Energy Efficiency and Carbon Footprintof PoS Blockchain Protocols
January 2022

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Energy Efficiency and Carbon Footprint of Proof of Stake Blockchain Protocols

Energy Efficiency and Carbon Footprint of
Proof of Stake Blockchain Protocols
January 2022

Ulrich Gallersdörfer, Lena Klaaßen, Christian Stoll
Crypto Carbon Ratings Institutecarbon-ratings.com

PreambleThis report is prepared by the Crypto Carbon Ratings Institute (CCRI) for
Avalanche Inc.

Executive summary•

The electricity consumption and carbon footprint of Proof of Work (PoW)-based
cryptocurrencies such as Bitcoin remain significant.

•

Blockchain networks based on alternative consensus mechanisms such as Proof of Stake (PoS)
consume significantly less electricity.

•

For the 6 PoS networks assessed in this study, we find a variance in total yearly electricity
consumption of 28x – ranging from 70 MWh (Polkadot) to 1,967 MWh (Solana).

•

Comparing electricity consumption per node or transaction shows different rankings of the 6
PoS networks.

•

Electricity consumption can be translated into carbon emissions via emission factors ofelectricity generation to gauge climate impacts.

•

Continuous development and evolution of networks require regular updates of measurement
and analyses.

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1 Introduction
The electricity consumption and related carbon footprint of Bitcoin and other cryptocurrencies are
subject to extensive discussion in public, academia, and industry. For these protocols, various
estimations exist, comparing Bitcoin's electricity consumption to countries like Norway (de Vries, 2021;
Stoll, Klaaßen, & Gallersdörfer, 2019). The problem has been known for several years, and othersystems and technologies have emerged to solve the issue. The consensus family of Proof of Stake(PoS) is deemed superior regarding the electricity requirements compared to the traditional Proof ofWork (PoW) consensus mechanisms (King & Nadal, 2012). While it is consensus in the broader scientificcommunity that PoS could solve the electricity issues of PoW, it is unclear how these PoS systems
compare to each other.
Instead of requiring computational power to solve mining puzzles for securing the network in PoW,
PoS requires validators to lock in funds for a specific period of time to propose or vote on new blocks.
Due to the nature of the software engineering process and network architectures, different PoS
systems rely on varying fundamentals regarding the hardware requirements, programming language,
network size, transaction throughput, transaction complexity, and more. These factors influence theelectricity consumption and, therefore, the carbon footprint of a respective network. While it isexpected that the overall differences between PoS networks are minor, it is nonetheless essential tounderstand the absolute and relative energy efficiency of single networks (Gallersdörfer, Klaaßen, &Stoll, 2020).
In this report, we provide an analysis of the electricity consumption, carbon footprint, and influencing
factors of six major Proof of Stake-based cryptocurrencies. We rank these currencies in terms ofelectricity consumption, carbon footprint, and other factors such as transaction throughput and energy
efficiency. Table 1 summarizes all results.

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Table 1: Overview of results. Green cells mark the best value for the respective column.

CardanoNodes[# total]

Transactions[Tx/year]aTotal electricity
consumption[kWh/year]

Electricity pernode [kWh/year]

Electricity pertransaction[Wh/Tx]Total carbon
emissions[tCO2e/year]

3,002

11.9 mn

598,755

199.4551.59

284.41

Polkadot

297

4.0 mn70,237236.49

17.42

33.36

Solana1,015

11.8 bn

1,967,930

1,938.85

0.166

934.77Tezos

375

2.5 mn113,249

250.99

41.45

53.79

Avalancheb

1,084

93.9 mn

489,311451.394.76

232.42

Algorand

1,190

190.0 mn

512,671430.822.70

243.52This report is outlined as follows: In chapter 2, we define the aim and scope and the selected
cryptocurrencies to understand the goals and limitations of this report. We describe the methodologyof how this study is conducted in Chapter 3. Chapter 4 outlines the selected hardware and theinfrastructure established for electricity measurements. Chapter 5 provides the calculation of theelectricity consumption and carbon footprint of the respective networks. In Chapter 6, we analyze theelectricity consumptions of the networks in the context of other metrics. Chapter 7 concludes with asummary and an outlook on the future of the environmental impact of PoS-based networks.

a

We assume the amount of transactions occurred during our measurement for a daily basis.
These numbers represent the numbers for Avalanche network, including a major update in the node software
implemented in September/October 2021. The initial measurement performed prior to the update in August2021 yielded the following results: Total energy consumption [kWh/year]: 144,384 | Electricity per node
[kWh/year]: 139.77 | Electricity per transaction [Wh/Tx]: 14.27 | Total carbon emissions [tCO2e/year]: 68.58
b

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2 Aim and scopeThis report aims to provide insights into the electricity consumption and carbon footprint of thecurrent state of PoS networks. To do so, we present a methodology which we describe in Chapter 3.
It is noteworthy that the methodology is a helpful tool to derive a ballpark estimate for total electricity
consumption and carbon emissions as well as the relative performance. However, the networks areassociated with uncertainties that impede deriving exact numbers of the electricity consumption or,
respectively, of the network's carbon footprint. Numerous factors, such as the network size, varying
hardware configuration, or network infrastructure, influence the overall electricity consumption.
Nonetheless, we deem this report to produce the most precise electricity consumption and carbon
footprint estimates for these cryptocurrencies to date, as we observe and measure the electricity
consumption of single hardware components and use them as a proxy for the overall network.
The establishment of representative hardware, network sizes, and electricity measurements form thebasis for future research, such as comparing different networks and their respective requirements andproperties. Identical assumptions (e.g., selected hardware) for networks and adjustments for theirrequirements (e.g., the hardware requirements for some networks are higher than others) allow tobuild a valid data set for the comparison of these networks.
For our analysis, we select all Proof-of-Stake networks in the Top 40 of cryptocurrency market
capitalization according to coinmarketcap.com on 7th of May 2021c. Table 2: Overview of PoS-based
cryptocurrencies by market cap and rank among all cryptocurrencies displays the respective currency
with their rank, name, ticker symbol and the market capitalization. Their combined market
capitalization is about $ 117 bn USD, a share of about 4.9 % of the overall market capitalization of all
cryptocurrencies.
Table 2: Overview of PoS-based cryptocurrencies by market cap and rank among all cryptocurrencies as of the 7th of May
2021
Rank

Name

SymbolMarket Capitalization

7

CardanoADA$ 52.8 bn

8Polkadot

DOT$ 37.4 bn

17

SolanaSOL$ 11.8 bn

33

Tezos

XTZ

$ 5.5 bn

37

Avalanche

AVAX$ 4.9 bn

38

AlgorandALGO$ 4.7 bn
$ 117.1 bnchttps://coinmarketcap.com/historical/20210507/

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3 Methodology
Our methodology builds upon four steps to generate data on the electricity consumption and carbon
footprint of PoS-systems and additional data to develop metrics for a valid comparison between thesystems.
In the first step, we analyze the selected PoS-systems and their minimum hardware requirements. The
hardware requirements are an indicator of the hardware composition of the network. We use thisinformation and additional hardware data from PassMark to select and obtain hardware that we use
to measure a single node's electricity consumption.
In the second step, we estimate the electricity usage of a single node and provide upper and lower
bounds for the networks. We start by running the software on all obtained hardware devices andmeasure their single electricity consumption while running the network and while idling. We alsomeasure other data points to be able to evaluate additional metrics. These values allow us to produce
reasonable upper and lower bounds for running a single node, as our hardware is selected accordingly.
In the third step, we estimate the electricity consumption of the complete network. Firstly, we collectinformation about the size of the network, as the node count significantly influences the amount ofelectricity consumed. Secondly, we develop a weighting between the single hardware devices for each
network. Lastly, we multiply the electricity consumption of the weighed nodes by the number of nodes
in the network. In case the distribution of nodes for all networks is available, we use the respective
carbon intensity factors of the regions to calculate the carbon footprint of the respective network.
Otherwise, we rely on an average global carbon intensity factor.
In the fourth step, we analyze the additional data (such as transaction data) to develop additional
metrics to explore energy efficiency in transaction throughput. This allows us to put the electricity
consumption of single networks into perspective with other PoS networks and also othercryptocurrencies such as Bitcoin and Ethereum.

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4 Proof of Stake systems, hardware requirements, and selected hardware
The six proof of stake networks we selected do not employ identical algorithms and have different
prerequisites in terms of hardware, network size, transaction throughput, and other properties. For
example, some of them support stake-delegation, meaning that one node can stake on behalf of otherentities in the network which do not need to run a machine themselves. While this allows for someefficiency gains and allows users with lower funds to participate in the revenue generation of staking,it comes at the price of trustworthiness and decentralization. The first part of this chapter takes acloser look at the single PoS systems and hardware requirements. In the remaining two parts of thechapter, we describe the hardware selection for running the nodes and the infrastructure required tomeasure electricity consumption.

4.1

Overview of PoS systems and their hardware requirements

The systems previously selected in chapter 3 have different requirements for the hardware of their
network participants. The hardware requirements are partly unspecific: Tezos, for instance, specifies
only the amount of CPU cores required to run their software. For the consumer market, the first twocore CPU was released in 2005 (Intel® Pentium® Processor Extreme Edition 840 (Intel, 2005)), deeming
almost any CPU released in the last 15 years suitable for running Tezos (although we did not test).
Other networks are more precise or have higher requirements. 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) and states that a node might profit fromusing a GPU. It is noteworthy that Polkadot does not provide a minimum hardware requirement but
rather links to a recommended setup. Algorand provides two sets of requirements, one for regular and
one for enterprise nodes, although it remains unclear what is meant by that.
Table 3: Hardware requirements of PoS networks
Name

CardanoPolkadot

SolanaTezos

Avalanche

AlgorandCPU

2x2GHzi7-7700k

12x2.8GHz

2 cores> 2GHz4 / 16 cores

RAM

8 GB

64 GB

128 GB

8 GB

6 GB

4-8 / 24 GB

Storage

30 GB

80-160 GB

2TB

100 GB

200 GB

100 / 500 GB

SSD/NVMe

N/A

NVMe

NVMe

SSD

N/A

SSD

(Minchev, 2021)(Algorand
Foundation,
2021)

Source4.2(IOHK, 2021)(Polkadot, 2021)(Solana, 2021)(Heng, 2020)

Hardware selection

Based on the hardware requirements outlined in the previous section, we define three separate
categories of hardware requirements:
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Low hardware requirements: Cardano, Tezos, Avalanche, and Algorand specify 2 to 4 CPU
cores, 4-8 GB RAM, and under 200 GB. These requirements are somewhat imprecise. However,this hints that computational power is not a concern for these systems, and users should be
comfortable running the software on any system they have available. All of these networks(except for Cardano) recommend using low-energy hardware such as the well-known
Raspberry Pi for running nodes within the respective networks. 4-8 GB RAM and 200 GB of
storage (even an SSD) are not uncommon anymore in today's average consumer desktop PC.

•

Specific hardware requirements: Polkadot specifies the most precise hardware requirementswith the exact CPU type as well as RAM and storage. While they list it as "Requirements –Standard Hardware", the description instead clarifies that they meant "We are able to run
Polkadot on this machine". While we use hardware that satisfies Polkadot's requirements, we
also run Polkadot on hardware that does not meet the requirements and include them in our
calculation if they are able to run a node reliably. 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: Solana has surprisingly high hardware requirements. The CPU,
RAM, and storage requirements are at the highest level of standard desktop computers(besides servers). They mention that the AMD Threadripper Zen3 family is famous among thecommunity. They also mention that graphic cards could be required in the future, which hints
at the immense required processing power.

Based on the hardware requirements, both an upper and a lower bound of hardware are evident. For
the lower bound, as of the popularity of the Raspberry Pi computers is high within all communities(and outside), we select a Raspberry Pi 4 Model B with 8 GB RAM and 128 GB SD-card as the lower
bound. We opt for an official Raspberry Pi full kit, including fan and power supply.
Solana's hardware requirements define the upper bound. We opt for an average system within the
Threadripper specifications consisting of an AMD Ryzen Threadripper 3970X, 32C/64T, 256 GB RAM(DDR4-3600), and a Samsung 970 Evo Plus 2TB. As the processor does not have an onboard graphics
processor, we need a graphics card. However, as a graphics card is not required at that time, we opt
for a card that does not support CUDA and cannot participate in the calculations of Solana or any othernetwork. We select an appropriate mainboard as well as a power supply.

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The upper and lower bounds highly deviate from each other in terms of computational power andelectricity consumption. Further, the two computers may not capture the complete picture of the
hardware used within these networks. Therefore, we decided to add three additional computers toensure a well-balanced set of hardware for electricity consumption measurements.
As there are millions of different computer configurations, thousands of variables, and other factorsthat influence the electricity consumption of devices, we opt for one key variable and derive otherspecifications of the system from it: The central processing unit (CPU). Nonetheless, also the CPU has
several variables as the number of cores, threads, speed, turbo speed, thermal design power (TDP),
and others. Further, identical variables do not necessarily lead to the same computational power orelectricity consumption. To get an in-depth view and understanding of the CPU landscape, we obtaina 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 over1 million results of their benchmarking suite (Passmark Software, 2021). Based on this data set, weselect three CPUs to derive our final configurations. We thereby aim at three categories of
performance (high, mid, and low) and select a CPU 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 4), we identified the Intel Core i5-10400F as being closest to theaverage efficiency. As Intel's F-models only have a deactivated onboard graphics chip (Intel, 2021), we
decided to opt for the non-F 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 tobenchmarking results. We opted for 32 GB DDR4 RAM and a Samsung 970 Evo Plus 512 GB NVMe SSD
to complement the system. Mainboard, power supply unit, and case have been selected appropriately.
The Intel Core i5-8400T 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 andinstead opted for a completed build: The Lenovo ThinkCentre M720q Tiny 10T8S3KD00. Besides theprocessor as mentioned above, it includes a 256 GB 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 anaverage energy efficiency for its class. The U-label refers to a "Mobile power-efficient" CPU but isnonetheless included in MiniPCs. To our knowledge, this CPU was never sold separately on the
consumer market but is available in Intel's NUC series. We obtain the Intel NUC Kit NUC8i3BEK2Barebone and augment it with the Samsung 970 Evo Plus 512 GB NVMe SSD as well as 8 GB RAM.
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We consider our selection as representative to provide a balanced set of hardware for electricity
measurements with these five computers. As an operating system, we use for all our devices Ubuntu
Server 20.04, except for configuration 4. Due to driver issues, we had to opt for Ubuntu Server 21.
Table 4 displays an overview of the hardware configurations. 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.
Table 4: Overview of selected hardware from lowest to highest requirement
Category

1

2

3

4

5

Broadcom
BCM2711Intel i3-8109UIntel i5-8400TIntel i5-10400

AMD 3970X

ArchitectureARM

x86/x64x86/x64x86/x64x86/x64

RAM

8 GB

8 GB

8 GB

32 GB

256 GBCPU

Storage

128 SD512 SSD

256 SSD

512 SSD

2 TB SSD

GPU

OnboardOnboardOnboardOnboard

AMD 6970

PSU

USB-C

65 Watt

65 Watt

650 Watt

1000 Watt

Case

Integrated

Integrated

Integrated

CustomCustomOS

Ubuntu 20.04

Ubuntu 20.04

Ubuntu 20.04

Ubuntu 21

Ubuntu 20.04

4.3

Infrastructure for electricity measurements

For the measurement of the electricity consumption, we use five Mystrom WiFi Switch which measure
the electricity consumption as well the room temperature and provide them over a REST interface.
The electricity measurements are made in Munich, Germany in a separate basement room with nearconstant room temperature. The average room temperature during the measurement period was
between 18°C and 22°C.
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 suchas temperature or system load.
A separate Raspberry Pi, equipped with a Python script, collected and monitored the five systems andanalyzed the data generated during the runs. All computers are only connected to the power outlet
and LAN. All systems share a 100 Mbit internet connection.

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5 Electricity consumption of Proof of Stake networks
The definition of the to-be used hardware allows us to establish single node measurements. With thesemeasurements, we provide upper and lower bounds for the electricity consumption of a single nodeand the best guess as a weighted average between the computer devices. On that basis, we establish
the electricity consumption of the respective overall network and discuss additional metrics such as
the electricity use per transaction.

5.1

Single node measurements

After defining and obtaining the hardware required for our analysis, we set up the hardware and install
the node software for the respective network. For that, we use the following process:-Hardware Setup: We install the node with the respective Linux version, configure Glances andconfigure remote access.

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

-

Node Setup: We download and install the node software and verify the correct installation.

-

Node Bootstrap: The single nodes are fully synced, as we do not want to skew the electricity
consumption of the devices, as some might process data faster.

-Electricity Measurement: We shut down the node, start the electricity measurement and thenstart the node again. The node runs for 24 hours, as this covers an entire day cycle. Appendix
B contains an overview of every electricity measurement.

To understand what exactly we are measuring, we need to describe the network and its setup. The
network usually consists of nodes, either validators (participating in the consensus protocol andproducing new blocks) or regular full nodes (broadcasting and verifying regular transactions).
Validators are selected to propose 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. On the Main net, significant stakes are required to run a validator; on the available
test nets, we were not able to generate blocks due to the low share of the overall stake. Furthermore,
previous research suggests that participating in the PoS consensus mechanism has only a negligible
effect on the device's electricity consumption (Sedlmeir, Buhl, Fridgen, & Keller, 2020). Therefore, we
run our electricity measurement on regular full nodes running on the Main net.

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5.1.1 Idle electricity consumption
We measure the electricity consumption of the devices idle. Table 5 depicts the minimum, maximum,
median, and quantile 1 and 3 of the electricity consumption for 24 hours. All values are rounded to
one decimal. Interestingly, the setup 2 consumes less electricity than the Raspberry Pi (configuration1), which we deemed the most energy-efficient solution beforehand.
Table 5: Electricity consumption in Idle measured in Watt [W] – hardware selection for each of the five clusters can be foundin Table 4
1

2

3

4

5

Min [W]

2.3

2.2

2.6

23.5

75.2

Q1 [W]

2.9

2.4

2.7

24.9

76.7

Median [W]

3.1

2.5

2.8

25.4

77.8

Q3 [W]

3.1

2.5

2.8

25.1

77.1

Max [W]

3.3

2.6

2.8

25.5

78.5

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 6, we give an overview about which measurements take place on which machines. As we do notwant to enforce the hardware requirements as a strict lower bound, we also test the lower-tier device
if software is available (e.g., due to Raspberry Pi’s architecture, software might not be available).
Table 6: Hardware configurations used for measuring the electricity consumption of networks. (🗸 (yes), ~ (test), 🗶 (no), theresult of the test is given in brackets)
Config.

CardanoPolkadot

Avalanche

AlgorandTezos

Solana

1

🗶

🗶

~ (🗶)

~ (🗸)

🗸

🗶

2

🗸

~ (🗸)

🗸

🗸

🗸

🗶

3

🗸

🗸

🗸

🗸

🗸

🗶

4

🗸

🗸

🗸

🗸

🗸

~ (🗶)

5

🗸

🗸

🗸

🗸

🗸

🗸

We outline challenges that occurred during the electricity measurements for some of the networks.
Due to technical restrictions, we were not able to run all nodes in Avalanche simultaneously
throughout a period of time but had to test [2], [3,4], and [5] hardware separately. The hardware used
for establishing an Internet connection crashed reproducibly when we started five nodes
simultaneously. Therefore, we tested on different days accounting and controlling for the respective
circumstances, such as transaction throughput. Further, PC 1 was not able to catch up to the network,
therefore, we excluded this configuration from our calculations.

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Cardano outlines in its requirements that an IPv4 address is necessary to connect to the network. We
obtained an IPv4 address from our ISP to be able to run fully functional nodes. Due to this limitation,
it can be assumed that a large portion of the network either is deployed on-premise or use hosting
solutions, as anecdotical evidence suggests that a large share of private internet connections only
natively support IPv6 and use Dual Stack Lite for IPv4 services. Future research could look into how thisaffects the hardware distribution of the Cardano network.
We also had issues with running Tezos on all five devices for 24 hours. During several runs, some nodes
were unable to continue their work in the network and the CPU utilization dropped to zero percent.
To account for this, we removed the respective timeslot (six hours are missing for computer 4) andonly consider the time the nodes were fully connected and functional.
Solana poses high requirements for the internet connection. The same problems that occurred during
the Avalanche measurements also occurred while running a single node on Solana. Due to that
limitation, we relocated configuration number 5 to a different place with a 1 Gbit internet connectionin which the node worked as expected. Noteworthy: Solana also requires an IPv4 address, but does
not state so in its requirements.
In Table 7, we outline the mean consumption of all networks and nodes.
Table 7: Mean consumption of nodes for the respective network in Watt [W]
1

2

3

4

5

Algorand

5.53

34.89

32.23

69.95

168.59Avalanche

N/A

23.44

24.03

57.34

144.67

Cardano

N/A

3.90

3.70

27.56

84.47

Polkadot

N/A

4.31

5.35

29.25

107.86

Tezos

4.86

19.25

19.74

52.41

141.65

Solana

N/A

N/A

N/A

N/A

221.33

Two currencies are very close to each other in terms of the electricity consumption of the single nodes.
Cardano and Polkadot only deviate within the range of +/- 10 % of each other throughout all hardware
configurations. On the other hand, Algorand seems to consume most throughout the networks that
support all devices. The Raspberry Pi (configuration 1) seems to be running at almost maximum
capacity, consuming 5.5 watts. Also for configuration 2 and 3, Algorand consumes up to the factor of
9 compared to other cryptocurrencies such as Cardano and Polkadot. Tezos is in between these twogroups. It consumes up to factor 5 of the low-energy blockchains, but only about 50% of Algorands
consumption. Solana is different due to its high hardware requirements. It only runs on configuration

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5 and consumes the most out of any network-device combination with over 220 watts while running.
Avalanche consumes electricity similar to Tezos while not supporting configuration 5.

5.2

Calculation of bounds for electricity consumption

To calculate the electricity consumption of the overall networks, we need to understand the averagecalculation for a single node. We measured the electricity consumption for five different computers.
With these measurements, we can provide upper bounds, meaning the highest electricity that a nodeconsumes, lower bounds, the least electricity a node consumes, and a best guess that captures theconsumption of the average node best for the respective network.

5.2.1 Upper and lower boundThe upper and lower bound are measured by the least efficient and most efficient hardware,
respectively. The lower bound therefore is constituted by the Raspberry Pi (configuration 1) for
Algorand and Tezos. Configuration 2 is used for Avalanche, Cardano, and Polkadot as a lower bound.
Configuration 5 serves both as the lower bound for Solana and as an upper bound for all currencies, as
for Solana only one device is available. The respective upper and lower bounds are displayed in Table
8.
Table 8: Overview of lower and upper bounds for the respective network per single node
Lower bound[watts]Lower bound[kWh / year]

Upper bound[watts]Upper bound[kWh / year]

Algorand

5.53

48.47

168.591,476.81

Avalanche

23.44

205.33144.67

1,267.31

Cardano

3.90

34.15

84.47

740.00Polkadot

4,31

37.72

107.86944.88

Tezos

4.86

42.58

141.651,240.89

SolanaN/A

N/A

221.33

1,938.85

5.2.2 Best guess
The electricity consumption of an average node in the networks is challenging to estimate. There is noempirical data on the concrete hardware that nodes are running on or indicating user's 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 long-term operations. Second, due to the profit structure, they do not intend
to spend all their revenue on hardware and might rather opt for 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

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the hardware selection, based on a regular distribution for key questions. The distribution for eachhardware type is displayed in Table 9.
Table 9: Overview of node distribution for the six networks
1

2

3

4

5

Nodes

Algorand, Tezos

6.25 %

25.00 %

37.50 %

25.00 %

6.25 %

Nodes

Avalanche, Cardano, Polkadot

N/A

12.50 %

37.50 %

37.50 %

12.50 %

Nodes

Solana

N/A

N/A

N/A

N/A

100 %

With this distribution, we calculate the weighted electricity consumption of an average node:∑𝑎𝑣𝑔𝐸𝑛𝑒𝑟𝑔𝑦𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝑖 ∗ 𝑠ℎ𝑎𝑟𝑒𝑖

𝑖 ∈ ℎ𝑎𝑟𝑑𝑤𝑎𝑟𝑒

An average node in the networks consumes from 22.77 watts for Cardano to 221.33 watts for Solana.
This difference of over factor 13 implies that not only the number of nodes are relevant for theelectricity consumption of PoS networks, but also the underlying software and its requirements. Table
10 gives an overview about the best guess electricity consumption for every network.
Table 10: Overview of best guess estimates per single node
Best guess [watts]

Best guess [kWh / year]

Algorand

49.18

430.82

Avalanche

51.53

451.40Cardano22.77

199.45

Polkadot

27.00

236.49

Tezos

34.47

302.00

Solana221.33

1,938.85

Additionally, we display the electricity consumption estimates for the upper and lower bound as wellas the best guess for each network in Figure 1.

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Figure 1: Overview of lower bound, upper bound and best guess estimates for a single node of the respective network in watts

5.3

Electricity consumption of the networks

We apply our lower bound, upper bound as well as our best guess at the number of nodes in therespective networks. We obtain the number of nodes from the respective block explorers. Appendix C
gives an overview of all data sources. The results are depicted in Table 11.
Table 11: Overview of electricity consumption of the respective networks applying the best guess estimate
Node countElectricity intensity network [W]Consumption / day [kWh]Consumption / year [kWh]Algorand

1,190

58,524.08

1,404.58

512,670.92

Avalanche

1,084

55,858.52

1,340.58

489,311.19

Cardano3,002

68,351.08

1,640.43

598,755.44

Polkadot

297

8,018.01

192.4370,237.76

Tezos

375

12,928.06

310.27113,249.81

Solana1,015

224,649.62

5,391.59

1,967,930.68

The electricity consumption of the networks amounts from 70 to 1,967 MWh annually in our best
guess, Polkadot with the lowest electricity consumption and Solana with the highest electricity
consumption in the field. These results imply that there is a factor of more than 28 between theelectricity consumptions of the different networks, hinting at the respective carbon footprint. Figure 2
gives an overview of the electricity consumptions of the networks and their respective node count incomparison.

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Figure 2: Electricity consumption per year [kWh] and count of nodes of the respective networks

5.4

Electricity consumption per transaction

An often-used metric in comparing electricity consumption between systems is the electricity
consumption per transaction. This allows comparing systems that have different architectures,transaction throughput, and electricity requirements. Nonetheless, companies that want to reportemissions associated with cryptocurrency expose should not use a transaction-based allocation
approach and should rely on other methodologies (Gallersdörfer, Klaaßen, & Stoll, 2021).
The complexity of this metric is based on the fact that some systems provide a theoretical electricity
consumption per transaction, simulating the network at full speed. Other calculations are based ontransaction rates measured in the networks, making comparisons skewed. Further, the definition of atransaction might vary from network to network.
An additional complexity is the attribution of the electricity consumption solely to the transactions.
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 "low-transaction"period might yield higher electricity per transaction costs than usually to be expected. While this metricprovides a straightforward insight into different protocols, its base assumptions need to be understoodand its results must be treated with care.
As we measured the electricity consumption of our nodes in real-world scenarios, we also apply thetransaction numbers that took place during the respective time periods. As we sometimes havedifferent time slots for when we tested our hardware (for Avalanche and Tezos), we also apply the

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respective transaction rates before we weigh the single nodes for the overall network. Thereby wealso apply the previously described binomial distribution.
Table 12: Best guess electricity consumption of PoS networks on a per-transaction basis. The weighted average oftransactions is the weighted number of transactions that took place on the respective blockchain during our measurements.
Wh/tx per node

Wh/tx per network

Weighted avg. of tx

Algorand

0.002272.70

520,417

Avalanche

0.00439

4.76

276,907

Cardano0.01718

51.59

32,559Polkadot

0.05865

17.42

11,037Tezos

0.11054

41.45

7,033

Solana0.000160.166

32,383,318

The range for the electricity consumption per transaction goes from 0.166 watthours for Solana up to
51.59 watthours for Cardano. As expected, this metric depends on the amount of transactions taking
place on the respective blockchain, also the overall electricity consumption per transaction further
depends on the number of nodes connected to the respective network. Generally, these numbers are
expected to go down with an increase in the transaction rate, regardless which blockchain is in use.

5.5

Carbon footprint of PoS networks

The electricity consumption of any system has no direct environmental impact, as mere usage does
not cause any harm. However, the indirect impacts due to the carbon intensity of the underlying
energy sources used for electricity consumption do cause damage to the environment and need to beconsidered for sustainable business operations.
Depending on the underlying energy sources, the respective carbon footprint of electricity
consumption can vary. For a precise estimate of the carbon footprint, two pieces of data are essential:
The location of the electricity consumers as well as the carbon intensity of the respective grid.
There are several ways for local electricity consumers to ensure that their energy is carbon neutral andhas no displacement effect, meaning that other local electricity consumers are pushed fromsustainable energy into fossil fuels. Corporate Power Purchase Agreements (PPAs), unbundled energy
attribute certificates (EACs), or off-grid electricity production for self-consumption are ways to ensurethat an entity is carbon-neutral from an electricity consumption perspective. As these are instruments
aimed at energy-intensive industries or large corporations, we do not assume that any node relies onsuch methodologies and apply an average grid intensity factor.

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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 thenodes are distributed worldwide. Unfortunately, structured data on the location of nodes in all
networks is not available.
Due to the absence of such data, we rely on the average grid intensity worldwide. A formula tocalculate the respective carbon footprint is shown.
∑ 𝐸𝑛𝑒𝑟𝑔𝑦𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝑖 ∗ 𝑐𝑎𝑟𝑏𝑜𝑛𝐼𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦𝑖 ∈ 𝑛𝑜𝑑𝑒

We assume the carbon intensity of the grid to be 475 g CO2e/kWh. We derive this value from theInternational Energy Agency (IEA) Global Energy & CO2e Status Report (International Energy Agency,2018). With that, we can derive the carbon footprint of all networks. The respective values are depicted
in Table 13 and in Figure 3.
Table 13: Overview of CO2e emissions of the networks on an annual basis
CO2e emissions / year [t]Lower boundUpper boundBest guess

Algorand

27.40

834.77243.52

Avalanche

105.74652.54232.42Cardano48.69

1055.20284.41

Polkadot

5.32

133.30

33.63

Tezos

7.58

221.03

53.79

SolanaN/A

934.77934.77

Figure 3: Carbon emissions per year [in tonnes] of each network for lower bound, upper bound and best guess estimate.

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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 six Proof of Stake networks. In this chapter, we
contextualize the results of our work.

6.1

Yearly electricity consumption in the context of other systems

The electricity consumption of a single network is often meaningless without a context to compare thesystem. In chapter 5, we outline that the yearly electricity consumption of the networks range from70,000 kWh to roughly 1,900,000 kWh. An average US household consumes about 10,600 kWh peryear and therefore, the least electricity consuming network Polkadot consumes about 6.6 times theelectricity and the most electricity consuming network Solana about 200 times the electricity (U.S.
Energy Information Administration, 2021). In comparison to the decentralized cryptocurrency Bitcoin,
PoS networks consume less than 0.001 % of the Bitcoin network assuming 89.78 TWh on the 21st
August 2021 (CBECI, 2021). Bitcoin consumes much more electricity than any Proof of Stake system
due to its Proof of Work consensus mechanism, resulting in the deployment of energy-intensive
hardware. In Figure 4 , we compare the Bitcoin network, the Ethereum network assuming 17.3 TWhon the 21st August 2021d, the PoS networks, and the average US household.

Figure 4: Yearly electricity consumption for Bitcoin, Ethereum, Proof of Stake networks Polkadot, Tezos, Avalanche, Algorand,
Cardano and Solana, and an average US household in kWh. Logarithmic scale.

d

We used the methodology outlined in (Gallersdörfer et al., 2020) and applied it to the respective date.

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6.2

Electricity consumption per transaction

In chapter 5, we also calculate the electricity costs per transaction for each network. We compare
these numbers to other approaches such as Visa, Bitcoin and Ethereum. Visa consumes about 1.5 Wh
per transaction, while Bitcoin consumes about 1722.24 kWh per transaction (Statista, 2021). We obtainthe figure 37 kWh per transaction from own calculations based on (Gallersdörfer et al., 2020). Figure
5 gives a comparison of these systems. Generally, it is not surprising that a centralized system like Visa
is more energy-efficient than a decentralized system. Due to the difference in the amount ofcomputational hardware and distribution worldwide, most systems must consume more electricity.
Due to its Proof of Work (PoW), Bitcoin and Ethereum consume much more on an overall and on a pertransaction basis.

Figure 5: Electricity consumption [Wh] per transaction for Bitcoin, Ethereum, Visa, and all PoS systems. Logarithmic scale.

6.3

The carbon footprint of Proof of Stake networks

Overall, the emissions of the PoS networks are very low. As outlined in Chapter 5, the networks emit
from 33.36 for Polkadot to 934.77 tonnes for Solana of CO2e yearly. For example, nine round trips from
Munich (MUC) to San Francisco (SFO) in business class emit about the same amount of carbon dioxide(MyClimate, 2021) produced by the Tezos network. It can be assumed that the carbon emissions ofcompanies behind these networks are higher than the emissions from the networks itself. Figure 6
compares the carbon footprints of the six networks to each other and to a roundtrip MUC – SFO in
business class.

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Figure 6: Yearly carbon footprint of PoS networks compared to a roundtrip flight in business class

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7 Conclusion
In this report, we outline an approach for calculating the electricity consumption and carbon footprintof six Proof of Stake networks, namely Algorand, Avalanche, Cardano, Polkadot, Tezos, and Solana. We
selected hardware, made measurements of the protocols, and calculated the respective metrics. We
discussed our results and introduced several other key metrics, such as the Bitcoin and Ethereum
network for comparison.
Our best guess estimates the yearly electricity consumption of the Proof of Stake networks from 70
MWh for Polkadot to 1,967 MWh for Solana. This results in carbon footprints between 33 and 934
tonnes of CO2e annually, respectively. Compared to other electricity consumers such as an average US
household, these networks consumes up to 200 times more electricity, and produce up to 153 times
the amount of an intercontinental roundtrip flight.
Given the continuous development and evolution of Proof of Stake networks, our results can only betaken as a snapshot of the respective timeframe. Further measurements and analyses are required to
update and further enhance the validity of the metrics for electricity consumption and carbon footprintof Proof of Stake and other networks. Additionally, other networks employing different consensus
mechanisms as well as second layer networks need to be taken into account to gain a holistic pictureof 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, regardlessof their technical foundations or capabilities, harming the adoption of blockchain protocols in theindustry, public sector, and private investors. Based on the total emissions calculated for these six
networks, one may conclude that Proof of Stake-based blockchain protocols consume an amount ofelectricity that does not justify the discussions about their environmental footprints. Instead, an
extensive perspective, including corporate footprints and the ecosystem, must be taken. For
practitioners selecting a PoS blockchain protocol, other factors such as decentralization, network
throughput or functionality (e.g., Smart Contracts) should increase in relevance as decision criteria.

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8 References
Algorand Foundation. (2021). Algorand Network Architecture. Retrieved fromhttps://algorand.foundation/algorand-protocol/network
CBECI. (2021). Cambridge Bitcoin Electricity Consumption Index. Retrieved from https://cbeci.org
de Vries, A. (2021). Bitcoin boom: What rising prices mean for the network’s energy consumption.
Joule, 5(3), 509-513.
Gallersdörfer, U., Klaaßen, L., & Stoll, C. (2020). Energy consumption of cryptocurrencies beyond
bitcoin. Joule, 4(9), 1843-1846.
Gallersdörfer, U., Klaaßen, L., & Stoll, C. (2021). Accounting for carbon emissions caused by
cryptocurrency and token systems. arXiv preprint arXiv:2111.06477.
Heng, C. (2020). Guide to Using the Tezos-node Part I: Setting Up a Node. Retrieved fromhttps://www.tezos.org.sg/guide-to-setting-up-a-tezos-node/Hennion, N. (2021). Glances - An eye on your system. Retrieved fromhttps://github.com/nicolargo/glancesIntel. (2005). Intel® Pentium® Processor Extreme Edition 840 Retrieved fromhttps://ark.intel.com/content/www/us/en/ark/products/27613/intel-pentium-processorextreme-edition-840-2m-cache-3-20-ghz-800-mhz-fsb.html
Intel. (2021). Intel® Processor Names and Numbers. Retrieved fromhttps://www.intel.com/content/www/us/en/processors/processor-numbers.html
International Energy Agency. (2018). Global Energy & CO2 Status Report. Retrieved fromhttps://iea.blob.core.windows.net/assets/23f9eb39-7493-4722-aced61433cbffe10/Global_Energy_and_CO2_Status_Report_2018.pdf
IOHK. (2021). Cardano Mainnet - Stake Pool Minimum System Requirements Retrieved fromhttps://iohk.zendesk.com/hc/en-us/articles/900001951586-Stake-Pool-Minimum-SystemRequirements
King, S., & Nadal, S. (2012). Ppcoin: Peer-to-peer crypto-currency with proof-of-stake. self-publishedpaper, August, 19(1).
Minchev, R. (2021). Technical Requirements for Running a Validator Node on Avalanche. Retrieved
from https://support.avax.network/en/articles/4064879-technical-requirements-forrunning-a-validator-node-on-avalanche
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
Polkadot. (2021). Run a Validator. Retrieved from https://wiki.polkadot.network/docs/maintainguides-how-to-validate-polkadot
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. (2021). Validator Requirements. Retrieved from https://docs.solana.com/runningvalidator/validator-reqs
Statista. (2021). Bitcoin average energy consumption per transaction compared to that of VISA as ofAugust 16, 2021 Retrieved from https://www.statista.com/statistics/881541/bitcoin-energyconsumption-transaction-comparison-visa/
Stoll, C., Klaaßen, L., & Gallersdörfer, U. (2019). The carbon footprint of bitcoin. Joule, 3(7), 16471661.
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

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Appendix A: Hardware Selection
We use the Passmark CPU Benchmark Dataset. Our methodology to select three CPUs consists of thefollowing steps:
1. The data set contains many processor types that are not relevant to us. We filter out:
1.

CPUs with less than 50 benchmarking results, as we expect that they are not relevant for
the validator community.

2. CPUs that were released before 1/1/2015, as we consider less usage of outdated hardware
and a practical reason: We cannot buy these CPUs in the market.
3. CPUs with missing or incomplete data.
4. CPUs of AMD. Intel is the dominating manufacturer of CPUs with over 80 % market shareover the last years. 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 toconsider AMD processors.
5. CPUs intended for servers or notebooks. We think that the share of server hardware is low
and notebooks nonexistent. Some CPUs are marked as "Laptop only" in our dataset;however, we find them included in MiniPCs, e.g., the Intel NUC. To account for these CPUs,
we consulted geizhals.de as a source of CPU models sold within MiniPCs and did notremove them from the data set.
2. After obtaining a cleaned data set, we can separate the data set into three equally large
categories for later selection: High-level, mid-level, and low-level. While the hardware within
the networks might not be equally distributed among these three categories, this approachallows us to shift the allocation for single networks between the devices depending on theirhardware 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 demandcapabilities, as it describes the maximum amount of heat measured in Watts the CPU coolingsystem has to deal with.
4. This variable allows us to calculate the average energy efficiency for each category of CPUs (4high/3-mid/2-low) and select an average processor from the respective tier. This approachensures that we a) cover three different performance categories and b) select an averageenergy efficiency for their respective class.

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Appendix B: Electricity Measurements of Single Nodes
All electricity measurements are conducted in Watt.
Algorand
Table 14: Electricity Consumption while running Algorand measured in Watt [W]
1

2

3

4

5

Min [W]

4.4

6.9

10.8

27

87.8

Q1 [W]

5.3

35.4

23

55.5

150.5

Mean [W]

5.5

34.9

32.2

70

168.6

Median [W]

5.5

38

29.7

70.4

169.9

Q3 [W]

5.8

38.5

43.3

85.2

186.1

Max [W]

7.5

40.2

51.6

118.9

232.2

3

4

5

Avalanche
Table 15: Electricity Consumption while running Avalanche measured in Watt [W]1

2

Min [W]

N/A

3.6

5.0

24.4

80.1

Q1 [W]

N/A

14.8

14.5

38.6

128.1

Mean [W]

N/A

20.4

19.6

49.4

138.1

Median [W]

N/A

23.4

24.0

57.3

144.7

Q3 [W]

N/A

36.8

35.5

81.6

166.7

Max [W]

N/A

45.1

48.7

94.7

196.1

CardanoTable 16: Electricity Consumption while running Cardano measured in Watt [W]
1

2

3

4

5

Min [W]

N/A

2.5

2.7

24.3

75.9

Q1 [W]

N/A

2.9

2.9

26.4

80

Mean [W]

N/A

3.9

3.7

27.6

84.5

Median [W]

N/A

3

3

26.8

81.2

Q3 [W]

N/A

3.3

3.5

27.1

83.4

Max [W]

N/A

36.7

31.5

69.6

150.6

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Polkadot
Table 17: Electricity Consumption while running Polkadot measured in Watt [W]
1

2

3

4

5

Min [W]

N/A

2.36

3

23.4

77.9

Q1 [W]

N/A

3.5

4.4

26.1

91

Mean [W]

N/A

4.3

5.3

29.3

107.9

Median [W]

N/A

4

5.1

27

116.5

Q3 [W]

N/A

4.7

6

30.6

118.1

Max [W]

N/A

28.2

19.9

55.2

144

Tezos
Table 18: Electricity Consumption while running Tezos measured in Watt [W]
1

2

3

4

5

Min [W]

3.5

8.7

11.6

28

123

Q1 [W]

4.5

16.8

17.7

48.5

140.3

Mean [W]

4.9

19.3

19.7

52.4

141.7

Median [W]

4.8

19.2

19.9

55.4

141.8

Q3 [W]

5.2

21.8

22.2

57.2

143.3

Max [W]

6.8

33.2

29.4

69.4

158.9

SolanaTable 19: Electricity Consumption while running Solana measured in Watt [W]
1

2

3

4

5

Min [W]

N/A

N/A

N/A

N/A

164.3

Q1 [W]

N/A

N/A

N/A

N/A

204.1

Mean [W]

N/A

N/A

N/A

N/A

221.3

Median [W]

N/A

N/A

N/A

N/A

217.9

Q3 [W]

N/A

N/A

N/A

N/A

233.5

Max [W]

N/A

N/A

N/A

N/A

329.5

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

Information
2021-08-16 08:28:19 to 2021-08-17 08:28:10

Number of nodes

https://metrics.algorand.org/ ➔ “Decentralization”

Transaction Count

https://algoexplorer.io/top-statistics ➔ “Transaction Metrics” ➔ “Total Transactions”

Software version

go-algorand 2.8.0 (https://github.com/algorand/go-algorand)

Avalanche
Measurement period

Information[PC2]: 2021-10-19 21:54:52 to 2021-10-20 21:54:52[PC3,PC4]: 2021-10-20 23:24:13 to 2021-10-21 23:24:52[PC5]: 2021-10-22 00:16:11 to 2021-10-23 00:15:59

Number of nodes

https://explorer.avax.network/validators ➔ “Validators”

Transaction countP-Chain / X-Chain : https://explorer.avax.network/tx ➔ counted tx with API
P-Chain: https://snowtrace.io/chart/tx ➔ Weighted countSoftware version

avalanchego 1.6.2 (https://github.com/ava-labs/avalanchego)

CardanoInformation

Measurement period2021-08-07 00:01:02 to 2021-08-08 00:35:26

Number of nodes

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

Transaction counthttps://explorer.bitquery.io/cardano/transactions?from=2021-08-01&till=2021-08-31➔ “Transactions By Date”

Software version

cardano-node 1.27.0 (https://github.com/input-output-hk/cardano-node)

Polkadot
Measurement period

Information
2021-08-28 12:59:26 to 2021-08-29 12:58:06

Number of nodes

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

Transaction counthttps://polkadot.subscan.io/transfer ➔ “Transaction history”

Software version

polkadot 0.9.9 (https://github.com/paritytech/polkadot)

Tezos
Measurement period

Information[PC1,PC2,PC3,PC5]: 2021-08-22 17:17:44 to 2021-08-23 17:17:24[PC4]: 2021-08-24 22:52:57 to 2021-08-25 17:19:51 (missing hours accounted for)Number of nodes

https://tzstats.com/bakers ➔ “Bakers”

Transaction counthttps://api.tzstats.com/series/block.json for respective time frames, see
https://tzstats.com/docs/api#time-series-endpoints, used “n_tx”

Software version

tezos 10.0.0 (https://gitlab.com/tezos/tezos)

SolanaMeasurement period

Information
2021-09-09 16:58:18 to 2021-09-10 16:57:15

Number of nodes

https://solanabeach.io ➔ “Validators”

Transaction counthttps://explorer.solana.com/ ➔ “Live transaction stats” ➔ “TPS history” averaged over respective period.
Additionally, subtracted for “votes” operations by crawling 202 random blocks from the respective periodusing https://solanabeach.io/ and subtracted the average share of votes (78.2 %).

Software version

solana 1.6.21 (https://github.com/solana-labs/solana)

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About CCRI
The Crypto Carbon Ratings Institute (CCRI) provides carbon estimates for investments incryptocurrencies and technologies such as Blockchain and distributed ledger technologies (DLT). We
have built a multi-year research track record with a specific focus on Bitcoin and its environmental
impacts. We published comprehensive and formerly peer-reviewed studies on Bitcoin's carbon
footprint in the renowned scientific journals. Our research has been covered by major media outlets,
such as CNN and The New York Times, and has been appraised as very good estimate by majororganizations, such as the IEA.
Ulrich Gallersdörfer
Ulrich Gallersdörfer is a research associate in the Department of Informatics at the Technical Universityof Munich. His research focuses on identity management in blockchains. His interest extends to further
aspects of the technology, ranging from environmental implications to data analytics applications.
Lena Klaaßen
Lena Klaaßen has a background in Management and Technology with a particular focus on finance,
power engineering and energy markets. She has conducted research on carbon accounting in thecorporate and cryptocurrency space at TUM and MIT. She now focuses on research in the field of
climate finance at ETH Zurich.
Dr. Christian Stoll
Christian Stoll conducts research at the Center for Energy and Environmental Policy Research at theMassachusetts Institute of Technology and at the Center for Energy Markets of the Technical Universityof Munich. His research focuses on the implications of climate change from an economic point of view.

Crypto Carbon Ratings Institute 2022

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CCRI
Crypto Carbon Ratings Institute