Sep

REPORT

The Merge
Implications on theElectricity Consumption
and Carbon Footprintof the Ethereum Network© Crypto Carbon Ratings Institute, 2022
Crypto Carbon Ratings Institute (CCRI) is a brand of CCRI GmbH based in Dingolfing, Germany.

https://carbon-ratings.com

The Merge – Implications on the Environmental Sustainability of Ethereum

The Merge – Implications on the Electricity
Consumption and Carbon Footprint of the
Ethereum Network
September 2022
CCRI
carbon-ratings.com

PreambleThis commissioned report is prepared by CCRI for
ConsenSys Software Inc.

Executive summary•

Ethereum’s Merge is one of the largest and most complex upgrades to any cryptocurrency
network in history.

•

The Proof of Work consensus mechanism Ethash is replaced by a Proof of Stake mechanism.
Instead of miners, validators verify and propose new blocks after the Merge.

•This report establishes metrics for the electricity consumption and carbon footprint of both the
Ethereum Proof of Work and Proof of Stake network.

•

Given the complexity of the Ethereum Proof of Stake network, multiple consensus and execution
clients exist. CCRI’s measurements cover 95.54 % of all clients and provide an estimate for thenumber of nodes in the network.

•

The Merge reduces the electricity consumption and carbon footprint of the Ethereum network byover 99.988 % and 99.992 %, respectively.

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The Merge – Implications on the Environmental Sustainability of Ethereum

I.

Overview

1.

Introduction

The Merge, as one of the most important updates in the history of the Ethereum network is referred to, takes
place in September 2022. After years of planning, software design and engineering, the largest smart contract
platform switches its consensus engine from Proof of Work (PoW) to Proof of Stake (PoS). In the history ofblockchain networks, such a complex update is unprecedented.
There are several factors that increase the complexity of the Merge. First, the Merge should not interrupt theoperations of the network and is designed for a seamless switch between the consensus mechanisms.
Second, the network is designed for resilience against individual node failure. Therefore, several teams workin different implementations of the respective clients. In case a single type of node fails due to any reason,
the functionality of the network is not at stake, as the remainder of nodes are able to establish and keep
consensus. However, this increases the complexity of testing and verifying any correct functionality, given thatany combination of consensus and execution clients need to be properly tested. Before the core developers
decided on a Total Terminal Difficulty1, a plethora of merges of both testnets as well as shadow main nets took
place. The findings during these tests were incorporated into the respective client software to sort out any
potential problems due to the Merge.
Plans for switching from Proof of Work to Proof of Stake have existed since the first days of Ethereum in 2015.
While many reasons exist for a switch to PoS (scalability, lower barriers of entry, reduced issuance of new ether
and more), the sustainability of the PoW and PoS consensus mechanisms has significantly increased in
relevance in the public debate as well as for the networks itself. Especially Bitcoin’s electricity consumption
and carbon footprint has sparked a broader debate. Proof of Stake is expected to reduce the electricity
consumption and the carbon footprint significantly. Therefore, the Merge will substantially enhance theenvironmental sustainability of the Ethereum network.
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. The cost to secure
the network is decreasing, as electricity as a safeguard for the network is replaced by network participants’stake.
It remains the question: By how much will the Merge reduce the electricity consumption and carbon footprintof the Ethereum network due to the switch from Proof of Work to Proof of Stake? For both Proof of Work andProof of Stake networks, previous research and methodologies to estimate electricity consumption andcarbon footprint exist. CCRI, its team members and other institutions and researchers have put forward
multiple studies, assessing the sustainability of Proof of Work networks such as Bitcoin, Ethereum, Litecoin andothers (de Vries et al., 2022; Gallersdörfer et al., 2020; Krause & Tolaymat, 2018; Stoll et al., 2019), Proof of Stake
networks such as Algorand, Avalanche, Cardano, Polkadot, Tezos, Solana (CCRI, 2022b), TRON (CCRI, 2022d)
as well as Layer 2 networks involving PoW and PoS components such as Polygon (CCRI, 2022c; KlimaDAO,
2022).
In this report, we provide estimates on the electricity consumption and carbon footprint for Ethereum preMerge (PoW) and Ethereum post-Merge (PoS). For the PoW component, we rely on established literature andmethodologies. For the PoS component, we leverage a research framework initially proposed in CCRI (2022b)

1

The Total Terminal Difficulty (TTD) is the value at which, when the network reaches it, the Merge takes place. The TTD is set to58750000000000000000000.

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The Merge – Implications on the Environmental Sustainability of Ethereum

and adapt it to cater for Ethereum’s specific landscape of different client versions. We find that the Merge
leads to a reduction of the electricity consumption and carbon emissions of 99.988 % and 99.992 %,respectively. Table 1-3 provides an overview of the main results.

Electrical power[MW]

Annualized electricity consumption[MWh/year]

Annualized carbon emissions[tCO2e/year]

2,565

22,900,320

11,016,000

Table 1: Overview of results for Ethereum PoW network, annualized values as of August 2022 (pre-Merge)

Beacon NodeCount[# total]

4,755

Total electricity

Electricity per

Total electricity

Total carbon[Tx/year]2

consumption[MWh/year]

node[kWh/year]

per transaction[Wh/Tx]emissions[tCO2e/year]

413,209,565

2,600.86

547.01

6.2943869.78Transactions

Table 2: Overview of results for Ethereum PoS network, annualized values as ofour measurements in August and September 2022 (post-Merge)

Ethereum PoW
Electricity consumption [MWh/year]
CO2e emissions [t/year]

Ethereum PoS

Reduction factor

22,900,320

2,600.86

0.99988

11,016,000

869.780.99992

Table 3: Overview of reduction factors as a result of the Merge

2

We count the transactions occurring during our measurements and thus estimate the number of transactions for a year.

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The Merge – Implications on the Environmental Sustainability of Ethereum2.

Aim and scopeThis report aims to provide insights into the electricity consumption and carbon footprint of both the Ethereum
network pre-Merge (PoW) and post-Merge (PoS).
The Ethereum network investigated in our analysis takes the 2nd position with regard to market capitalizationof coinmarketcap.com on 1st September 2022.3 We summarize important key figures for the Ethereum (ETH)
cryptocurrency as per 1st September 2022 in the following:

Name:

Ethereum

Symbol:ETHMarket Capitalization (Rank):

193,834,454,492 USD (2nd)

ETH Price:

1,586.18 USD

Circulating Supply:

120,344,720 ETH

24 Hours Trading Volume:387,409,445 USD

The following of this report is divided into three components. Firstly, we analyze the electricity consumption
and carbon footprint of the Ethereum network pre-Merge. Secondly, we examine the electricity consumption
and the emissions generated by the Ethereum network post-Merge. Thirdly, we compare these results anddiscuss our observations. The specific methodologies are defined and explained in the corresponding
sections.
It is noteworthy that the approach applied in this report is a helpful tool to derive a ballpark estimate for total
electricity consumption and carbon emissions. However, any cryptocurrency network is associated withuncertainties that impede deriving exact numbers of the electricity consumption or, respectively, of a
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 toproduce a precise estimate for the electricity consumption and carbon footprint of the Ethereum pre- andpost-Merge network, as we a) build on an established method for the PoW network, b) observe and measure
the electricity consumption of single hardware components and use them as a proxy for the overall PoS
network as well as c) use the network-specific carbon intensity of the Ethereum network to estimate theoverall carbon footprint.
The establishment of methodology, representative hardware, network sizes, and electricity measurements
form the basis for future research, such as comparing different networks and their respective requirements
and properties.

3

The data is taken from https://coinmarketcap.com/currencies/ethereum/historical-data/

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The Merge – Implications on the Environmental Sustainability of Ethereum

II.

Ethereum’s Proof of Work network

1.

Overview

Since its inception, the Ethereum network has been based on the Proof of Work consensus mechanism andtherefore requires computational power to secure the integrity of its blockchain. The electricity consumption
and carbon footprint of the PoW component of the Ethereum network has been subject to extensive research(de Vries, 2022b; Gallersdörfer et al., 2020; McDonald, 2021). In this report, we deploy a two-step approach toderive the sustainability metrics of the network.

Two-Step Carbon Footprint Analysis of Ethereum Pre-Merge
1.

Electricity consumption of the Ethereum network: In an initial step, we derive the overall electricity
consumption of the Ethereum network by determining the hardware composition and device efficiency
required for producing the hash rate the Ethereum network is running on.

2.

Carbon footprint of the Ethereum network: As a second step, we translate the electricity consumption
calculated in step 1 into a carbon footprint. For this, the locations of miners need to be determined andthe carbon intensity of the respective electricity sources are utilized to calculate an overall carbon
intensity of the network. With this value, one can determine the overall carbon footprint of the Ethereum
network.

2.

Electricity consumption of the Ethereum network

Ethereum is the second largest Proof of Work network as well as the largest platform that supports smart
contracts in terms of market capitalization. 4 As such, it has received considerable attention on its
sustainability performance, albeit Bitcoin’s carbon footprint as the largest cryptocurrency in terms of market
cap has been still in the center of the discussion.
Proof of Work protects the integrity of the network by relying on computationally intensive mining puzzles that
require miners to run hardware devices that produce solutions for these puzzles. Once a puzzle is solved, aminer is allowed to propose a new block5 to the network and collect a mining reward containing a blocksubsidy as well as all transaction fees6 from the included transactions. PoW works as a sybil control
mechanism in that regard, that an adversary is not able to control the next forthcoming block or is able torewrite past blocks unless it controls more than 50 % of the overall computational power. Given the involved
hardware requirements and electricity costs for such attacks, they are very expensive and thus very unlikely
to occur.
This security mechanism comes with the cost of a comparatively high electricity consumption for any Proofof Work network. One key driver of the electricity consumption is the respective reward for the miners; if theyreceive a higher payment for their operations, they can allocate larger amounts of money for electricity costs.

4
5
6

Data available on https://coinmarketcap.com.
A new block refers to a new state based on the transactions that are included in the respective block.
Since 5th August 2021 (block 12,965,000), EIP-1559 forces the miner to burn a certain share of transaction fees in the Ethereum network.

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The Merge – Implications on the Environmental Sustainability of Ethereum

On the 6th of September 2022, Bitcoin miners received around 19.1 million USD in the last 24 hours7 as mining
reward whereas Ethereum miners received about 22.2 million USD for the same period8.
There are two key mechanisms to determine the electricity consumption of a Proof of Work network, namely
a top-down calculation and a bottom-up calculation.
The top-down approach starts by assessing the income of miners both from block reward and transaction
fees. Second, it estimates the share of income that is spent on electricity. Given the incentive structure andmarket conditions of cryptocurrency mining, the share for electricity costs can be a substantial amount of theoverall cost structure. With the assumption of an average electricity price miners pay, one can determine theoverall network electricity consumption.
The bottom-up approach starts by looking at the metric of the hash rate of the respective network. The hashrate is the required computational power to produce the number of blocks in the respective time frame withthe given difficulty for that period of time9. In a second step, the amounts and types of hardware aredetermined that can operate profitably under current computational requirements in the network. Given thevariety of different hardware devices and efficiencies, the selection of hardware influences the overall result
significantly. The overall electricity consumption of the network is then determined by summing up theelectricity consumption of all the devices considered in the previous step.
The top-down approach was initially presented and widely promoted by Alex de Vries. His website
digiconomist.net provides both a Bitcoin Energy Consumption Index and an Ethereum Energy Consumption

Index. He assumes that a certain share of the miners’ revenue is spent on electricity consumption whileassuming a price of 0.1 USD / kWh for the Ethereum network. This share is regularly adjusted depending onmarket conditions and lies at almost 100 % as of the beginning of September 2022. With these assumptions,his estimate of the Ethereum network derives an overall annual electricity consumption of ~78 TWh as of the
beginning of September 2022 (de Vries, 2022a, 2022b).
The bottom-up approach was initially described in scientific literature by Marc Bevand in 201710 and sincethen has been adopted by several researchers (Krause & Tolaymat, 2018). In 2020, (Gallersdörfer et al., 2020)
have utilized this methodology to determine the electricity consumption of, amongst other currencies,
Ethereum. Based on this methodology, CCRI developed up-to-date calculations for the electricity
consumption and carbon footprint of Ethereum and other major PoW protocols. The estimates feed into the
CCRI Cryptocurrency Sustainability API (CCRI, 2022a). In 2021, Kyle McDonald also utilized the bottom-up
approach to determine the electricity consumption and carbon footprint of the Ethereum network (McDonald,2021). The results have aligned with previously conducted studies.
Figure 1 displays an overview of both top-down method by de Vries (2022b) as well as both bottom-up
estimates by Gallersdörfer et al. (2020)11 and McDonald (2021)12.

7
8

https://bitinfocharts.com/de/bitcoin/, accessed on the 6th September 2022
https://bitinfocharts.com/de/ethereum/, accessed on the 6th September 2022

The hash rate is a stochastic metric, meaning that a lower or higher hash rate can exist in reality. Depending on the time frame that is
considered, the hash rate can vary significantly, but if sufficiently long periods of time are selected, the hash rate becomes a reliable metric.
9

10
11
12

http://blog.zorinaq.com/bitcoin-electricity-consumption, accessed on the 7th September 2022
CCRI has leveraged the initial research methodology and updated all values for 2022.
Current values have been obtained from https://kylemcdonald.github.io/ethereum-emissions/

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The Merge – Implications on the Environmental Sustainability of Ethereum

Figure 1: Estimates of power usage of the Ethereum network by Gallersdörfer et. al, Kyle McDonald andDigiconomist. Data from Digiconomist is transformed to GW to align.
While the estimates from the bottom-up approaches are closely aligned, the top-down approach fromdigicomist.net is around fourfold in terms of electricity consumption as of mid-August 2020. As such, we
believe the bottom-up approach to derive more realistic estimates as it is directly based on actual metricsof the network (i.e., hash rate) and the market (i.e., hardware profitability). Therefore, CCRI uses it for its own
calculations which also serve as the underlying foundation in this report.

3.

The carbon footprint of the Ethereum network

In comparison to the use-phase of the hardware, the production and disposal of cryptocurrency mining
devices play a subordinate role of carbon emissions in PoW networks (De Vries & Stoll, 2021; Köhler & Pizzol,2019). Especially for ASIC-resistant PoW algorithms13, general purpose hardware can be repurposed afterward
and is available for secondary markets. Therefore, the carbon footprint of a PoW network largely depends on
the utilized electricity sources during the mining process and their respective carbon intensities.
To properly identify the carbon intensity of the respective cryptocurrency network, one needs to determine
the locations and, ideally, the electricity sources of miners. This is an inherently difficult endeavor due to thenature of mining:

One of the key variables that miners can influence is the price paid for the electricity for their operations;selecting locations with a high availability of electricity as well as cheap rates can make or break abusiness and are therefore a well-kept secret. Miners have no interest in sharing their location or theirelectricity prices, as this only would attract competition.

An ASIC-resistant PoW algorithm is an algorithm that, in theory, prevents the development of specialized hardware aimed at solving therespective puzzle in a more efficient way than general purpose devices such as CPUs or GPUs. For Bitcoins double-SHA 256 algorithm, the13market is dominated by Application specific integrated circuits (ASICs), whereas Ethereum’s Ethash algorithm is mainly dominated by GPUs.

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The Merge – Implications on the Environmental Sustainability of Ethereum

Miners might even be required to keep their operations a secret, if they are operating in locations thathave banned the usage of mining devices, such as China. In such cases, miners use technologies such as
VPN and other approaches to disguise their business and remain under the radar.
Miners connect to larger mining pools in order to enhance the predictability of their income stream14.
Mining pools provide a raw block including details such as transactions and payouts and the individual
miners try to solve the respective mining puzzle for the block. Given the network structure, miners cannot
be observed directly when they create a valid block (e.g., by IP-addresses), making it harder to determine
the location of the mining devices.
Even if a location is known, it is unclear which electricity is exactly used. Miners could operate behind themeter, utilize electricity that is otherwise stranded or their electricity usage is leading to displacement
effects. Therefore, if locations are available, average grid intensities are leveraged to balance between
different potential situations.

For Bitcoin as the largest PoW network, various estimates on the locations and carbon intensity exist (CBECI,
2022; de Vries et al., 2022; Stoll et al., 2019). In these cases, mining pools have provided data on the location of
the connected miners or other information such as IoT search engines have been leveraged for location
determination. Nonetheless, these data points face the same issues as previously mentioned, allowing only
for a rough estimate of the carbon intensity of the network.
To the best of our knowledge, only one estimate on the carbon intensity of the Ethereum network exists. In his
paper, Kyle McDonald also analyzed the miner location of Ethereum miners to determine an overall carbon
intensity (and thus carbon footprint) of the network (McDonald, 2021). He thereby uses data partly relying on
self-reported location by miners15 as well as further information about mining pools, blog posts, and othersources such as Reddit. In his article, he comes up with a carbon intensity of 320 gCO 2/kWh. In comparison,
the world average carbon intensity lies at 459 gCO2/kWh (International Energy Agency, 2021), which issignificantly higher than McDonald’s estimate. Furthermore, estimates for the Bitcoin network are significantly
higher ranging from 480 gCO2/kWh to 560 gCO2/kWh (de Vries et al., 2022; Stoll et al., 2019).
We see two challenges with McDonald’s carbon intensity data:

Self-reported data: It is unclear how reliable self-reported location data is. Given that the blocks might
contain the location of the respective mining pool, it is entirely unclear if miners select their mining poolbased on proximity and whether this approach leads to a fair approximation of miner locations.
PoW incentives: The consensus mechanisms of Bitcoin and Ethereum differ in their selected hash function,
resulting in different utilized hardware16. Nonetheless, the underlying incentive structure for the cheapest
electricity should lead to similar locations and thus carbon intensities for both networks. Given also thatboth types of mining businesses are rather investment-intensive, it seems incongruent that McDonald’s
estimate resides significantly below the world average and most of Bitcoin’s estimates reside significantly
above the world average.

14

Miners with a low hash rate cannot reliably expect to mine a block in a given timeframe; instead of finding one block and receiving a large

payout, they connect to mining pools that distribute earnings depending on the respective hash rate of the miner.
15
Miners are able to store information in an extra field of the block they mined. Some blocks contain the information of the location of themining pool; assuming that a miner selects the nearest mining pool, regions and a geographical distribution can be determined.
16
Bitcoin relies on a Double-SHA256 hash function, allowing the broad deployment of ASIC devices. Ethereum relies on a more complex hashfunction that severely restricts the usage of ASIC devices, therefore it is believed that most of the hash rate of Ethereum is provided by GPUs.

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The Merge – Implications on the Environmental Sustainability of Ethereum

We use a conservative approach here to avoid underestimating emissions. Therefore, we select the carbon
intensities of the Bitcoin network and apply them to Ethereum’s electricity consumption. We utilize an updated
carbon intensity based on the methodology presented in (de Vries et al., 2022) and additional data available
from the Cambridge Mining map17, leading to an average carbon intensity for the relevant period of
501 gCO2e/kWh, leading to the overall emissions of 12.721 MtCO2e for the period of 1st August 2021 to 31st July
2022. Figure 2 gives an overview of the annualized emissions of the Ethereum network pre-Merge.

Figure 2: Estimates of annualized carbon emissions in MtCO2e of the Ethereum network based on(de Vries et al., 2022; Gallersdörfer et al., 2020)

4.

Conclusion

Table 4 displays the key results of the analysis of the electricity consumption and carbon footprint of the
Ethereum pre-Merge network. We opt for a one-month period from 1st August to 31st August 2022 and annualize
the values for later comparison in chapter IV: The annualized average power for the given time period is 2,565
MW, the annualized consumed electricity amounts to 22,900,320 MWh, whereas the respective annualized
carbon footprint amounts to 11,016,000 tCO2e.

Ethereum
pre-Merge

Electrical power[MW]Electricity consumption[MWh/year]

Carbon emissions[tCO2e/year]

2,565

22,900,320

11,016,000

Table 4: Overview of the annualized electrical power, electricity consumption and carbon emissions for the
Ethereum pre-Merge network for the time period from 1st August to 31st August 2022.

17

Available at https://ccaf.io/cbeci/mining_map

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The Merge – Implications on the Environmental Sustainability of Ethereum

III.

Ethereum’s Proof of Stake network

The upcoming Merge event will change Ethereum’s consensus engine to a Proof of Stake mechanism. This
event is claimed to be the most significant upgrade in the history of Ethereum, aiming for less electricity
consumption and to prepare the blockchain for future scaling upgrades.
The PoS based Beacon chain was launched on 1st December 2020 and has been running in parallel toEthereum’s PoW based mainnet since then. After the Merge, the Beacon chain will be used as the consensus
engine for the Ethereum mainnet, and thus be the engine for block production. Instead of mining, PoS
validators will verify transactions and propose new blocks.
Since the Beacon chain is already in operation without actually processing transactions, the power
consumption of participating consensus and execution nodes can already be determined. This allows an
estimate for the electricity consumption of the Ethereum network after the Merge took place.

1.

Client diversity of Ethereum

Generally, after the Merge, an Ethereum client consists of two different layers: On the one hand, the so-called
consensus layer, which is responsible for participating in the consensus mechanism. On the other hand, theso-called execution layer, which is in charge of executing and bundling transactions as well as handling thestate management. For both layer types, several different and independently developed client software exist,
each built in different programming languages and offering different advantages.
At the time of writing (05th of September 2022), Prysm is the most widely used consensus client (44.15 %), andGeth is the dominant execution client (80.74 %). The data describing the client diversity stem from theBlockprint Public API18 concerning the consensus clients and from Ethernodes19 for the execution clients.
Table 5 gives an overview of the distribution among the most important clients in the network as of September
2022.
Consensus Clients

Execution ClientsPrysm

44.15 %

Geth

80.74 %

Lighthouse

33.99 %

Erigon8.55 %

Teku

17.49 %

Besu

6.24 %

Nimbus4.33 %

Nethermind

4.01 %

Lodestar

0.05 %

Others0.45 %

Others0.00 %

Table 5: Client diversity within the Ethereum network. Data
from Blockprint Public API (consensus clients) and Ethernodes (execution clients).

18

https://github.com/sigp/blockprint

19

https://ethernodes.org/

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The Merge – Implications on the Environmental Sustainability of Ethereum

2.

Methodology for analyzing Ethereum PoS network

Our methodology builds upon four steps to generate data on the electricity consumption and carbon footprintof the Ethereum PoS network.
In the first step, we analyze the different client solutions 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 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 participating in the network that runs a
specific combination of one consensus and one execution client. For this, we first determine the electricity
usage of the hardware devices while idling. Secondly, we measure the execution of different consensus andexecution clients on their own on the hardware devices selected. We provide upper and lower bounds as well
as a best guess metric for each client software considered. Thirdly, we subtract the idle electricity usage fromthe results obtained for each client, allowing us to calculate the power consumption for arbitrary
combinations of consensus with execution clients. Taking the idle power consumption into account, thesevalues allow us to produce reasonable upper and lower bounds and a best guess for running a full node
applying different client software combinations, as our hardware is selected accordingly. We also measureother 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 complete network. Firstly, we collectinformation about the size of the network, as the node count significantly influences the amount of electricity
consumed. Thereby, we consider the client diversity within the Ethereum network as presented in Table 5 since
the various combinations differ in terms of electricity usage. We thus weight the electricity consumption ofclient combinations according to their frequency of occurrence in the network. Secondly, we develop aweighting between the single hardware devices. Lastly, we multiply the electricity consumption, adjusted forthe client diversity, of the weighted nodes by the number of accounts in the network.
In the fourth step, we estimate the CO2 emissions arising from the operation of the Ethereum PoS network. For
this, we use our weighted data on electricity consumption calculated and multiply it with a carbon intensity
factor adjusted to the regional distribution of the nodes in the network. We provide a best guess as well as an
upper and a lower bound for the carbon footprint of the Ethereum PoS network.

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The Merge – Implications on the Environmental Sustainability of Ethereum3.

Ethereum PoS hardware requirements and test environment

In this section, we first establish our selected hardware pool for carrying out analyses of PoS networks.
Secondly, we summarize the hardware requirements for the different Ethereum clients and derive a hardware
selection out of our pool that satisfies the requirements for all clients considered. Thirdly, we provide details of
the infrastructure applied to measure electricity consumption and further describe our test environment.
Hardware selection
For analyses of PoS systems, we generally define three different categories of hardware requirements fornodes participating in a network:1.

Low hardware requirements: For PoS networks with rather low hardware requirements, we assume thatcomputational power is not a concern for the systems, and users should be comfortable running thesoftware on any system they have available. Typically, such networks recommend using low-energy
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.

2.

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.

3.

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), and a Samsung 970 Evo Plus 2TB in order toaddress high hardware requirements. As the processor does not have an onboard graphics processor, weneed a graphics card. However, as graphics cards are not always required at that time, we opt for a card that
does not support CUDA and cannot participate in the calculations of any network. 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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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 (Intel, 2021), we decided to opt
for the non-F variant, as otherwise, a dedicated GPU would add unnecessary electricity consumption to thesystem. 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, 2022b), 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.
To ensure comparability with our previous analyses of other PoS systems, we largely stick to the same
hardware selection as defined in CCRI (2022b). However, some minor adjustments to better fit the assumed
hardware diversity in the Ethereum network have been conducted. Besides including a further mid-tier setup
into the hardware pool with configuration 4, configuration 5 was enhanced regarding both RAM and storage
capacity ensuring a significant difference to the new configuration 4 not only in terms of the CPU.
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. Table 6 displays an
overview of the hardware configurations just introduced. Other factors than CPU are also relevant for the

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The Merge – Implications on the Environmental Sustainability of Ethereum

electricity consumption of the systems. Nonetheless, this set of hardware yields a broad overview of usedhardware 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

AM 6970

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

Table 6: Overview of selected hardware configurations from lowest to highest requirement
It is very likely that a considerable share of nodes in the network are hosted on cloud providers such as
Amazon Web Services (AWS) or similar. Establishing precise data metrics for the hardware in use in such data
centers, its energy efficiency and electrical consumption goes beyond the scope of this report. Especially
hyperscale data centers tend to exhibit a comparatively high degree of efficiency. Therefore, the approachbased on self-hosted hardware as presented in this report may overestimate the electricity consumption of
Ethereum’s PoS networks and hence represents a rather conservative estimate.

Hardware requirements of Ethereum clients
Different hardware requirements are recommended for the various Ethereum clients. There are considerable
differences between consensus and execution clients, also within the same client type the requirements canvary. Consensus clients tend to have higher hardware requirements than consensus clients, which is why the
hardware required to run a full node is often determined by the execution client. Table 7 summarizes therecommended minimum hardware requirements for consensus clients we have considered in our analyses.
Likewise, Table 8 lists the recommendations for the regarded execution clients.

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The Merge – Implications on the Environmental Sustainability of EthereumConsensus Client

CPU

RAM

Storage

Prysm20

4 cores, 2.8 GHz

16 GB

2 TB SSD

Lighthouse21

2 cores, 2015 or newer

8 GB

128 GB SSD

Teku22

2 cores, Intel Core i5–760 / AMD FX-8100 or better

4 GB

20 GB SSD

Nimbus23

N/A

4 GB

200 GB

Lodestar24

Intel Core i5–760 / AMD FX-8100 or better

4 GB

20 GB SSD

Table 7: Hardware requirements for measured Ethereum consensus clients
Execution Client

CPU

RAM

StorageGeth252 cores

4 GB

320 GB

Erigon26

N/A

16 GB

400 GB SSD

Besu27N/A

8 GB

750 GB SSD

Table 8: Hardware requirements for measured Ethereum execution clients
Applying the client’s requirements for executing full nodes to our hardware pool presented in the previous
section, we deduce that configurations 5 and 6 shown in Table 6 exceed the hardware recommendations for
all clients listed. Consequently, these configurations are chosen to be included in our analysis. Moreover, since
we avoid treating hardware recommendations as a strict lower bound, we also involve configuration 4 into
our experiment, as it closely fulfills the minimum recommendations for the most demanding client (Prysm).
As a result, we also examine a node representative of the mid-tier category. Table 9 summaries which
configurations of our hardware pool were included as a foundation to derive the electricity consumption of
the Ethereum PoS network.
Ethereum
Configuration 1✗

Configuration 2✗

Configuration 3✗

Configuration 4✓

Configuration 5✓

Configuration 6✓

Table 9: Overview of nodes of our hardware pool selected for running a Ethereum full node20

Source: https://docs.prylabs.network/docs/install/install-with-script

Source: https://lighthouse-book.sigmaprime.io/system-requirements.html
22
Source: https://www.coincashew.com/v/spanish/coins/overview-eth/guide-how-to-stake-on-eth2-with-teku-on-ubuntu
21

23
24
25
26
27

Source: https://nimbus.guide/hardware.html
Source: https://chainsafe.github.io/lodestar/
Source: https://docs.ethhub.io/using-ethereum/ethereum-clients/geth/
Source: https://github.com/ledgerwatch/erigon
Source: https://besu.hyperledger.org/en/stable/public-networks/get-started/system-requirements/

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The Merge – Implications on the Environmental Sustainability of Ethereum

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 fresh Ubuntu server 20.04/21 installation, and themonitoring tool Glances that allows us to collect additional system information such as temperature orsystem load during the experiment (Hennion, 2021).
A separate Raspberry Pi, equipped with a Python script, collected, and monitored the systems duringexecuting the Ethereum full nodes and analyzed the data generated during the runs. All computers are only
connected to the power outlet and LAN. All systems share an internet connection with 350 Mbit/s download
and 110 MBit/s upload.

4.

Electricity consumption and carbon footprint of the Ethereum PoS network

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 node runninga specific client, and a best guess as a weighted average between the selected computer devices. On thatbasis, we establish the electricity consumption of the overall Ethereum PoS network assuming each node
executes both a consensus and an execution client and taking the client diversity as introduced in section 1of chapter III. We furthermore discuss additional metrics such as the electricity use per transaction.
Single node measurements
After defining and obtaining the hardware required for our analysis, we set up the hardware and install thenode software for the Ethereum PoS network. For that, we use the following process:1.

Hardware Setup: We install the node with the respective Linux version, configure Glances and configure
remote access.

2.

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

3.

Node Setup: We download and install the software necessary for executing a specific Ethereum clientand verify the correct installation.

4.

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

5.

Electricity Measurement: We shut down the nodes, start the electricity measurement and then start
the nodes again. The nodes run for 48 hours executing the respective Ethereum client, as this covers
two entire day cycles. Appendix B contains an overview of every electricity measurement for each node
executing each client.

To understand what exactly we are measuring, we need to describe the Ethereum PoS network and its setup.
It consists of nodes running either just a single client software or both a consensus and an execution client.

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The Merge – Implications on the Environmental Sustainability of Ethereum

However, to execute a consensus client exclusively, a connection to one or more remote execution clients isrequired. While consensus clients are responsible for state synchronization, execution clients manipulate thestate and handle smart contracts. The combination of both clients is what we refer to as a full node in thisreport. Furthermore, a limited number of validators exist, which receive blocks and re-execute included
transactions to verify block validity before submitting a vote on the verified block to the network. To operate avalidator node, 32 ETH need to be deposited and, along with operating a full node, a validator software must
be executed. In an ideal setup, we measure the electricity consumption of consensus clients that run
validators, but given the costs we decide not to. Furthermore, previous research suggests that participatingin the PoS consensus mechanism has only a negligible effect on the device's electricity consumption(Sedlmeir et al., 2020). Therefore, we run our electricity measurements on nodes executing either a consensus
or an execution client, thereby determining the electricity consumption of various full node configurations
consisting of different consensus and execution client pairs.
Idle electrical power
We measure the electricity consumption of the devices idle. Table 10 depicts the minimum, maximum,
median, and the first and third quartile of the electricity consumption for 24 hours. All values are rounded to
one decimal. Interestingly, the setup 2 and 3 consumes 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 10: Electrical power in Idle measured in Watt [W] – hardware selection foreach of the six clusters can be found in Table 2
Node electrical power
Due to the hardware requirements outlined in section 3, we do not run the Ethereum clients on all nodes. Whilethe hardware setups 5 and 6 of Table 6 exceed the recommended configurations even for the mostdemanding client software, we also test configuration 4 as we do not want to enforce the recommended
hardware requirements as a strict lower bound. However, we exclude hardware configurations 1-3 from ourmeasurements since these do not satisfy the requirements of all clients.
In Table 11 and 12, we outline the mean and the median electrical power of the nodes during the measurementsof the single Ethereum clients. We also provide the electrical power consumption less the devices’ idle
consumption during the executions in order to obtain the marginal power consumption per client software.
These values are necessary for our calculation of estimations for the execution of different consensus andexecution client combinations later in this chapter.

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The Merge – Implications on the Environmental Sustainability of Ethereum

Execution Client

Geth

Erigon

Besu

4

5

6

Total

Marginal

Total

Marginal

Total

Marginal

Mean

14.89

11.23

34.74

9.70

125.87

47.70

Median

14.86

11.20

34.58

9.71

125.93

47.89

Mean

22.26

18.60

42.63

17.59

122.7944.62

Median

22.20

18.54

42.44

17.57

121.20

43.16

Mean

33.91

30.25

56.06

31.02

153.21

75.04

Median

33.73

30.07

55.42

30.55

153.1975.15

Table 11: Electrical power of nodes executing different execution clients in Watt [W].
The most consuming clients per node are highlighted.

Consensus Client

Prysm

Lighthouse

Teku

NimbusLodestar

4

5

6

Total

Marginal

Total

Marginal

Total

Marginal

Mean

7.17

3.51

27.91

2.87

102.5024.33

Median

7.13

3.47

27.86

2.99

102.36

24.32

Mean

6.41

2.75

28.18

3.14

97.01

18.84

Median

6.26

2.60

28.03

3.16

96.74

18.70

Mean

7.37

3.71

28.36

3.32

105.63

27.46

Median

7.44

3.78

28.02

3.15

105.31

27.27

Mean

5.33

1.67

27.12

2.08

95.28

17.11

Median

5.17

1.51

26.89

2.02

94.99

16.95

Mean

6.80

3.14

28.93

3.89

111.72

33.55

Median

6.68

3.02

28.80

3.93

111.86

33.82

Table 12: Electrical power of nodes executing different consensus clients in Watt [W].
The most consuming clients per node are highlighted.
In summary, the measurements show that Besu requires the most electrical power within the execution clients.
Among the consensus clients, Teku was the most expensive on hardware configuration 4, and Lodestar on
configurations 5 and 6. However, we note that the measurements took place sequentially and, therefore, someminor differences in the transaction numbers during the single measurements occur.

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The Merge – Implications on the Environmental Sustainability of Ethereum5.

Calculation of best guess and bounds for electricity consumption

To calculate the electricity consumption of the overall network, we need to understand the average
consumption for a single node participating with a specific combination of a consensus and an execution
client. We measured the electrical power for three different hardware configurations several times runningvarious consensus and execution clients. Thus, with these measurements, we can compute a best guess that
captures the consumption of a node for arbitrary configurations of one consensus and one execution client
best. Since we know about the distribution of clients among the network as explained in section 1 of chapter
III, we can finally weight the best guesses for the single client configuration possibilities and come up with an
overall best guess for the electrical consumption of the average node in the network.
Furthermore, we can provide upper bounds, meaning the highest electricity that a node consumes, and lower
bounds, meaning the least electricity a node consumes.
Best guess
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 type is displayed in Table 13.

Ethereum
1

N/A

2

N/A

3

N/A

4

25.00 %

550.00 %

6

25.00 %

Table 13: Overview of node distribution for the six networks

With this distribution, we calculate the weighted electricity consumption of an average node for eachcombination of one consensus and one execution client. For this, we sum up the weighted marginal electricity
consumption of the respective consensus and execution client, and add the weighted idle consumptions:∑(𝑎𝑣𝑔𝑀𝑎𝑟𝑔𝑖𝑛𝑎𝑙𝐸𝑛𝑒𝑟𝑔𝑦𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝑖,𝑐𝑐 + 𝑎𝑣𝑔𝑀𝑎𝑟𝑔𝑖𝑛𝑎𝑙𝐸𝑛𝑒𝑟𝑔𝑦𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝑖,𝑒𝑐 + 𝑎𝑣𝑔𝐼𝑑𝑙𝑒𝐸𝑛𝑒𝑟𝑔𝑦𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝑖 ) ∗ 𝑠ℎ𝑎𝑟𝑒𝑖

𝑖 ∈ ℎ𝑎𝑟𝑑𝑤𝑎𝑟𝑒∀ 𝑐𝑐 ∈ [𝑃𝑟𝑦𝑠𝑚, 𝐿𝑖𝑔ℎ𝑡ℎ𝑜𝑢𝑠𝑒, 𝑇𝑒𝑘𝑢, 𝑁𝑖𝑚𝑏𝑢𝑠, 𝐿𝑜𝑑𝑒𝑠𝑡𝑎𝑟], 𝑒𝑐 ∈ [𝐺𝑒𝑡ℎ, 𝐸𝑟𝑖𝑔𝑜𝑛, 𝐵𝑒𝑠𝑢]

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The Merge – Implications on the Environmental Sustainability of Ethereum

Table 14 lists the per node best guess electricity consumption weighted according to Table 13 for eachcombination of consensus and execution client that we can compute with our measurements conducted.
Additionally, the share of each client configuration within the network is indicated, which was computed
based on Table 5.
Client configuration

Best guess

Best guess

Configuration[W][kWh / year]

share [%]

Consensus Client

Execution Client

Prysm

Geth

60.96

533.97

35.65

Prysm

Erigon65.97

577.91

3.77

Prysm

Besu

83.21

728.882.75

Lighthouse

Geth

59.53

521.47

27.44

Lighthouse

Erigon64.54

565.41

2.91

Lighthouse

Besu

81.78

716.38

2.12

Teku

Geth

62.01

543.24

14.12

Teku

Erigon67.03

587.18

1.50

Teku

Besu

84.26

738.15

1.09

Nimbus

Geth

58.29

510.66

3.50

NimbusErigon63.31

554.590.37

Nimbus

Besu

80.54

705.570.27

Lodestar

Geth

63.68

557.800.04

Lodestar

Erigon68.69

601.740.00

Lodestar

Besu

85.93

752.71

0.00
Sum: 95.54

Table 14: Best guess estimates per single node for different client configurations.
The most electricity consuming configuration is highlighted.
The values show that the combination of Lodestar as the consensus and Besu as the execution client
consumes most electricity for a best guess node in the network (85.93 W). However, this combination
represents a negligible share in the network. The two most popular client combinations are Prysm and Geth
(36.65 %) followed by Lighthouse and Geth (27.44 %). Both are rather efficient in terms of their power
consumption (60.96 W and 59.53 W).
Since we know the shares of the individual client combinations, we can weight the power consumption of thesingle best guess nodes per client configuration accordingly, and thus obtain the electricity consumption of
an average best guess node in the network:∑𝑐𝑐,𝑒𝑐 ∈ 𝑐𝑙𝑖𝑒𝑛𝑡𝐶𝑜𝑛𝑓𝑖𝑔𝑢𝑟𝑎𝑡𝑖𝑜𝑛𝑠(𝑏𝑒𝑠𝑡𝐺𝑢𝑒𝑠𝑠 𝑐𝑐,𝑒𝑐 ∗ 𝑐𝑜𝑛𝑓𝑖𝑔𝑢𝑟𝑎𝑡𝑖𝑜𝑛𝑆ℎ𝑎𝑟𝑒 𝑐𝑐,𝑒𝑐 )∑𝑐𝑐,𝑒𝑐 ∈ 𝑐𝑙𝑖𝑒𝑛𝑡𝐶𝑜𝑛𝑓𝑖𝑔𝑢𝑟𝑎𝑡𝑖𝑜𝑛𝑠 𝑐𝑜𝑛𝑓𝑖𝑔𝑢𝑟𝑎𝑡𝑖𝑜𝑛𝑆ℎ𝑎𝑟𝑒 𝑐𝑐,𝑒𝑐Both the estimated electrical power and the yearly electricity consumption of an average best guess node in
the Ethereum PoS network are provided in Table 15.

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The Merge – Implications on the Environmental Sustainability of Ethereum

Ethereum PoS per node
Best guess [W]62.44

Best guess [kWh / year]

547.01

Table 15: Best guess estimates for electrical power and electricity consumptionof the Ethereum PoS network per single nodeUpper and lower bound
Apart from this best guess estimate for an average node in the network, we can determine upper and lower
limits for the power consumption of nodes participating in the network. These upper and lower bounds are
determined by the least efficient and most efficient hardware, respectively. The lower bound therefore isconstituted by configuration 4 from Table 6. Accordingly, configuration 6 serves as an upper bound. The
resulting bounds, weighted by the share of the different client configurations, are summarized in Table 16.
Ethereum PoS per node
Lower bound [W]

20.00

Lower bound [kWh / year]

175.19Upper bound [W]150.06Upper bound [kWh / year]

1,314.75

Table 16: Weighted lower and upper bounds of electrical power and electricity consumptionof the Ethereum PoS network per single node

6.

Electricity consumption of the Ethereum PoS network

To estimate the electrical power and the yearly consumption of the entire network, we apply our best guessestimation to the number of active beacon chain nodes. The number of nodes is obtained from an explorer
called Beacon Chain Network Public Dashboard of Miga Labs28 for the 5th of September 2022. The results are
depicted in Table 17. We find that the electricity consumption of the network amounts to 2,600,863.27 kWhannually in our best guess:
Ethereum PoS
Beacon chain node countElectrical power of network [W]4,755
296,902.20

Consumption / day [kWh]7,125.65

Consumption / year [kWh]2,600,863.27

Table 17: Overview of electricity consumption of the Ethereum PoS network
applying the best guess estimate

28

https://migalabs.es/eth2-client-analyzer/

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The Merge – Implications on the Environmental Sustainability of Ethereum

7.

Electricity consumption per transaction of the Ethereum PoS network

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, andelectricity requirements. Still, companies that want to report emissions associated with cryptocurrency
exposure should not necessarily rely on a transaction-based allocation approach but should also consider
other methodologies in order to avoid potential underreporting (Gallersdörfer et al., 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 metric provides a straightforward
insight into different protocols, its base assumptions need to be understood and its results must be treatedwith care.
Lastly, the electricity consumption per transaction is only a single 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 investing in; inan extreme case, a network consisting of a single, high-performance computer, would be the mostsustainable cryptocurrency, however making nonsense of the decentralization idea.
As we measured the electricity consumption of our nodes in real-world scenarios, we can rely on thetransaction count that took place during the respective time periods. We consider the measurements of thethree execution clients (Geth, Erigon, and Besu) and calculate the average number of transactions that took
place during these three measurements that took place sequentially. Since the measurements were takenover 48 hours, we divide the resulting value by two in order to obtain an average number of transactions perday. To calculate the power consumption per transaction, we apply our best guess node for the entire network
as defined in chapter III section 5. Hence, the results of individual node measurements are weighted for boththe binomial distribution and the client diversity within the Ethereum network. The results can be found in
Table 18.
Ethereum PoS
Wh/tx per node0.0013237

Wh/tx per network

6.2943

Number of tx per day

1,132,081

Table 18: Best guess electricity consumption of the Ethereum PoS network on a per-transaction basis.
The transaction count amounts to the average number of transactions per 24hthat took place during our measurements.
As expected, this metric depends on the number of transactions taking place on the blockchain, also theoverall electricity consumption per transaction further depends on the number of nodes connected to the

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The Merge – Implications on the Environmental Sustainability of Ethereum

Ethereum Beacon Chain. Generally, these numbers are expected to go down with an increase in thetransaction rate, regardless which blockchain is in use.

8.

Verification of approach to estimate electricity consumptions

In order to estimate the electricity consumption of the Ethereum PoS network accurately, the diversity of client
software must be considered in the calculation. As explained in section 4 of chapter III, we execute several
consensus and execution clients separately to conduct measurements. By subtracting the previously
measured average electricity consumption of the hardware in idling state from the measured results, we candetermine the marginal electricity consumption for each client software per computer. Hence, by summing
up both clients’ marginal electricity consumption and adding the respective idle consumption, we caninvestigate various combinations of one consensus and one execution client. This allows us to quantify theelectricity consumption of multiple different client setups.
To verify this approach, we have additionally measured the simultaneous execution of the most popular client
configuration, i.e., Prysm as the consensus and Geth as the execution client (35.65 %, see Table 14). Relying on
the binomial hardware distribution as introduced in section 5, we calculated the electricity consumption of abest guess node in the Ethereum PoS network that runs this specific configuration. Table 19 summarizes theresults in comparison to the results we obtain for the same best guess node by our combinatorial approach,which means by adding up the idle as well as the marginal electricity consumption of Prysm and Geth, as
provided in Table 14.
Ethereum PoS

Combinatorial determination(Prysm + Geth + Idle)

Best guess [W]60.96

Best guess [kWh / year]

533.97

Number of tx (Geth measurement)

Direct measurement(Prysm & Geth)2,431,505

Best guess [W]

54.91

Best guess [kWh / year]

481.00

Number of tx

2,138,612

Table 19: Comparison between combinatorial approach and concrete measurement ofbest guess estimates for a client configuration executing Prysm and Geth
The results show that both estimates for the same best guess node are quite close to each other, but the
combinatorial calculation we apply slightly overestimates the electricity consumption in comparison to thedirect measurement of the same client configuration (9 %). However, it should be noted that themeasurements were not taken at the same points in time and, therefore, are not perfectly comparable. During
the verification measurement, which means while monitoring the simultaneous execution of Prysm and Geth,292,893 transactions less have occurred compared to the time of collecting data for our combinatorial
calculation. This corresponds to a reduction of over 12 % regarding the number of transactions, which is mostprobably one reason for the slightly lower electricity consumption and thus the difference between both
estimation approaches. If the best guess power consumption of the verification measurement were reducedby 12 %, the difference between both approaches would be almost negligible.

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The Merge – Implications on the Environmental Sustainability of Ethereum

9.

Carbon footprint of the Ethereum PoS network

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 the Ethereum network 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 findthe application of the average grid intensity factor to be plausible for a solid estimate of the carbon footprintof the Ethereum network.
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 the Ethereum Beacon Chain, information on the node location is collected by Miga
Labs and presented on their public dashboard (Miga Labs, 2022). Figure 3 displays the node locations on theworld map provided by Miga Labs on the 6th of September 2022.

Figure 3: Distribution of Beacon nodes in the
Ethereum PoS network (September 2022) (Miga Labs, 2022)

Utilizing country specific emission factors (carbon footprint, 2022), we calculate the average carbon intensityof the network to be 334.42 gCO2e/kWh. For unavailable carbon intensities and unavailable node locations,
we assume the world average of 459 gCO2e/kWh in accordance with the IEA (International Energy Agency,

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The Merge – Implications on the Environmental Sustainability of Ethereum2021) which is the case for 1.95 % of all nodes in the dataset. With that, we can derive the carbon footprint of
the network using the following formula:∑𝑖 ∈ 𝑛𝑜𝑑𝑒

𝐸𝑛𝑒𝑟𝑔𝑦𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝑖 ∗ 𝐶𝑎𝑟𝑏𝑜𝑛𝐼𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦

The resulting values are depicted in Table 20.

Ethereum PoS

CO2e emissions / year [t]Lower bound278.60Best guess869.78Upper Bound

2,090.32

Table 20: Overview of CO2e emissions of the networks on an annual basis
as of time of measurement (August-September 2022)

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The Merge – Implications on the Environmental Sustainability of Ethereum

IV. Results and discussion
In this chapter of the report, we discuss and contextualize the results of our work.
The Merge will result in a significant reduction of the electricity consumption and carbon footprint of the
Ethereum network, putting it in a similar range with other Proof of Stake networks. The detailed values for boththe pre-Merge (PoW) Ethereum network, the post-Merge (PoS) Ethereum network, and the reduction of therespective sustainability metric are depicted in Table 21.

Ethereum PoW
Electricity consumption [MWh/year]
CO2e emissions [t/year]

Ethereum PoS

Reduction factor

22,900,320

2,600.86

0.99988

11,016,000

869.780.99992

Table 21: Comparison of annualized electricity consumption and CO2e emissionsof the Ethereum network before (PoW) and after (PoS) the Merge

According to our estimates, the Merge reduces the annualized electricity consumption of the Ethereum
network from 22,900,320 MWh to 2,600.86 MWh by over 99.988 %. This is a reduction by a factor of over 8,639.
In addition to the electricity consumption, the carbon footprint is reduced from 11,016,000 tCO2e to869.78 tCO2e, a reduction by 99.992 %, a factor of over 12,425.
The difference between the reduction factors for the electricity consumption and carbon footprint for preand post-Merge are a result of different emission factors. For Ethereum’s Proof of Work network, we assume asignificantly higher emission factor of ~523 gCO2e/kWh, whereas we estimate the carbon intensity of
Ethereum’s PoS network to be ~334 gCO2e/kWh. Miners in Proof of Work networks seek cheapest electricity, as
the electricity costs are a significant business expense. In contrast, validators in Proof of Stake networks do
not need to cater to electricity costs in the same manner, as their main expense is, besides the initial stake of
32 ETH, hardware and maintenance costs.
Following figure might help to visualize the electricity consumption reduction the Merge will result in. The Eiffel
Tower (330 meters) represents the total electricity consumption of the pre-Merge Ethereum network for one
month, the resulting electricity consumption of the post-Merge Ethereum network will be ~3.8 cm high, about
the same height as a single plastic toy figure.

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The Merge – Implications on the Environmental Sustainability of Ethereum

Figure 4: Comparison of the reduction of the electricity consumption of Ethereum pre-Merge andpost-Merge on the example of the Eiffel tower and a plastic toy figure29.

29

Eiffel tower image from https://wikipng.net/photo/7939/architecture-eiffel-tower-png-free,

Plastic toy figure taken from https://publicdomainvectors.org/en/free-clipart/Plastic-toy-vector-drawing/31404.html.

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The Merge – Implications on the Environmental Sustainability of Ethereum

V.

Conclusion

The Merge is the most significant update for the Ethereum network and the cryptocurrency space in general.
It changes the consensus mechanism of Proof of Work to Proof of Stake, reducing the electricity consumption
and carbon footprint of the network significantly.

In this report, we first discuss, analyze, and calculate the electricity consumption and carbon footprint of the
pre-Merge (PoW) Ethereum network. Second, we outline an approach for calculating the electricity
consumption and carbon footprint of the post-Merge (PoS) Ethereum network, accounting for Ethereum’s highclient diversity and node locations. Third, to provide an estimate of the emissions, we selected hardware,
made measurements of the individual nodes, and calculated the respective metrics.

For the pre-Merge Ethereum network, we calculate the electricity consumption to be 24.10 TWh and thecarbon footprint to be 13.64 MtCO2e for the period from 1st August 2021 to 31st July 2022. The respective
annualized metrics based on the last month of the pre-Merge network amount to 22,900,320 MWh and11,016,000 tCO2e, respectively.

For the post-Merge Ethereum network, we estimate the annualized electricity consumption to be
2,600.86 MWh and the carbon footprint to be 869.78 tCO2e based on the measurements taken place inAugust and September 2022.

Therefore, the Merge reduces the electricity consumption and carbon emissions of the Ethereum networkby 99.988 % and 99.992 %, respectively. Applying this reduction of electricity consumption to the height of theEiffel tower, it would shrink to about the size of a plastic toy figure.

Given the continuous development and evolution of the Ethereum network, our results can only be taken as asnapshot of the respective timeframe. Further 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. Additionally, other networks employing different consensus mechanisms need to betaken into account to gain a holistic picture of the environmental impact of cryptocurrencies and tokens.

In recent years, Ethereum has faced harsh criticism for its electricity demand and carbon emissions. The
Merge and its reduction of electricity consumption and carbon footprint by over 99.98 % marks a significant
milestone in both the Ethereum network as well as for the entirety of the cryptocurrency space. After the Merge
has taken place, a major step towards environmental sustainability has been done.

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The Merge – Implications on the Environmental Sustainability of Ethereum

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(2022).

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Specific

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CCRI. (2022a). CCRI Cryptocurrency Sustainability API. https://docs.api.carbon-ratings.com
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de Vries, A. (2022b). Ethereum Energy Consumption Index. https://digiconomist.net/ethereum-energyconsumption
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De Vries, A., & Stoll, C. (2021). Bitcoin's growing e-waste problem. Resources, Conservation and Recycling, 175,
105901.
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 andtoken systems. arXiv preprint arXiv:2111.06477.
Hennion, N. (2021). Glances - An eye
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53(23), 13598-13606.
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The Merge – Implications on the Environmental Sustainability of Ethereum

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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The Merge – Implications on the Environmental Sustainability of Ethereum

Appendix B: Electricity measurements of single consensus andexecution clients

All electricity measurements are conducted in Watt.

Prysm

1

2

3

4

5

6

Min [W]

N/A

N/A

N/A

6.32

27.07

96.37

Q1 [W]

N/A

N/A

N/A

6.99

27.69

100.93

Median [W]

N/A

N/A

N/A

7.13

27.86

102.36

Mean [W]

N/A

N/A

N/A

7.17

27.91

102.50Q3 [W]

N/A

N/A

N/A

7.32

28.06

103.89Max [W]

N/A

N/A

N/A

13.09

34.08

118.42

Software version

beacon-chain-v2.1.4-linux-amd

Measurement period2022-07-17 03:14:57,349 to 2022-07-19 03:14:59,194

Table 22: Electrical power while running a node with Prysm consensus client measured in Watt [W]Lighthouse

1

2

3

4

5

6

Min [W]

N/A

N/A

N/A

5.62

27.18

89.97

Q1 [W]

N/A

N/A

N/A

6.11

27.82

95.45

Median [W]

N/A

N/A

N/A

6.26

28.03

96.74

Mean [W]

N/A

N/A

N/A

6.41

28.18

97.01

Q3 [W]

N/A

N/A

N/A

6.42

28.29

98.25

Max [W]

N/A

N/A

N/A

14.28

38.09

118.39Software version

v2.4.0-21dec6f

Measurement period2022-08-04 11:39:26,451 to 2022-08-06 11:39:26,552

Table 23: Electrical power while running a node with Lighthouse consensus client measured in Watt [W]

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The Merge – Implications on the Environmental Sustainability of Ethereum

Teku

1

2

3

4

5

6

Min [W]

N/A

N/A

N/A

5.97

27.50

100.16

Q1 [W]

N/A

N/A

N/A

6.46

27.88

104.15Median [W]

N/A

N/A

N/A

7.44

28.02

105.31

Mean [W]

N/A

N/A

N/A

7.37

28.36

105.63Q3 [W]

N/A

N/A

N/A

7.80

28.24

106.65

Max [W]

N/A

N/A

N/A

26.46

41.43

130.58

Software version

22.8.0 (JDK: OpenJDK 11.0.16 2022-07-19)

Measurement period2022-08-13 11:08:35,624 to 2022-08-15 11:08:35,726

Table 24: Electrical power while running a node with Teku consensus client measured in Watt [W]Nimbus

1

2

3

4

5

6

Min [W]

N/A

N/A

N/A

4.72

26.08

84.67

Q1 [W]

N/A

N/A

N/A

5.09

26.69

92.71

Median [W]

N/A

N/A

N/A

5.17

26.89

94.99

Mean[W]

N/A

N/A

N/A

5.33

27.12

95.28

Q3 [W]

N/A

N/A

N/A

5.31

27.29

97.55

Max [W]

N/A

N/A

N/A

9.52

36.95

111.10Software version

22.7.0Measurement period2022-08-17 11:02:03,248 to 2022-08-19 11:02:04,123

Table 25: Electrical power while running a node with Nimbus consensus client measured in Watt [W]Lodestar

1

2

3

4

5

6

Min [W]

N/A

N/A

N/A

5.82

27.64

100.94

Q1 [W]

N/A

N/A

N/A

6.48

28.52

110.02

Median [W]

N/A

N/A

N/A

6.68

28.80

111.86

Mean [W]

N/A

N/A

N/A

6.80

28.93

111.72Q3 [W]

N/A

N/A

N/A

7.01

29.20

113.58Max [W]

N/A

N/A

N/A

9.98

33.91

131.68

Software version

v1.0.0/d78131b (Node: 16.17.0, Yarn: 1.22.19)

Measurement period2022-08-26 13:01:03,054 to 2022-08-28 13:01:03,156

Table 26: Electrical power while running a node with Lodestar consensus client measured in Watt [W]

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Geth

1

2

3

4

5

6

Min [W]

N/A

N/A

N/A

4.77

26.32

81.94

Q1 [W]

N/A

N/A

N/A

13.53

33.24

123.71

Median [W]

N/A

N/A

N/A

14.86

34.58

125.93

Mean [W]

N/A

N/A

N/A

14.89

34.74

125.87

Q3 [W]

N/A

N/A

N/A

16.18

36.07

128.00Max [W]

N/A

N/A

N/A

26.71

54.63

152.12

Software version

1.10.21-stable (Go: 1.18.04)

Measurement period2022-08-20 11:57:46,076 to 2022-08-22 11:57:46,177

Table 27: Electrical power while running a node with Geth execution client measured in Watt [W]Erigon

1

2

3

4

5

6

Min [W]

N/A

N/A

N/A

16.97

39.33

117.72

Q1 [W]

N/A

N/A

N/A

21.30

41.96

119.88Median [W]

N/A

N/A

N/A

22.20

42.44

121.20Mean [W]

N/A

N/A

N/A

22.26

42.63

122.79Q3 [W]

N/A

N/A

N/A

23.06

43.00

124.73

Max [W]

N/A

N/A

N/A

34.40

55.90

152.19Software version

2022.99.99-dev-15ca3d25 (Go: 1.18.04)

Measurement period2022-08-09 18:08:23,164 to 2022-08-11 18:08:23,762

Table 28: Electrical power while running a node with Erigon execution client measured in Watt [W]

Besu

1

2

3

4

5

6

Min [W]

N/A

N/A

N/A

31.70

51.73

104.84

Q1 [W]

N/A

N/A

N/A

33.16

54.76

151.39Median [W]

N/A

N/A

N/A

33.73

55.42

153.19Mean [W]

N/A

N/A

N/A

33.91

56.06

153.21Q3 [W]

N/A

N/A

N/A

34.36

56.41

155.05

Max [W]

N/A

N/A

N/A

40.36

70.40

169.27

Software version

22.7.1 (JDK: OpenJDK 11.0.16)

Measurement period2022-09-02 09:06:38,844 to 2022-09-04 09:06:38,854

Table 29: Electrical power while running a node with Besu execution client measured in Watt [W]

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The Merge – Implications on the Environmental Sustainability of Ethereum

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, 2022
Crypto Carbon Ratings Institute (CCRI) is a brand of CCRI GmbH based in Dingolfing, Germany.

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