system has no direct environmental impact, as mere usage does notcause any emissions. However, the impacts due to the potential emissions of the underlying energy sources
may cause damage to the environment and need to be considered for sustainable business operations.
Depending on the underlying energy sources, the respective carbon footprint of the electricity consumption
can vary. For a precise estimate of the carbon footprint, two pieces of data are essential: The location of theelectricity consumers as well as the carbon intensity of the respective grid.
There are several ways for local electricity consumers to claim that their electricity consumption is carbon
neutral. This includes corporate Power Purchase Agreements (PPAs), unbundled energy attribute certificates(EACs) – also often referred to as Renewable Energy Certificates (RECs) –, or off-grid electricity production for
self-consumption. As we do not have any information on whether or to what extent the electricity
consumption of PoS networks or PoS platform networks is backed by such instruments, we rely on the average
grid intensity factor. As these instruments are also often aimed at energy-intensive industries or large
corporations, we find the application of the average grid intensity factor to be plausible for a solid estimateof the carbon footprint of PoS networks and platforms.
Previous research localized nodes in other protocols by relying on internet search machines aimed at ASIC
devices, IP addresses, or pool addresses. These approaches allowed for an estimate of how the nodes are
distributed worldwide.
For some PoS networks, data on the node locations is publicly available. In that case, we calculate the averagecarbon intensity of the countries in which the nodes are located utilizing country specific emission factors(carbon footprint, 2022). For the U.S., in which commonly a comparatively high number of a network’s nodes
are located, we even resort to state level carbon intensities. For unavailable carbon intensities or unavailable
node locations, we assume the world average of 459 gCO2e per kWh in accordance with the IEA (International
Energy Agency, 2021).
Table 17 summarizes the carbon intensities for the different networks and platforms we apply to determine
their carbon footprint. Furthermore, we provide the sources of the node locations that we have used to
calculate the location specific carbon intensity factors.
Carbon intensity

Node location

Location specific[gCO2e/kWh]sourceintensity factor

459

N/A

✗ (world avg)

379

Avascan.info16✓

365

Adatools.io17

✓

459

N/A

✗ (world avg)

348

Migalabs.es18✓

AlgorandAvalanche(platform total)

CardanoCosmos(platform total)

Ethereum16
1718

https://avascan.info/stats/staking
https://adatools.io/earth
https://migalabs.es/beaconnodes#geo-dist

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Energy Efficiency and Carbon Footprint of PoS Blockchain Networks and Platforms

Carbon intensity

Node location

Location specific[gCO2e/kWh]sourceintensity factor

459

N/A

✗ (world avg)

327

Validators.app19✓

Polkadot(platform total)

SolanaTable 17: Carbon intensity factors applied for networks and platforms todetermine their carbon footprints (as of April 24, 2023).
With that, we can derive the carbon footprints of the single networks and platforms: For each node of a networkor platform, we determine the carbon footprint by multiplying its energy consumption with the respective
carbon intensity factor of the network. Summing up the emissions caused by each validator node gives us
the total carbon footprint of the network or platform (𝐶𝐹𝑁𝑒𝑡𝑤𝑜𝑟𝑘|𝑃𝑙𝑎𝑡𝑓𝑜𝑟𝑚 ):𝑁𝑜𝑑𝑒𝑠

𝐶𝐹𝑁𝑒𝑡𝑤𝑜𝑟𝑘|𝑃𝑙𝑎𝑡𝑓𝑜𝑟𝑚 = ∑𝑛𝑜𝑑𝑒(𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝑛𝑜𝑑𝑒 × 𝑐𝑎𝑟𝑏𝑜𝑛𝐼𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦𝑁𝑒𝑡𝑤𝑜𝑟𝑘|𝑃𝑙𝑎𝑡𝑓𝑜𝑟𝑚 )Table 18 summarizes the resulting carbon footprints of the analyzed standard PoS networks Algorand,
Cardano, Ethereum, and Solana. Since our approach to measure the electricity consumption of a single nodeof a standard PoS network provides upper and lower bounds in addition to a best guess estimate (seeChapter 5.1.2), we can likewise establish a lower bound, an upper bound, and a best guess for the carbon
footprint of each network.
Emissions (best guess)

Emissions (lower bound)

Emissions (upper bound)[tCO2e/year][tCO2e/year][tCO2e/year]

Algorand352.16188.21

1,297.48

Cardano358.3046.34

851.71

Ethereum

2,088.40

661.584,964.97

Solana1,620.79

1,620.79

1,620.79

Table 18: Overview of annualized best guess, lower bound, and upper boundCO2e emissions analyzed of the standard PoS networks.
For the investigated platform networks Avalanche, Cosmos, and Polkadot, the calculation of the total energy
consumption is more complex; our methodology as described in Chapter 5.4.2 provides an annualized best
guess for the electricity consumption of the platforms. We multiply this by the respective platform’s carbon
intensity factor as listed in Table 17 to obtain a best guess for the total yearly CO2e emissions. Table 19
summarizes the results.
The annual CO2e emissions of the examined standard PoS networks and those of the PoS platform networks
lie in a similar range. Figure 10 compares the annualized lower bound, best guess, and upper bound emissionsof the standard PoS networks from Table 18 and the best guess emissions of the platform networks from Table
19.

19

https://www.validators.app/

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Energy Efficiency and Carbon Footprint of PoS Blockchain Networks and Platforms

Emissions (best guess)[tCO2e/year]
Avalanche(platform total)

Cosmos(platform total)

Polkadot(platform total)

178.27266.9970.78

Table 19: Overview of annualized best guess CO2e emissionsof the analyzed PoS platform networks.

Figure 10: Comparison of annualized carbon emissions of standard PoS networks(lower bound, best guess, and upper bound) and PoS platform networks (best guess).

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6

Discussion and comparison of results

In the previous chapters, we introduced our methodology and conducted measurements to derive theelectricity consumption and carbon footprint of the Proof of Stake networks Algorand, Cardano, Ethereum, andSolana as well as the PoS platform networks Avalanche, Cosmos, and Polkadot. In this chapter, we
contextualize the results.

6.1

Yearly electricity consumption in the context of other systems

The electricity consumption of a single network or platform is often meaningless without any context tocompare the system. In Chapter 5.4, we outline that the yearly electricity consumption of networks or
platforms range from 154,202.10 kWh (Polkadot platform) to 5,750,351.57 kWh (Ethereum network). An average
US household consumes about 10,600 kWh per year and, therefore, the least electricity consuming system
Polkadot requires about 14.5 times the electricity, and the most electricity consuming network Ethereum about
542.5 times the electricity (U.S. Energy Information Administration, 2021).
In comparison to the decentralized and PoW-based cryptocurrency Bitcoin, these two PoS systems consume
about 0.0001 % (Polkadot) or 0.0042 % (Ethereum) of the Bitcoin network assuming an annualized
consumption of 137.79 TWh as of April 24, 2023 (CBECI, 2023). Bitcoin consumes much more electricity than
Proof of Stake systems due to its Proof of Work consensus mechanism, which results in the deployment ofenergy-intensive hardware.
In Figure 11, we compare the Bitcoin network (as of April 24, 2023), the average US household as introduced
above (as of 2021), and the standard PoS networks and the PoS platform networks we have analyzed in thisstudy.

Figure 11: Comparison of the yearly electricity consumption of an average US household (as of 2021), the PoW
network Bitcoin (as of April 24, 2023), and the PoS networks as well as platforms analyzed in this study.

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6.2 The carbon footprint of Proof of Stake networks
In absolute terms, the emissions of the PoS networks are rather small. As outlined in Chapter 5.5, the networks
or platform networks emit between 70.78 tonnes (Polkadot) and 2,088.40 tonnes (Ethereum) of CO2e per year.
As a reference point for comparison, one round trip flight from Munich (MUC) to San Francisco (SFO) in
business class emits about 6.1 tonnes CO2e (MyClimate, 2021). As a result, the networks or platforms assessed
emit the same amount of carbon dioxide as 12 to 342 roundtrip flights. It can be assumed that the carbon
emissions of companies behind the networks or platforms are higher than the emissions from the network
itself (e.g., due to business travel).
Figure 12 compares the carbon footprints of the PoS networks and platforms examined in this study to aroundtrip flight MUC – SFO in business class.

Figure 12: Comparison of the yearly carbon footprint of a MUC-SFO roundtrip flight inbusiness class and the PoS networks as well as platforms analyzed in this study.

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Energy Efficiency and Carbon Footprint of PoS Blockchain Networks and Platforms

7

Conclusion

In this report, we benchmark the electricity consumption and carbon footprint of the Proof of Stake networks
Algorand, Cardano, Ethereum, and Solana as well as the platform networks Avalanche, Cosmos, and Polkadot.
Our results are based on a selection of representative hardware devices, measurements of node electricity
consumption, and calculations of carbon emissions via emission factors.
Our best guess estimates the yearly electricity consumption of the Proof of Stake systems from 154,202.10 kWh
for the Polkadot platform to 5,750,351.57 kWh for the Ethereum network. The electricity consumptions result ina carbon footprint between 70.78 and 2,088.40 tonnes of CO2e annually. Compared to other electricity
consumers such as an average US household, the most consuming network Ethereum requires roughly 542x
more electricity and produces 342x the amount of an intercontinental round trip flight.
Given the continuous development and evolution of Proof of Stake networks, our results can only be taken asa snapshot of the respective timeframe. Additional measurements and analyses are required to update andfurther enhance the validity of the metrics for electricity consumption and carbon footprint of Proof of Stakeand other networks. Moreover, other networks employing different consensus mechanisms as well as second
layer networks (CCRI, 2023b) need to be taken into account to gain a holistic picture of the environmental
impact of cryptocurrencies and tokens.
In recent years, Bitcoin has faced harsh criticism for its electricity demand and carbon emissions. In the public,
these fears and accusations have often been applied to other blockchain protocols, regardless of theirtechnical foundations or capabilities, harming the adoption of blockchain protocols in the industry, public
sector, and private investors (Rieger, Roth, Sedlmeir, & Fridgen, 2022).
Despite the relatively low absolute electricity consumption and carbon emission level of PoS networks andplatforms as benchmarked in this study, it remains essential to continue monitoring and disclosing energy
efficiency and carbon emissions of PoS chains. Transparency in terms of informing stakeholders, such asusers, regulators, policymakers, and the public, about the climate costs and benefits of blockchain solutions
is vital. In addition, sustainability indicators might be a fundamental decision criterion for developers,
corporates, NFT creators, etc. to select an energy efficient chain. Finally, compliance with upcoming
regulatory requirements entails disclosure of climate impact. In general, carbon footprint calculations canbe considered as the essential data basis to reach corporate climate goals via energy consumption
reduction strategies and/or carbon offsetting.

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References
Algorand. (2023). Algorand - Hardware Requirements. Retrieved fromhttps://developer.algorand.org/docs/run-a-node/setup/install/#hardware-requirements
Astar Developers Hub. (2023). Astar Docs - Requirements. Retrieved fromhttps://docs.astar.network/docs/nodes/archive-node/#requirements
Ava Labs. (2023a). Avalanche - Computer Hardware and OS. Retrieved fromhttps://docs.avax.network/nodes/build/run-avalanche-node-manually#computer-hardwareand-os
Ava Labs. (2023b). What Is a Subnet? Retrieved from https://docs.avax.network/learn/avalanche/subnetsoverview
BitSong. (2023). BitSong - What are the hardware requirements? Retrieved fromhttps://docs.bitsong.io/validators/validator-faq#what-are-hardware-requirements
Cardano Foundation. (2023). Cardano - Prerequisites Retrieved fromhttps://developers.cardano.org/docs/get-started/installing-cardano-node/#prerequisites
CBECI. (2023). Cambridge Bitcoin Electricity Consumption Index. Retrieved from https://ccaf.io/cbnsi/cbeci
CCRI. (2022a). Energy efficiency and carbon emissions of PoS Networks. Retrieved from https://carbonratings.com/dl/pos-report-2022
CCRI. (2022b). The Merge - Implications on the Electricity Consumption and Carbon Footprint of the
Ethereum Network. Retrieved from https://carbon-ratings.com/dl/eth-report-2022
CCRI. (2023a). Determining the electricity consumption and carbon footprint of Proof of Stake networks.
Retrieved from https://carbon-ratings.com/dl/whitepaper-pos-methods-2023
CCRI. (2023b). Energy Efficiency and Carbon Footprint of the Polygon Blockchain. Retrieved fromhttps://carbon-ratings.com/dl/polygon-report-2022
Composable Finance. (2023). Collator Set-up Guide. Retrieved fromhttps://docs.composable.finance/developer-guides/collator-guide/#select-virtual-hardware
De Vries, A., Gallersdörfer, U., Klaaßen, L., & Stoll, C. (2022). Revisiting Bitcoin’s carbon footprint. Joule, 6(3),498-502.
Hennion, N. (2021). Glances - An eye on your system. Retrieved from https://github.com/nicolargo/glances
Injective Labs. (2023). Injective Docs - Hardware Specification. Retrieved fromhttps://docs.injective.network/nodes/RunNode/mainnet/#hardware-specification
MyClimate. (2021). Carbon Footprint calculator. Retrieved fromhttps://co2.myclimate.org/en/flight_calculators/new
Passmark Software. (2021). Hardware & Software Market Trends. Retrieved fromhttps://www.passmark.com/services/market-analysis.php
Rieger, A., Roth, T., Sedlmeir, J., & Fridgen, G. (2022). We need a broader debate on the sustainability ofblockchain. Joule, 6(6), 1137-1141.
Sedlmeir, J., Buhl, H. U., Fridgen, G., & Keller, R. (2020). The energy consumption of blockchain technology:
Beyond myth. Business & Information Systems Engineering, 62(6), 599-608.
Solana Foundation. (2023). Solana - Validator Requirements. Retrieved fromhttps://docs.solana.com/de/running-validator/validator-reqs
Tendermint. (2023a). Cosmos Hub - Hardware. Retrieved from https://hub.cosmos.network/main/hubtutorials/join-mainnet.html#hardware
Tendermint. (2023b). What is Cosmos? Retrieved from https://v1.cosmos.network/intro
U.S. Energy Information Administration. (2021). How much electricity does an American home use? Retrieved
from https://www.eia.gov/tools/faqs/faq.php?id=97&t=3
Web3 Foundation. (2023a). Parachains. Retrieved from https://wiki.polkadot.network/docs/learn-parachains
Web3 Foundation. (2023b). Polkadot - Reference Hardware. Retrieved fromhttps://wiki.polkadot.network/docs/maintain-guides-how-to-validate-polkadot#referencehardware

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List of Figures
Figure 1: Total annualized network carbon emissions determined 2023 PoS Benchmarking Study. ........................................................... 4
Figure 2: Overview of lower bound, upper bound and best guess power demand estimates for a single node of therespective network in watts............................................................................................................................................................................................................................ 18
Figure 3: Base power demand and marginal power demand per TPS for a best guess single node of the respective
network in watts...................................................................................................................................................................................................................................................... 21
Figure 4: Electricity consumption per year [kWh] and node counts of non-platform networks. ................................................................ 23
Figure 5: Electricity consumption per year [kWh] and node count of analyzed platforms’ lower bound, upper bound, andbase layer chains. ................................................................................................................................................................................................................................................ 25
Figure 6: Linear models built out of upper and lower bound measurements to estimate the power demand of arbitrary
Subnets of the Avalanche platform based on daily transaction volume. ...................................................................................................................26
Figure 7: Linear models built out of upper and lower bound measurements to estimate the power demand of arbitrary
Zones of the Cosmos platform based on daily transaction volume. .............................................................................................................................. 29
Figure 8: Linear model built out of upper and lower bound measurements to estimate the power demand of arbitrary
Parachains of the Polkadot platform based on daily transaction volume. .................................................................................................................30
Figure 9: Electricity consumption per year [kWh] and node counts of platform networks.............................................................................. 31
Figure 10: Comparison of annualized carbon emissions of standard PoS networks (lower bound, best guess, and upperbound) and PoS platform networks (best guess). ........................................................................................................................................................................34
Figure 11: Comparison of the yearly electricity consumption of an average US household (as of 2021), the PoW network
Bitcoin (as of April 24, 2023), and the PoS networks as well as platforms analyzed in this study. .............................................................. 35
Figure 12: Comparison of the yearly carbon footprint of a MUC-SFO roundtrip flight in business class and the PoS networks
as well as platforms analyzed in this study. ......................................................................................................................................................................................36

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List of Tables
Table 1: Key figures of measured networks. Values are taken from Coinmarketcap.com as of June 02, 2023. ................................. 5
Table 2: Overview of selected hardware configurations from lowest to highest requirement........................................................................ 9
Table 3: Hardware requirements of the selected PoS networks. Minimal requirements are marked with a “>” sign. CPUs
labeled with “v” denote virtual CPUs. ....................................................................................................................................................................................................... 10
Table 4: Overview of configurations out of our hardware pool selected for the different PoS networks selected. ........................... 11
Table 5: Power demand in Idle measured in Watt [W] – hardware selection for each of the six configurations can be foundin Table 2. ..................................................................................................................................................................................................................................................................... 13
Table 6: Mean power demand of nodes in Watt [W]. .................................................................................................................................................................. 14
Table 7: Mean power demand of nodes in Watt [W]. ................................................................................................................................................................... 15
Table 8: Overview of lower and upper bounds of power demand and electricity consumption per single node. ......................... 16
Table 9: Overview of node distribution for the measured networks. ................................................................................................................................. 17
Table 10: Overview of best guess estimates for power demand and electricity consumption per single node. ............................. 18
Table 11: Overview of marginal power demands per TPS and base power demands of a best guess node for eachmeasured network................................................................................................................................................................................................................................................ 21
Table 12: Overview of electricity consumption of analyzed regular PoS networks applying the respective best guessestimate. ..................................................................................................................................................................................................................................................................... 22
Table 13: Overview of electricity consumption of analyzed platforms’ upper bound, lower bound, and base layer chain
references applying the respective best guess estimate. ...................................................................................................................................................... 24
Table 14: Electricity consumption of the Avalanche platform. .............................................................................................................................................. 27
Table 15: Electricity consumption of the Cosmos platform. .................................................................................................................................................... 28
Table 16: Electricity consumption of the Polkadot platform. .................................................................................................................................................... 31
Table 17: Carbon intensity factors applied for networks and platforms to determine their carbon footprints (as of April 24,2023). ............................................................................................................................................................................................................................................................................. 33
Table 18: Overview of annualized best guess, lower bound, and upper bound CO2e emissions analyzed of the standard PoS
networks. ..................................................................................................................................................................................................................................................................... 33
Table 19: Overview of annualized best guess CO2e emissions of the analyzed PoS platform networks. ...............................................34
Table 20: Power demand while running an Algorand full node measured in Watt [W]. .................................................................................... 42
Table 21: Power demand while running an Avalanche-Mainnet full node measured in Watt [W]. ............................................................ 42
Table 22: Power demand while running an Avalanche-DFK full node measured in Watt [W]. ......................................................................43
Table 23: Power demand while running an Avalanche-DOS full node measured in Watt [W]. ....................................................................43
Table 24: Power demand while running a Cardano full node measured in Watt [W]. ........................................................................................43
Table 25: Power demand while running a Cosmos-Hub full node measured in Watt [W]. ............................................................................ 44
Table 26: Power demand while running a Cosmos-Injective full node measured in Watt [W]. .................................................................. 44
Table 27: Power demand while running a Cosmos-Bitsong full node measured in Watt [W]. ..................................................................... 44
Table 28: Power demand while running a Polkadot-Relaychain full node measured in Watt [W]............................................................ 45
Table 29: Power demand while running a Polkadot-Astar full node measured in Watt [W]. ........................................................................ 45
Table 30: Power demand while running a Polkadot-Composable full node measured in Watt [W]. ...................................................... 46
Table 31: Power demand while running a Solana full node measured in Watt [W]. ............................................................................................. 46
Table 32: Data sources for analysis of Algorand. ........................................................................................................................................................................... 47
Table 33: Data sources for analysis of Avalanche-Mainnet. .................................................................................................................................................. 47
Table 34: Data sources for analysis of Avalanche-DFK. ........................................................................................................................................................... 48
Table 35: Data sources for analysis of Avalanche-DOS. .......................................................................................................................................................... 48
Table 36: Data sources for analysis of Cardano............................................................................................................................................................................ 48
Table 37: Data sources for analysis of Cosmos-Hub. ................................................................................................................................................................ 49
Table 38: Data sources for analysis of Cosmos-Injective. ...................................................................................................................................................... 49
Table 39: Data sources for analysis of Cosmos-Bitsong. ........................................................................................................................................................ 49
Table 40: Data sources for analysis of Ethereum. ......................................................................................................................................................................... 50
Table 41: Data sources for analysis of Polkadot-Relaychain................................................................................................................................................. 50
Table 42: Data sources for analysis of Polkadot-Astar. ............................................................................................................................................................ 50
Table 43: Data sources for analysis of Polkadot-Composable............................................................................................................................................. 51
Table 44: Data sources for analysis of Solana. ................................................................................................................................................................................. 51

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Appendix A: Hardware Selection
We use the Passmark CPU Benchmark Dataset. Our methodology to select three CPUs consists of the following
steps:
1)

The data set contains many processor types that are not relevant to us. We filter out:A) CPUs with less than 50 benchmarking results, as we expect that they are not relevant for the validator
community.
B) CPUs that were released before 1/1/2015, as we consider less usage of outdated hardware and apractical reason: We cannot buy these CPUs in the market.
C) CPUs with missing or incomplete data.
D) CPUs of AMD. Intel is the dominating manufacturer of CPUs with over 80 % market share over the lastyears. Not all values in the data set are consistent between both producers, and already one AMD
system is included in our data set. Therefore we decided not to consider AMD processors.
E)

CPUs intended for servers or notebooks. We think that the share of server hardware is low andnotebooks nonexistent. Some CPUs are marked as "Laptop only" in our dataset; however, we find themincluded in MiniPCs, e.g., the Intel NUC. To account for these CPUs, we consulted geizhals.de as asource of CPU models sold within MiniPCs and did not remove them from the data set.

2) After obtaining a cleaned data set, we can separate the data set into three equally large categories forlater selection: High-level, mid-level, and low-level. While the hardware within the networks might not be
equally distributed among these three categories, this approach allows us to shift the allocation for single
networks between the devices depending on their hardware requirements.
3) We are confronted with the fact that older, high-level CPU models might have the same computational
power as recent low-level CPU models but different energy efficiencies, leading to entirely different results.
Therefore, we introduce an additional variable in our data set called energy efficiency. The energy
efficiency of a processor is the average benchmarking result divided by the TDP. The TDP serves as a proxy
for a processor's energy demand capabilities, as it describes the maximum amount of heat measured inWatts the CPU cooling system has to deal with.
4) This variable allows us to calculate the average energy efficiency for each category of CPUs (4-high/3mid/2-low) and select an average processor from the respective tier. This approach ensures that we a)cover three different performance categories and b) select an average energy efficiency for theirrespective class.

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Appendix B: Electricity Measurements of Single Nodes
All electricity measurements are conducted in Watt.

Algorand
1

2

3

4

5

6

Min [W]

N/A

22.61

24.51

29.47

54.32

152.63

Q1 [W]

N/A

22.94

24.77

29.87

56.67

159.69Median [W]

N/A

23.07

24.9

29.94

56.74

160.25

Mean [W]

N/A

23.25

25.07

30.86

56.79

160.30Q3 [W]

N/A

23.2

25.03

33.14

56.81

160.85

Max [W]

N/A

33.47

36.34

41.06

70.6

175.19

Table 20: Power demand while running an Algorand full node measured in Watt [W].

Avalanche
Avalanche-Mainnet
1

2

3

4

5

6

Min [W]

N/A

N/A

N/A

6.66

26.67

104.64

Q1 [W]

N/A

N/A

N/A

7.04

26.99

108.37

Median [W]

N/A

N/A

N/A

7.12

27.12

109.31

Mean [W]

N/A

N/A

N/A

7.29

27.22

109.61Q3 [W]

N/A

N/A

N/A

7.38

27.32

110.51

Max [W]

N/A

N/A

N/A

13.72

39.28

121.52

Table 21: Power demand while running an Avalanche-Mainnet full node measured in Watt [W].

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Avalanche-DFK
1

2

3

4

5

6

Min [W]

N/A

N/A

N/A

9.41

29.15

113.41

Q1 [W]

N/A

N/A

N/A

12.81

32.89

118.89Median [W]

N/A

N/A

N/A

13.73

33.86

120.54

Mean [W]

N/A

N/A

N/A

13.86

34.00

120.67

Q3 [W]

N/A

N/A

N/A

14.77

34.96

122.18Max [W]

N/A

N/A

N/A

32.03

52.67

144.76

Table 22: Power demand while running an Avalanche-DFK full node measured in Watt [W].

Avalanche-DOS
1

2

3

4

5

6

Min [W]

N/A

N/A

N/A

6.86

26.98

105.62

Q1 [W]

N/A

N/A

N/A

7.32

27.38

111.16Median [W]

N/A

N/A

N/A

7.58

27.64

112.54

Mean [W]

N/A

N/A

N/A

7.73

27.73

112.61Q3 [W]

N/A

N/A

N/A

7.97

27.91

114.00Max [W]

N/A

N/A

N/A

15.16

35.89

122.31

Table 23: Power demand while running an Avalanche-DOS full node measured in Watt [W].

Cardano
1

2

3

4

5

6

Min [W]

N/A

N/A

N/A

3.85

25.22

81.11

Q1 [W]

N/A

N/A

N/A

4.11

25.62

82.23

Median [W]

N/A

N/A

N/A

4.18

25.81

82.63

Mean [W]

N/A

N/A

N/A

4.57

26.36

83.91

Q3 [W]

N/A

N/A

N/A

4.24

26.21

83.17

Max [W]

N/A

N/A

N/A

31.37

59.36

144.98

Table 24: Power demand while running a Cardano full node measured in Watt [W].

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CosmosCosmos-Hub
1

2

3

4

5

6

Min [W]

N/A

5.48

7.32

8.95

28.62

118.28

Q1 [W]

N/A

12.09

15.47

9.93

30.43

120.25Median [W]

N/A

12.81

18.10

10.39

31.10

121.91

Mean [W]

N/A

12.95

17.02

13.37

34.40

125.69Q3 [W]

N/A

13.86

19.30

11.04

31.96

123.86

Max [W]

N/A

29.61

30.13

37.84

63.47

158.37

Table 25: Power demand while running a Cosmos-Hub full node measured in Watt [W].

Cosmos-Injective
1

2

3

4

5

6

Min [W]

N/A

12.35

14.44

10.75

36.99

124.27

Q1 [W]

N/A

14.77

17.52

17.44

40.45

130.65Median [W]

N/A

15.62

18.36

19.35

42.09

132.84

Mean [W]

N/A

15.71

18.32

19.35

42.24

132.89Q3 [W]

N/A

16.53

19.02

21.05

43.99

135.13

Max [W]

N/A

21.37

23.14

26.20

49.03

149.00

Table 26: Power demand while running a Cosmos-Injective full node measured in Watt [W].

Cosmos-Bitsong
1

2

3

4

5

6

Min [W]

3.33

4.57

6.73

9.08

29.08

117.99Q1 [W]

3.59

4.96

7.12

9.60

30.26

119.25Median [W]

3.59

5.42

7.58

9.93

30.72

120.07

Mean [W]

3.63

5.69

7.70

10.18

30.90

120.43Q3 [W]

3.72

5.88

7.84

10.39

31.30

121.30Max [W]

4.18

10.72

12.09

13.47

35.36

127.84

Table 27: Power demand while running a Cosmos-Bitsong full node measured in Watt [W].

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Energy Efficiency and Carbon Footprint of PoS Blockchain Networks and Platforms

Ethereum
For Ethereum, different client software for both the execution and the consensus client are available, which
impact the electricity consumption of the network’s nodes. CCRI has recently analyzed the electricity
consumption of the Ethereum network in (CCRI, 2022b); all calculations conducted for Ethereum are basedon the measurements and findings of this report.

Polkadot
Polkadot-Relaychain
1

2

3

4

5

6

Min [W]

N/A

5.49

8.69

20.85

43.01

121.59Q1 [W]

N/A

5.88

9.15

21.31

44.06

122.74

Median [W]

N/A

6.01

9.21

21.44

44.32

123.09Mean [W]

N/A

6.07

9.27

21.46

44.32

123.16Q3 [W]

N/A

6.14

9.34

21.57

44.57

123.48Max [W]

N/A

22.94

15.49

23.53

46.80

131.05

Table 28: Power demand while running a Polkadot-Relaychain full node measured in Watt [W].

Polkadot-Astar
1

2

3

4

5

6

Min [W]

N/A

5.55

7.97

19.60

36.67

120.78

Q1 [W]

N/A

6.53

9.67

22.48

44.53

124.08Median [W]

N/A

6.86

9.93

22.74

45.30

124.87

Mean [W]

N/A

6.88

9.92

22.66

45.07

125.10Q3 [W]

N/A

7.12

10.19

22.94

45.82

125.76

Max [W]

N/A

18.42

16.47

25.55

49.68

141.41

Table 29: Power demand while running a Polkadot-Astar full node measured in Watt [W].

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Energy Efficiency and Carbon Footprint of PoS Blockchain Networks and Platforms

Polkadot-Composable
1

2

3

4

5

6

Min [W]

N/A

3.72

4.57

7.97

28.04

109.98

Q1 [W]

N/A

4.37

5.62

11.89

34.19

121.47

Median [W]

N/A

4.64

6.86

14.05

37.25

122.29Mean [W]

N/A

4.73

6.76

14.63

36.85

122.36Q3 [W]

N/A

4.96

7.57

17.25

39.74

123.23

Max [W]

N/A

12.74

12.81

22.55

44.90

140.75

Table 30: Power demand while running a Polkadot-Composable full node measured in Watt [W].

Solana
1

2

3

4

5

6

Min [W]

N/A

N/A

N/A

N/A

N/A

289.86

Q1 [W]

N/A

N/A

N/A

N/A

N/A

312.16Median [W]

N/A

N/A

N/A

N/A

N/A

320.33

Mean [W]

N/A

N/A

N/A

N/A

N/A

319.60Q3 [W]

N/A

N/A

N/A

N/A

N/A

327.15

Max [W]

N/A

N/A

N/A

N/A

N/A

362.47

Table 31: Power demand while running a Solana full node measured in Watt [W].

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Energy Efficiency and Carbon Footprint of PoS Blockchain Networks and Platforms

Appendix C: Data sources for single networks
All market capitalizations are taken from https://coinmarketcap.com.

AlgorandInformation
Measurement period2023-02-26 18:19 to 2023-02-27 18:19
https://metrics.algorand.org/#/decentralization/ → “Nodes”

Number of nodes(Number unavailable at time of analysis, average value taken from January 17 to
February 05, 2023)

Transaction Count

Software version

https://algoexplorer.io/top-statistics → “Transaction Metrics” → “Total
Transactions”
algorand-3.14.2 (non-relay node in non-archival mode)(https://github.com/algorand/go-algorand/releases/tag/v3.14.2-stable)
Table 32: Data sources for analysis of Algorand.

Avalanche
Avalanche-Mainnet
Information
Measurement period2023-02-19 19:44 to 2023-02-20 19:44

Number of nodes

https://avascan.info/blockchain/c/info → “Subnet Validators”

Transaction Count

https://avascan.info/blockchain/c/info → “Total transactions” (24h difference)

Software version

avalanchego-1.9.8(https://github.com/ava-labs/avalanchego/releases/tag/v1.9.8)
Table 33: Data sources for analysis of Avalanche-Mainnet.

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Energy Efficiency and Carbon Footprint of PoS Blockchain Networks and Platforms

Avalanche-DFK
Information
Measurement period2023-05-30 09:57 to 2023-05-31 09:57

Number of nodes

https://avascan.info/blockchain/dfk/info → “Subnet Validators”

Transaction Count

https://avascan.info/blockchain/dos/info → “Total transactions” (24h difference)

Software version

avalanchego-1.10.1 (subnet-evm 0.5.1)(https://github.com/ava-labs/avalanchego/releases/tag/v1.10.1)
Table 34: Data sources for analysis of Avalanche-DFK.

Avalanche-DOS
Information
Measurement period2023-05-31 19:29 to 2023-06-01 19:29

Number of nodes

https://avascan.info/blockchain/dos/info → “Subnet Validators”

Transaction Count

https://avascan.info/blockchain/dos/info → “Total transactions” (24h difference)

Software version

avalanchego-1.10.1 (subnet-evm 0.5.1)(https://github.com/ava-labs/avalanchego/releases/tag/v1.10.1)
Table 35: Data sources for analysis of Avalanche-DOS.

CardanoInformation
Measurement period2023-01-03 13:03 to 2023-01-04 13:03

Number of nodes

https://cardanoscan.io/ → “Total stake pools”

Transaction Count

https://explorer.bitquery.io/cardano/transactions → “Transactions By Date”

Software version

cardano-node-1.35.4(https://github.com/input-output-hk/cardano-node/releases/tag/1.35.4)
Table 36: Data sources for analysis of Cardano.

CCRI 2023

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Energy Efficiency and Carbon Footprint of PoS Blockchain Networks and Platforms

CosmosCosmos-Hub
Information
Measurement period2023-03-22 23:02 to 2023-03-23 23:02

Number of nodes

https://www.mintscan.io/cosmos/validators → “Validators”

Transaction Count

https://www.mintscan.io/cosmos → “Transactions” (24h difference)

Software version

gaiad-9.0(https://github.com/cosmos/gaia/releases/tag/v9.0.1)
Table 37: Data sources for analysis of Cosmos-Hub.

Cosmos-Injective
Information
Measurement period2023-03-31 18:53 to 2023-04-01 18:53

Number of nodes

https://www.mintscan.io/injective/validators → “Validators”

Transaction Count

https://www.mintscan.io/injective → “Transactions” (24h difference)

Software version

mainnet-1.10.0-1679065799(https://github.com/InjectiveLabs/injective-chain-releases/releases/tag/v1.10.01679065799)
Table 38: Data sources for analysis of Cosmos-Injective.

Cosmos-Bitsong
Information
Measurement period2023-04-07 00:10 to 2023-04-08 00:10

Number of nodes

https://www.mintscan.io/bitsong/validators → “Validators”

Transaction Count

https://www.mintscan.io/bitsong → “Transactions” (24h difference)

Software version

bitsongd-0.14.0(https://github.com/bitsongofficial/go-bitsong/releases/tag/v0.14.0)
Table 39: Data sources for analysis of Cosmos-Bitsong.

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Energy Efficiency and Carbon Footprint of PoS Blockchain Networks and Platforms

Ethereum
Information
Measurement period

different for each client software, see (CCRI, 2022b)

Number of nodes

https://migalabs.es/beaconnodes#act-nodes → “ACTIVE NODES”

Transaction Count
Software version

Dune Dashboard(https://dune.com/browse/dashboards)
different for each client software, see (CCRI, 2022b)
Table 40: Data sources for analysis of Ethereum.

Polkadot
Polkadot-Relaychain
Information
Measurement period2023-03-04 21:01 to 2023-03-05 21:01

Number of nodes

https://polkadot.subscan.io/validator → “Validators”

Transaction Count

https://polkadot.subscan.io/extrinsic → “Extrinsic History”

Software version

polkadot-0.9.38(https://github.com/paritytech/polkadot/releases/tag/v0.9.42)
Table 41: Data sources for analysis of Polkadot-Relaychain.

Polkadot-Astar
Information
Measurement period2023-03-14 17:12 to 2023-03-15 17:12

Number of nodes

https://astar.subscan.io/collator → “Candidate Collators”

Transaction Count

https://astar.subscan.io/extrinsic → “Extrinsic History”

Software version

astar-collator-4.49.0(https://github.com/AstarNetwork/Astar/releases/tag/v4.49.0)
Table 42: Data sources for analysis of Polkadot-Astar.

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Energy Efficiency and Carbon Footprint of PoS Blockchain Networks and Platforms

Polkadot-Composable
Information
Measurement period2023-03-18 01:55 to 2023-03-19 01:55

Number of nodes

https://composable.subscan.io/collator → “Candidate Collators”

Transaction Count

https://composable.subscan.io/extrinsic → “Extrinsic History”

Software version

composable-node-2.10009.0(https://github.com/ComposableFi/composable/releases/tag/releasev2.10009.0)
Table 43: Data sources for analysis of Polkadot-Composable.

SolanaInformation
Measurement period2023-02-01 12:40 to 2023-02-02 12:40

Number of nodes

https://www.validators.app/ → “Cluster” → “Validators”

Transaction Count

Software version

Dune Dashboard(https://dune.com/browse/dashboards)
solana-1.13.6(https://github.com/solana-labs/solana/releases/tag/v1.13.6)
Table 44: Data sources for analysis of Solana.

CCRI 2023

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Energy Efficiency and Carbon Footprint of PoS Blockchain Networks and Platforms

Disclaimer
PurposeThe CCRI PoS Benchmark Study 2023 provided herein is intended for informational purposes only. The PoS
Benchmark Study 2023 does not constitute a recommendation or suggestion, directly or indirectly, to buy, sell,hold, make, or undertake any investment or trading strategy with respect to any investment, loan, commodity,
security, or any issuer. It should not be construed as financial, investment, legal, or tax advice. CCRI does notprovide personalized advice, and the study should not be considered as a substitute for professional advice
tailored to your specific circumstances. It is important to note that investment decisions should be based onyour own assessment and understanding of the market, and not rely on the information presented in thisstudy. You should conduct your own research and seek guidance from independent financial, tax, or legal
advisors before making any investment decisions. CCRI shall not be held accountable for any financial losses
or damages incurred as a result of following or relying on the study's content. CCRI does not guarantee thesequence, accuracy, completeness, or timeliness of any information provided in this PoS Benchmark Study.
The content speaks only as of the indicated date, and any projections, estimates, forecasts, targets, prospects,
and/or opinions expressed herein are subject to change without notice and may differ or be contrary toopinions expressed by others.

Competing interests
This CCRI report was created with financial support of Avalanche.

CCRI 2023

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Energy Efficiency and Carbon Footprint of PoS Blockchain Networks and Platforms

About CCRI

CCRI – Crypto Carbon Ratings Institute – is a research-driven
company

providing

cryptocurrencies,

data

blockchainonandsustainability
otheraspectsoftechnologies.

The

interdisciplinary team has built a multi-year research track record
with a specific focus on cryptocurrencies and their sustainability
impacts. CCRI uses the most up-to-date data sources as well as
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,
exchanges and blockchain networks.

© Crypto Carbon Ratings Institute, 2023
Crypto Carbon Ratings Institute (CCRI) is a brand of CCRI GmbH based in Dingolfing, Germany.

CCRI 2022

https://carbon-ratings.com

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