∎ It can be accessed at
https://link.springer.com/article/10.1007%2Fs00287-020-01321-z#Ack1

PoW

    Proof-of-Work
PoS

    Proof-of-Stake
PoA

    Proof-of-Authority
11institutetext: Johannes Sedlmeir (Corresponding author) 22institutetext: FIM
Research Center, University of Bayreuth
Project Group Business & Information Systems Engineering of the Fraunhofer FIT
Bayreuth, Germany
22email<EMAIL_ADDRESS>33institutetext: Hans Ulrich Buhl
44institutetext: FIM Research Center, University of Augsburg
Project Group Business & Information Systems Engineering of the Fraunhofer FIT
Augsburg, Germany
44email<EMAIL_ADDRESS>55institutetext: Gilbert Fridgen
66institutetext: SnT - Interdisciplinary Center for Security, Reliability and
Trust
University of Luxembourg
66email<EMAIL_ADDRESS>77institutetext: Robert Keller
88institutetext: FIM Research Center, University of Augsburg
Project Group Business & Information Systems Engineering of the Fraunhofer FIT
Augsburg, Germany
88email<EMAIL_ADDRESS>
# Recent Developments in Blockchain Technology and their Impact on Energy
Consumption

Johannes Sedlmeir  Hans Ulrich Buhl  Gilbert Fridgen  Robert Keller
0000-0003-2631-8749 0000-0001-7037-4807 0000-0001-7097-1724

###### Abstract

The enormous power consumption of Bitcoin has led to undifferentiated
discussions in science and practice about the sustainability of blockchain and
distributed ledger technology in general. However, blockchain technology is
far from homogeneous – not only with regard to its applications, which now go
far beyond cryptocurrencies and have reached businesses and the public sector,
but also with regard to its technical characteristics and, in particular, its
power consumption. This paper summarizes the status quo of the power
consumption of various implementations of blockchain technology, with special
emphasis on the recent “Bitcoin Halving” and so-called “zk-rollups”. We argue
that although Bitcoin and other proof-of-work blockchains do indeed consume a
lot of power, alternative blockchain solutions with significantly lower power
consumption are already available today, and new promising concepts are being
tested that could further reduce in particular the power consumption of large
blockchain networks in the near future. From this we conclude that although
the criticism of Bitcoin’s power consumption is legitimate, it should not be
used to derive an energy problem of blockchain technology in general. In many
cases in which processes can be digitised or improved with the help of more
energy-efficient blockchain variants, one can even expect net energy savings.

###### Keywords:

blockchain cryptocurrencies energy consumption distributed ledger technology
sustainability

††journal: Informatik Spektrum

## Introduction

In leading German print media one can find statements that the Bitcoin system
consumes about as much electricity as the Federal Republic of Germany,
tendency rising (Frankfurter Allgemeine Zeitung, 2020-06-06 Thiel, (2020)). On
the other hand, an article was published in the magazine _Nature Climate
Change_ in 2018, according to which if Bitcoin is adopted on a large scale,
the emissions caused by it alone could lead to global warming of more than 2∘C
in the next three decades Mora et al., (2018). The FAZ article was modified on
our initiative shortly after its publication in the online version, and the
Nature article was followed by a controversial scientific discussion about the
sense of the underlying assumptions. Nevertheless, such publications lead to
an incorrect impression in the public regarding the ecological consequences of
Bitcoin and to an even more problematic generalisation to blockchains.

In essence, the statement that Bitcoin and also many other cryptocurrencies
cause an enormous power consumption is correct and important and has been
analysed in detail in numerous publications, including the journals _Joule_ De
Vries, (2018); Stoll et al., (2019); Gallersdörfer et al., (2020) and _Nature
Sustainability_ Krause & Tolaymat, (2018). Frequently, however, it is
precisely these striking statements that remain present, are taken out of
context, are incorrectly generalised or used for lines of argumentation that
testify to a lack of understanding of the fundamental interrelation between
the high electricity consumption of some cryptocurrencies and economic as well
as technical parameters. For example, Bitcoin’s electricity consumption does
not necessarily increase steadily nor does it grow significantly with the
number of transactions processed per time unit. Moreover, blockchain
technology is mentioned in the same breath as Bitcoin so frequently, both in
public reporting and, to some extent, in the scientific community, that
certain prejudices regarding the power consumption of blockchain technology
have become generally established.

In fact, there are now numerous cryptocurrencies based on technically
significantly modified blockchain variants with completely different
characteristics with regard to their power consumption. The situation is
similar for a large number of implementations of blockchain-based platforms
for cross-organisational processes in business and the public sector. In
Germany, for example, there are projects by automobile manufacturers in the
supply chain Miehle et al., (2019) or the Federal Office for Migration and
Refugees Rieger et al., (2019). As the topic of sustainability is rightly very
present in politics and economy Gimpel et al., (2019), the question of
electricity consumption and the sustainability of blockchain technology in
general is very often asked in the context of blockchain-related projects for
the reasons described above. The presence of this electricity consumption
stigma could therefore significantly impede the adoption of blockchain
technology and, thus, the exploitation of its benefits Beck et al., (2018).

Accordingly, in this paper we want to give a comprehensive overview of the
electricity consumption of blockchain technology in general in order to
provide a solid basis for the general discourse. To this end, we first
describe well-known estimates for the energy consumption of Bitcoin, but
expand on these estimates by a detailed discussion of the recent Bitcoin
Halving, which reveals many of the fundamental interrelations. In the Bitcoin
Halving event, which periodically takes place approximately every four years,
the number of new Bitcoins created per block and serving as a reward for the
miner is halved. This ensures that the number of existing Bitcoins remains
limited (geometric series). The aim of this construction is to reduce
inflation. On the other hand, we are also investigating a larger part of the
very heterogeneous spectrum of blockchains than just some cryptocurrencies
that are technically closely related to Bitcoin. With this we extend an
article Sedlmeir et al., (2020) published by us in the magazine _Business &
Information Systems Engineering_ on the energy consumption of blockchains,
which already discusses some of the issues addressed in this article and
focuses more on the sustainability discussion of blockchain technology
applications beyond cryptocurrencies. In comparison, we will go into more
detail on some aspects only briefly discussed there. In particular, we will
quantitatively analyse the implications of using so-called zk-rollups on the
power consumption of blockchains in addition to Bitcoin Halving.

Despite the fact that blockchain technology is used in a much wider range of
applications than in Bitcoin and cryptocurrencies, Bitcoin also plays a
central role in this article. This is due to its problematic high energy
consumption. We believe, however, that other applications of blockchain
technology are much more important in the long run.

## Basics of Bitcoin and Blockchain Technology

The Bitcoin blockchain was developed to create a decentralised electronic
currency system. The transfer of assets – in contrast to the transfer of
information – is not readily possible bilaterally in electronic form, since
electronic objects can be copied practically as often as desired without
effort. Therefore, although the information contained in the electronic
objects may be valuable in itself, no value is transferred by the transmission
or storage of the electronic object itself Faulkner & Runde, (2019).
Accordingly, for the electronic transfer of values within a certain group, a
so-called ledger that is accepted by all members and contains the ownership
relationships is required. A change in such ownership in an electronic
register can therefore be understood as the electronic transfer of values
(“transaction”).

Traditionally, trusted third parties, e.g., banks in the monetary context,
have controlled such digital ledgers in the form of databases. In contrast,
the cryptocurrency Bitcoin, which was presented in a white paper in 2008
Nakamoto, (2008) and was subsequently implemented and went into operation in
2009, is based on the decentralised management of the corresponding ledger by
redundant and synchronised (“physically decentralised, logically centralised”)
maintenance of the ledger on all participating computers (“nodes”). This means
that the validity and execution of transactions is now decided by all
participants in the Bitcoin network rather than by a single central authority.
Proving ownership of units of the cryptocurrency (token-based model) or
authorising payments (account-based model) is done with the help of a public
key infrastructure and corresponding digital signatures. For the purpose of
majority decision making, the Bitcoin network requires a so-called consensus
mechanism whereby the nodes decide which new transactions to include, and in
what order.

In principle, such replicated state machines, which guarantee the security and
functionality of a distributed network even in the presence of system failures
or Byzantine errors, have been intensively researched Lamport et al., (1982)
since 1982 and then practically implemented with PaxosLamport, (1998) and PBFT
Castro et al., (1999). Consensus could be found based on elections, following
the principle of “one node, one vote”. What is new about Bitcoin, however, is
that not only predefined nodes can participate in the network and the
consensus process, but anyone who wants to can do so. This is known as a
public permissionless system. In it, the election-based process just described
is not possible, because an attacker who wants to overrule the system would
only have to register a sufficient number of accounts on the network, which
would be possible for them without significant costs (this is called “Sybil
attack”, see, e.g., Douceur, (2002)).

A permissionless system like Bitcoin must therefore tie the weight of a vote
to a scarce resource in order to prevent such attacks. With Bitcoin and many
other cryptocurrencies this is done via the so-called “ Proof-of-Work (PoW)”,
i.e., the weight of the vote is linked to proven, performed computing work
and, thus, energy. PoW involves finding a random number, the so-called nonce,
so that the hash value of the nonce – together with other data – takes on a
certain form. In the case of Bitcoin, this is the requirement that the integer
representation of the hash value is smaller than a certain upper limit. The
choice of this upper limit thus defines a measure of complexity, the so-called
difficulty, of this cryptographic puzzle. The difficulty is indirectly
proportional to the probability that a randomly chosen nonce leads to a hash
value of the desired form. This method of proving computation effort has been
known for a long time and has been discussed, for example, in Hashcash for
preventing spam Back et al., (2002).

Participation in the PoW consensus mechanism, i.e. the search for appropriate
nonces, is therefore associated with costs, so that an economic incentive must
be created for participation in mining: Whoever finds a nonce that, together
with a bundle of transactions, leads to a hash value of the required form may
also register a reward for themselves, more precisely, a certain number of new
Bitcoins created for this purpose (“block reward”). The corresponding block
can then be communicated to the other participants in the blockchain network
and thus be attached to the existing chain, which means that the corresponding
transactions are executed. It should be mentioned that due to the competition
in Bitcoin mining, participation with CPUs has long since become unprofitable,
as specialised hardware, so-called ASICs, have been developed that can
calculate hashes by orders of magnitude faster and more energy-efficiently
than CPUs and GPUs De Vries, (2018). The difference in Bitcoin is so
significant that even the world’s 500 largest supercomputers taken together
can probably only achieve a small part of the current hash rate of Bitcoin,
which is mainly based on ASICs – and can only conduct mining at a considerable
financial loss.

To prevent undetected manipulation of the system, Bitcoin uses a data
structure that makes it very easy to detect and locate subsequent changes,
namely Merkle Trees. In order to reduce the effort involved in finding a
consensus, transactions, metadata, nonce and a hash pointer to the previous
block are combined in one block. The resulting append-only structure (“chain”)
is given the property that changing only one single transaction either leads
to the inconsistency of a single block (wrong Merkle root) or all hash
pointers from the manipulated block on would have to be changed as well. Due
to the requirement that the hash value of each block must have the form
described above, finding such blocks and thus an alternative chain of hash
pointers is very computationally intensive, so that the system is secure as
long as a large part of the hash rate is provided by “honest” nodes (for a
detailed presentation of attack scenarios see Eyal & Sirer, (2014)). The data
structure of blocks and hash pointers is generally characteristic of
blockchain technology, which in turn is a special case of so-called
distributed ledger technologies. However, blockchain technology is usually
understood to comprise not only this data structure, but also the existence of
a consensus mechanism that both allows agreement on the addition of new
transactions and ensures that no subsequent changes can be made to the
blockchain. For this purpose, however, other methods can be used instead of
competing for a solution of computationally intensive puzzles. These are
usually based on digital signatures which, depending on the consensus
mechanism, are created by network participants either according to fixed rules
or (pseudo-)randomly. We will briefly describe some of these variants in the
discussion of alternative consensus mechanisms.

## Estimates for the Energy Consumption of PoW Blockchains

As described above, the incentive for participating in mining in the Bitcoin
blockchain and generally in PoW-based blockchains is a reward in the form of a
fixed number of units of the corresponding (“native”) cryptocurrency for a
node that finds the next block. Due to the strong price increases of
cryptocurrencies with a peak at the end of 2017 and a market capitalisation of
briefly over 300 billion and since then always over 50 billion US dollars of
Bitcoin alone, there is and has been a strong economic incentive to
participate in mining. In order to maintain the functionality (and also the
security) of the PoW blockchain network, the time span in which a new block is
usually found must be kept constant, i.e., the difficulty of the hash puzzle
must be adjusted according to the current hash rate. This leads to a
correspondingly high power consumption of PoW based cryptocurrencies.

The exact determination of the power consumption in a public permissionless
PoW blockchain is in general very difficult because usually neither the
computing power used in mining nor the corresponding hardware can be
determined for each individual participant. However, a lower limit for the
power consumption of Bitcoin and all other PoW-based blockchains can easily be
determined from the indirectly observable average computing power, i.e. the
hash rate, and the most energy-efficient mining hardware on the market
Vranken, (2017); De Vries, (2018). In doing so, one can estimate the expected
value of the hash rate from the publicly visible, current difficulty of the
hash puzzle and the number of solutions communicated in the form of new
blocks. In Bitcoin, by construction of the protocol, a new solution of the
hash puzzle is found around every 10 minutes on average, and the probability
that a random hash value meets the requirements was about 1 : 6$\times$1022 at
the beginning of 2020. For Bitcoin, SHA-256 is used as the hashing algorithm;
modern ASICs achieve hash rates in the order of $10^{\mathrm{14}}$ hashes per
second at a power of a few thousand watts. This results in a lower limit for
the electricity demand of Bitcoin in early 2020 of about 60 TWh per year
Sedlmeir et al., (2020), which corresponds to the annual electricity
consumption of about 15 million households.

An upper limit for the electricity consumption caused by mining can also be
estimated, as long as it is assumed that all participants act rationally,
meaning that they aim to make a profit by participating in mining. This may
not be true for all participants, but the vast majority of the computing power
for Bitcoin and other relevant PoW cryptocurrencies is provided by companies
or groups specialising in mining (“pools”) Romiti et al., (2019), so this
assumption seems reasonable for them. The value of the economic incentive,
i.e. the new Bitcoins produced by mining, must be at least as high on average
as the costs caused by mining, e.g. for electrical energy and hardware, and,
thus, in particular higher than the expenses for electricity. A lower limit
for the costs of electrical energy in countries with significant mining
involvement is usually set at USD 0.05 per kilowatt-hour GlobalPetrolPrices,
(2019); University of Cambridge, (2020), and this results in an extrapolated
upper limit for electricity consumption of about 120 TWh per year for early
2020 at a Bitcoin price of just under USD 10 000, which corresponds to about
20 % of the German electricity consumption Sedlmeir et al., (2020).

For other well-known PoW blockchains, such as Ethereum, Bitcoin Cash, Bitcoin
SV and Litecoin (these are the largest PoW-based cryptocurrencies after
Bitcoin in terms of market capitalisation), the same estimation formulas apply
as for Bitcoin, except that there are other hashing algorithms, specialised
mining hardware and parameters such as average block times and block rewards.
In total, the electricity consumption of the four cryptocurrencies mentioned
above is between 10 and 40 TWh per year, which is significantly lower than
that of Bitcoin. It is also noted that due to generally similar parameters,
there is a very high correlation between market capitalisation and electricity
consumption for different PoW cryptocurrencies. As the market capitalisation
of Bitcoin is higher than the cumulative market capitalisation of all other
cryptocurrencies, it can be assumed that the cumulative electricity
consumption of all PoW cryptocurrencies is not much more than twice that of
Bitcoin, and a “Best Guess” is at a factor of approximately 1.5 Sedlmeir et
al., (2020); Gallersdörfer et al., (2020).

An important observation for PoW cryptocurrencies is that their power
consumption cannot be reduced in the long term by increasing the energy
efficiency of hardware: On the one hand, this can be seen from the fact that
the estimation of the upper bound depends only on electricity prices and not
on total computing power. The reason for this is that in the long term, all
miners would switch to more energy-efficient hardware as long as mining is
profitable. Accordingly, as described above, the total computing power of the
network will increase until the balance of revenue and expenses sides is
approximately restored.

Due to the requirement to enable as many nodes as possible to participate in
cryptocurrencies and the redundant execution of all transactions, the
technical requirements for participation, i.e. network bandwidth and storage
space, must be kept as low as possible. Since the “slowest” permitted node
dictates the performance of the system, Bitcoin and other cryptocurrency
systems can only process a few transactions per second – currently, the
storage space required for the complete Bitcoin blockchain requires just under
300 GB and is growing by about 60 GB per year; a multiple of transactions per
time unit would also multiply the growth accordingly. Hence, simply dividing
the power consumption by the number of transactions in PoW-based
cryptocurrencies yields an enormous amount of energy on a per transaction
basis: For Bitcoin, the electricity consumption for a single transaction would
then amount to several hundred kWh and thus correspond to the electricity
consumption of an average German household in several weeks to months. This
leads to the frequent criticism of Bitcoin’s sustainability described above.
For other PoW-based cryptocurrencies, one gets significantly lower energy
consumption per transaction, but this is still orders of magnitude more
energy-intensive than, for example, a conventional booking in the banking
system. It is essential to understand, however, that the number of
transactions processed has no effect on the total network’s mining-related
power consumption, since in theory the blocks could be arbitrarily enlarged
Dittmar & Praktiknjo, (2019). Thus, the metric “energy per transaction” for
PoW-based cryptocurrencies needs to be considered quite carefully.
Nevertheless, given the performance of Bitcoin and other current PoW
blockchains, their power consumption can certainly be regarded
disproportionate.

## Outlook: Implications of the recent Bitcoin Halving

In the following, a more detailed analysis of Bitcoin’s electricity
consumption will be carried out by analysing the recent Bitcoin Halving and
deriving implications for the long-term development of electricity
consumption. The comparison of the development of Bitcoin prices and hash rate
over the past 12 months, as shown in Figure 1, suggests that the upper bound
described above is indeed a fairly good estimate of actual electricity
consumption: With relatively stable Bitcoin prices until March 2020, the
observed hash rate increased continuously; apparently, the initiation or
expansion of mining activities, which is associated with the procurement of
appropriate hardware, was considered worthwhile. However, a fall in the price
of Bitcoin in early March 2020 in the context of a generally weak stock market
sentiment due to the Covid-19 pandemic was accompanied by a slightly less
pronounced but nevertheless significant decrease in the hash rate. This could
be explained by the fact that due to the reduction in the value of Bitcoin and
thus the level of the mining incentive, miners with higher variable costs, for
example due to obsolete hardware or high electricity prices, were forced out
of mining here for a short period of time. Thereafter, aligned with the
Bitcoin price, the hashrate also rose back to the previous level. However, the
Bitcoin halving on 11 May 2020, an event that is similarly scheduled in many
PoW blockchains and which takes place approximately every four years in the
case of Bitcoin, caused a permanent halving of the block rewards and a
corresponding reduction of the economic incentive to mine. As Bitcoin prices
remained largely constant, the hash rate fell significantly, similar to the
previous situation.

Figure 1: Hash rate and price development for Bitcoin. 1 EH denotes 1018
hashes. Data retrieved from Blockchain.com, (2020).

Surprisingly, however, just one week later, the hash rate rose again
significantly, without Bitcoin prices rising to a similar extent. This could
be due to the following reasons:

  * •

A closer look at the expected profits from mining must also include
transaction fees received by the producer of a new block – especially after
halving, these fees made up to 20 % of the reward on some days, and the
transaction fees have also increased after the halving.

  * •

The Difficulty is not adjusted in real time, but only around every 14 days.
While with the Difficulty before the halving it might not have been worthwhile
to participate in mining, this might have changed after the Difficulty was
adjusted for the first time after the halving and thus significantly reduced
(it takes a while for the system to regain its balance).

  * •

In China, the rainy season begins in May and June in some regions, so that
much cheaper electricity is available through hydropower, and some mining
pools were offering electricity for just under 0.03 USD/kWh at that time;
especially since competition has become much fiercer in the face of declining
mining revenues BloombergNEF, (2020).

  * •

More and more modern, energy-efficient hardware is being purchased and used,
significantly reducing variable costs.

Figure 2: Hash rate and expected mining rewards for Bitcoin. Data retrieved
from Blockchain.com, (2020).

To investigate these relationships, we analyse the relationship between the
economic incentive to participate in mining, in the form of an expected mining
revenue in USD per 1018 calculated hashes, and the actual participation in the
form of the hash rate, in the period around the halving with a more accurate
model. This also takes into account the actual current block time or
difficulty as well as the transaction fees received by the generator of a new
block in addition to the block rewards. The resulting curves are shown in
figure 2. The correlation between expected profits and the hash rate is
approx. 0.57. This suggests that some miners are already deciding in real time
or in the short term whether or not it is worthwhile for them to participate
in mining because they have already operated close to cost neutrality before
halving. Apart from the irregularities around halving, lower correlations have
been observed, especially in the last two weeks analysed, which could be due
to the changes in electricity tariffs in China’s mining pools described above.

In order to analyse the influence of electricity tariffs in more detail, we
illustrate in Figure 3 the influence of mining hardware and electricity prices
on the relative margin, i.e., the ratio of mining profits (i.e. the difference
between mining revenues and electricity costs) to the electricity costs of
mining in detail. Only electricity consumption is taken into account; other
variable costs and investments (e.g. for hardware procurement) are ignored.
Thus, the data shown represents an upper limit of the relative margin. The
reference hardware used was the popular Bitmain Antminer S9 (11.5 Th) for
hardware newly launched in 2016, the MicroBT Whatsminer M10S for 2018, and the
Bitmain Antminer S19 Pro (110 Th) for 2020 Asicminervalue, (2020). At the time
of their market launch, these probably corresponded to the most energy-
efficient mining hardware for Bitcoin. The vertical line shows the time of the
Bitcoin halving, as in Figure 1 and Figure 2. In fact, at electricity prices
of 0.05 USD/kWh, the halving can force old, less energy-efficient hardware out
of the market in the short term, whereas more modern, more energy-efficient
hardware remaines profitable and, at lower electricity prices, mining with
older hardware also makes economic sense.

Figure 3: Profitability of mining Bitcoin. Data retrieved fromBlockchain.com,
(2020) andAsicminervalue, (2020).

Based on the observations, it is obvious that the derived upper bound is now
indeed a good estimator of the actual electricity consumption of Bitcoin.
Figure 4 shows the electricity consumption of Bitcoin resulting from the
different scenarios. The lower bound with 2020 hardware and the upper bound
with 0.025 USD/kWh can be considered very reliable, as it is unlikely that
significant mining activities with more efficient hardware than the most
advanced available on the market or electricity costs below 0.025 USD/kWh will
occur. More realistic, however, on the basis of actual hardware and
electricity prices typical of the network, are the two dashed barriers.
Although the estimates of Digiconomist Digiconomist, (2020) and Cambridge
Cambridge Centre for Alternative Finance, (2020) seem plausible and fit well
within these realistic limits given the safe upper and lower barriers, they
may not sufficiently explain the collapse of the hash rate as a result of the
price collapse and the Bitcoin halving. In this respect, one could well expect
that the actual hash rate before the halving was rather oriented towards the
upper limit of 0.05 USD/kWh and that after the halving, due to the increased
competition from cheap electricity tariffs, mining hardware that was initially
forced out of the market was also used again and thus the actual electricity
consumption even rose above the upper limit of 0.05 USD/kWh.

However, one could expect that this is only a temporary effect and that after
the end of the rainy season, electricity consumption will tend to return to
the upper limit of 0.05 USD/kWh. Even if electricity prices would stay lower
than 0.025 USD/kWh, it can be assumed that in this case it would be
electricity from renewable energy sources, as their marginal cost could also
be zero, whereas the cost of electricity generation from fossil or nuclear
fuels is unlikely to fall below 0.025 USD/kWh. In this respect, even exceeding
the “safe” upper barrier due to locally or temporarily low electricity prices
would probably not result in a higher CO2 footprint for Bitcoin than with the
upper barrier at 0.025 USD/kWh.

Figure 4: Estimates for the electricity consumption of Bitcoin.

Assuming that the economic conditions remain unchanged, i.e., the prices for
Bitcoin and electricity as well as the transaction fees, this orientation of
the actual electricity consumption at the upper limit also means that, due to
the periodic halvings, Bitcoin’s electricity consumption will decrease
significantly in the long term. At present, block rewards still account for
about 80 % of the mining revenues. If the prices for electricity and Bitcoin
and transaction fees remain unchanged, electricity consumption in 4, 8 and 12
years respectively would decrease by 40 %, 60 % and 70 % compared to the
beginning of 2020 and settle at about 20 % of today’s value, i.e., 20 TWh/a
and thus about 4 % of today’s German electricity consumption. However,
economic conditions such as the prices for Bitcoin and transaction fees cannot
be predicted reliably in practice. Nevertheless, it can be concluded that
already in a few decades, and with limited Bitcoin prices, the value of the
electrical energy consumed by Bitcoin will not be much higher than the
cumulative transaction fees. This will in any case hold from 2140 onwards, but
whether Bitcoin will be relevant for such a long period is difficult to assess
after only 11 years. Due to the competition with other cryptocurrencies as
well as technical developments, for example in terms of performance (reduction
of the scarcity of transactions), it could be assumed that transaction fees
will not increase significantly compared to today’s level. This line of
reasoning can be applied to many cryptocurrencies which, like Bitcoin, exhibit
a periodic halving of block rewards.

## Alternative consensus mechanisms

In general, the power consumption of blockchains is made up of two components:
The consensus mechanism, i.e. the process by which the nodes agree on which
transactions are executed in which order, and the redundant execution of the
transactions themselves, i.e. the verification of signatures and the
adjustment of “account balances” in the local database of each node (“state
transitions”). In Bitcoin, the PoW consensus mechanism as described above is
responsible for the enormous power consumption; the cumulative effort for
redundant transaction execution is negligible in comparison, even with the
current size of the network of approximately 10 000 nodes De Vries, (2018).
Since the development of Bitcoin, however, alternative consensus mechanisms
have been developed, partly because of the high power consumption of PoW. For
permissionless systems the most successful alternative so far is probably the
Proof-of-Stake (PoS) consensus mechanism. Here the voting weight is not linked
to the resource computing power but to the resource capital (possessed units
of the cryptocurrency, which is scarce and visibile and thus verifiable within
the blockchain network). Often capital has to be “frozen” for a certain period
of time in order to participate in voting and then serves as security to
create an economic incentive for correct behaviour of the nodes. With PoS the
competition for more and more computing power is thus eliminated. The energy
requirement associated with the consensus mechanism is accordingly negligible
compared to PoW and requires only a few operations on each node. Examples of
current implementations of blockchains with PoS consensus mechanism are
Algorand, Cardano, EOS and Tezos. In addition, Ethereum 2.0 (Serenity), the
second largest cryptocurrency by market capitalization, will probably go into
operation later this year. It is also based on PoS and is intended to replace
or integrate the current Ethereum blockchain in the long term EthHub, (2020).
Besides purely PoS-based blockchains there are also combinations of PoS and
election-based protocols, such as the consensus mechanism Tendermint used for
the Cosmos network. Such protocols also do not contain any particularly
computationally or energy-intensive process.

There are often discussions among supporters of PoW and PoS whether PoS is as
secure as PoW – there are good arguments for both sides. For example, PoW can
be expected to have long-term centralisation effects due to economies of scale
in mining (procurement of hardware and electrical energy) and location-
dependent economic framework conditions, which also reflects the current
empirically observable centralisation of Bitcoin mining. This in turn may lead
to security problems. With PoS, on the other hand, participation in the
consensus mechanism corresponds to a return on the capital invested, so that
the ratio of the capital of all participants in the consensus mechanism and
thus the weight of their vote remains constant. On the other hand, with PoW it
is also possible to participate in consensus without having to obtain
resources from the network itself, which could make re-decentralisation
considerably more difficult in an already highly centralised system. In
addition, many PoS systems currently only allow one’s own participation in the
consensus mechanism if a certain minimum deposit is made, and for PoW
blockchains there is also a simpler rule for deciding which blockchain is the
“valid” in case of a conflict (fork). The success of PoS-blockchains in the
last years as well as research in this field Kiayias et al., (2017) indicate
that PoS can guarantee a level of security that is comparable to the level of
security achieved in PoW.

Permissioned blockchains that are used in the context of consortia in the
private and public sector all use voting-based consensus mechanisms, which can
be regarded as partial crash-fault-tolerant simplifications (such as RAFT) or
byzantine-fault-tolerant (such as PBFT or RBFT) successors of Paxos. As with
PoS, their consensus-based energy consumption is thus also negligible.

## Redundant Operations

Regardless of the type of consensus mechanisms, all blockchains are
characterised by redundant data storage and operations. Accordingly, the
cumulative computing effort and thus power consumption – with the exception of
hardware differences – is proportional to the number of participants in the
blockchain network. For small blockchain networks, as they are typically used
in consortia in a permissioned context, the redundancy results in a multiple
power consumption compared to a central system. However, this does not
necessarily mean that the use of a blockchain has to be a disadvantage from a
sustainability perspective. The following rough estimate is intended to
illustrate this: A small, private blockchain network, such as Hyperledger
Fabric or Quorum, with 10 nodes, each with a mediocre hardware configuration
(2 CPUs, 8GB RAM) can easily handle 1000 simple transactions per second, thus
each transaction consumes around 1 J. On the other hand, based on the
information provided in VISA’s Sustainability Report 2017, it can be
calculated that the energy consumption of the entire company (i.e. including
heating of buildings, etc.) amounts to approximately 6 000 J per transaction,
of which 3 000 J per transaction are incurred by data centres VISA, (2017). A
simple client-server system with a key-value store, such as LevelDB, can
process several thousand simple transactions per second with the above
hardware equipment, resulting in a power consumption in the order of 0.02 J
per transaction. Although the power consumption of a blockchain is generally
much higher than that of a corresponding centralized solution (here by a
factor of around 50) due to the redundancy (and to some extent also due to the
consensus and generally the more extensive use of cryptographic methods), it
may still only account for a very small part of the power consumption of the
entire IT solution or the entire process, even if clients and backups are
included. Particularly in scenarios in which processes can be further
digitised with the help of energy-efficient variants of blockchain technology,
it is therefore reasonable to assume that blockchain-based solutions can also
save energy below the line.

In the best-known cryptocurrencies, such as Bitcoin and Ethereum, the
corresponding blockchain networks already consist of many thousands of nodes,
and with large-scale adoption, their number would be likely to rise even
further in the future. Accordingly, the power consumption of these networks
can be considered problematic because of the resulting high degree of
redundancy. However, research also has promising solution concepts for this
challenge: In principle, a reduced degree of redundancy, i.e. the
recalculation of transactions only on subsets of all nodes, will also reduce
the power consumption per transaction. This is the case, for example, with so-
called second layer concepts such as Lightening or Raiden, but typically
involves trade-offs in security, since security essentially stems from the
high degree of redundancy. Similarly, in the Ethereum 2.0 blockchain mentioned
above, the network will be divided into a total of 64 so-called shards, which
will be periodically referenced by a main chain, the so-called “Beacon Chain”,
thus inheriting the security of the entire system with each such referencing.
It will be some time before Ethereum 2.0 makes full use of these features and
processes are distributed among the various shards, so it is difficult to
quantify at this point how much the degree of redundancy will ultimately
decrease and what impact this will have on security and functionality.

## ZK-Rollups

Particularly promising is the progress that has been made in recent years in
connection with proofs of computational integrity using probabilistically
checkable, succinct proofs, which are perhaps better known in the blockchain
environment under the keyword “Zero-Knowledge Proofs” (ZKP). This makes it
possible to show (probabilistically) with a usually very short and quickly
verifiable proof that certain calculations have been performed correctly
without having to specify all details of the calculation or to repeat the
complete calculation. Initially, ZKPs were used by some cryptocurrencies such
as Z-Cash to restore the confidentiality of transactions, which is practically
non-existent with cryptocurrencies such as Bitcoin Zcash, (2020).

An essential characteristic of typical ZKPs is that the size of proofs and the
computational complexity of verifying the proofs usually scale sublinearly
(e.g. constant or polylogarithmic in the case of SNARKs or STARKs Gennaro et
al., (2013); Ben-Sasson et al., (2019)) with the size of the calculation to be
verified. This makes it possible that a single party, e.g. a crypto exchange,
bundles a large number (several thousand or even tens of thousand
transactions) in so-called “zk-rollups” and sends only a short proof that all
steps have been executed correctly (verification of signatures, correct
updating of account balances, …) as a transaction to the blockchain, where the
proof is checked by the other nodes with little effort. In detail, the
architectures can differ significantly, both in terms of the proof system used
and the other data stored on the blockchain, which must be updated
continuously (there is a trade-off here: less data on the blockchain means a
higher dependency on the party creating the evidence and updating and storing
the account balances of all accounts, but also higher scalability, as the
blockchain then does not represent a bottleneck).

In contrast to existing second layer solutions, zk-rollups with complete on-
chain data storage can provide the same security guarantees as the blockchain
itself because the proof is still checked by all participants in the network
and thus manipulations can be excluded with the same security as conventional
transactions Gluchowski, (2019). In addition, significant improvements can be
achieved compared to the traditional processing of transactions, as the
majority of the storage and computing capacity is based on digital signatures,
the verification of which the operator of the zk-rollup compresses into a
short proof. In existing prototypes, transaction rates of several hundred to
several thousand transactions per second in Ethereum (conventionally approx.
10 transactions per second) have allegedly been already achieved with the help
of zk-rollups Loopring, (2020), StarkWare, (2020). We want to estimate here
with a (heavily simplified) example what implications this might have on the
redundancy-related share of the power consumption of a large blockchain
network.

On the basis of the zk-rollup of Loopring on the Ethereum blockchain, which is
already in use, we can make a good estimate of the potential savings in the
ideal case, i.e. with maximum utilisation of the zk-rollups and exclusive
presence of transactions within the zk-rollup: For Loopring 3, the so-called
“gas” costs, which are a measure of the storage and computing effort of a
transaction in Ethereum and determine the pricing of the execution of
transactions, are estimated to be around 365 at maximum utilisation of 2 100
transactions per second Loopring, (2020). For comparison, a simple transaction
in Ethereum requires at least 21 000 gas, often significantly more.
Accordingly, this already means a reduction of the redundant operations and
thus their electricity demand per transaction by a factor of approx. 100 (an
exact proportionality of gas costs and calculation effort is not given, but is
nevertheless a reasonable approximation). On the other hand, the costs for the
computationally complex proof generation, which only needs to be conducted by
the operator of the zk-rollup, are estimated at 0.000042 USD per transaction
Loopring, (2020). At 2 100 transactions per second, this corresponds to an
amount of approx. 5 USD per minute and thus, for example, the operation of AWS
instances with approx. 96 vCPUs. For these, we can estimate a power
consumption of some hundred watts CPU World, (2020); Masanet et al., (2020),
which does not result in more than 0.5 J per transaction. This also
corresponds to the order of magnitude from an alternative estimation, which,
at least for ZKP-friendly hash functions, indicates an increased effort for
the generation of evidence by a factor of around 100 compared to a simple
calculation. On the other hand, own measurements with Ethereum-based
blockchains without PoW consensus mechanism result in a power consumption of
about 0.01 J per transaction (tx) and node from CPU usage. In a network of
approximately 10 000 nodes, which is roughly the same size as the Bitcoin and
Ethereum network today, we can therefore estimate a power consumption of
approximately 100 J/tx without zk-rollup. With zk-rollup, on the other hand,
for the redundant operations, i.e. primarily proof verification, one obtains
an amount of (100/100 + 0.5) J/tx and thus an energy saving of 98.5 %. For
even larger networks, the energy saving in our example would further increase,
whereas for smaller networks it would decrease and for networks with only very
few nodes it would even lead to an increase in power consumption.

However, in a holistic view, the idle power consumption of nodes must also be
taken into account. Depending on whether a separate computer is used or
remains switched on exclusively for participation in the blockchain, or
whether it is running anyway, or is located in a large cloud with a tendency
to have a comparatively low idle consumption, as well as depending on the
average load of the blockchain network, the idle consumption for large
networks in non-PoW blockchains can significantly exceed the amount of energy
consumed in connection with transactions or be negligible in comparison.
Further improvements in energy proportionality could lead to idle consumption
becoming less important in the future Shehabi et al., (2016); large data
centres are generally more advanced in this respect than small ones Ruiu et
al., (2017). Furthermore, a scenario in which all transactions are processed
within a zk-rollup is unrealistic. Nevertheless, it is conceivable that a
large part of transactions can be processed within such zk-rollups in the
future. Zk-rollups were primarily developed to solve scalability and
performance problems of blockchains. Nevertheless, as just described, they can
have the pleasant side effect of contributing to significant improvements in
power consumption for large blockchain networks. However, these are only
noticeable if no PoW-based consensus mechanism is already causing such high
power consumption that improvements in redundant operations are not noticeable
at all, and the idle consumption is negligible in absolute terms or in
relation to the power consumption from CPU usage per transaction (this
probably requires a considerable average load).

## Conclusion

The PoW blockchains and especially Bitcoin, which are currently used as the
basis for many cryptocurrencies, have - considering their current technical
performance - an enormous energy consumption. The total power consumption of
all these PoW cryptocurrencies is still mainly caused by Bitcoin and amounts
to between 20 – 50 % of the German power consumption, with a best guess for
Bitcoin at about 100 TWh/a or 20 % of the German power consumption. The
driving force behind the electricity consumption is the price of Bitcoin and
not the number of transactions, and if the economic environment remains the
same, the periodic halving of block rewards in many PoW-based cryptocurrencies
would in the long term lead to a significant reduction in electricity
consumption.

In addition, there are now established blockchains with alternative consensus
mechanisms, above all PoS for public permissionless cryptocurrencies and the
election-based consensus mechanisms of private permissioned blockchains. The
latter usually include solutions that are used, for example, in companies or
the public sector as a neutral platform across organisations. Due to the
elimination of PoW, their power consumption is in each case orders of
magnitude lower than that of Bitcoin and other PoW-based cryptocurrencies.
However, mainly due to the redundant calculations characteristic of blockchain
technology, their power consumption per transaction is roughly proportional to
the number of participating nodes and thus still several times higher than
that of central systems. Especially for large cryptocurrency networks, this
can still mean high power consumption for non-PoW blockchains. Through
technological further developments and modifications, with which the effort
for redundant calculations and data storage can be reduced, and in particular
zero knowledge proofs in zk-rollups, it is to be expected that in the future,
the power consumption of large networks can be reduced further. However, the
idle consumption must also always be taken into account when considering the
whole network.

Figure 5 summarises the findings of this paper by estimating typical power
consumption of different blockchain technologies or improvements described in
the previous sections. These are primarily for illustrative purposes and do
not take the idle consumption into account. The figures given should therefore
not yet be regarded as fixed and reliable, but only as an indication of the
orders of magnitude. In particular, the error estimates are in most cases not
generally reliable, as they correspond to empirical values from tests with
few, different systems. However, the orders of magnitude are reasonable
estimates based on the assumptions made in the respective chapters to the best
of the authors’ knowledge.

Figure 5: Rough comparison of the power consumption of different blockchain
architectures per transaction. Note the logarithmic scale on the y-axis.

Figure 5 illustrates that the power consumption of blockchains already in use
today is reduced by several orders of magnitude compared to the PoW
blockchains of the “first generation” or could be with technology available
today.

Although the use of blockchain technology may not be the most energy-efficient
solution from a purely technical point of view, what ultimately matters is the
energy savings that can be achieved in return. The potential in the cross-
sectoral and cross-organizational use of IT is particularly big Buhl et al.,
(2009). It is precisely here that many of the blockchain applications that go
beyond cryptocurrencies and are currently being developed or tested in
business and the public sector. For economic or political reasons, it is often
not possible to implement a central, digital platform in these scenarios. When
using the described, energy-efficient blockchain solutions, it can be assumed
that resources can be saved not only from a financial but also from an energy
point of view when automating processes.

Here it is the task of business and information systems research to identify
and quantify the existing potential for energy savings and climate protection
and – depending on the scenario with or without blockchain technology – to
exploit it with the most suitable technology. A prerequisite for this is that
the consumption of resources (e.g. through a CO2 tax) is priced in such a way
that no ecologically harmful distortions occur and that the economic incentive
mechanisms therefore promote the development of such solutions. In addition,
it should always be kept in mind that a solution which initially appears
expensive due to its high complexity and the still young technology, and which
overall reduces the consumption of resources by using modern solutions such as
blockchain, can also be a hedge against future price increases or fluctuations
Buhl et al., (2011).

In conclusion, we would therefore like to encourage further research both into
technical improvements of blockchain technology, for example in terms of
performance and energy efficiency, and into areas of application with
particularly high potential for energy savings.

###### Acknowledgements.

We would like to thank Peter Mertens for his valuable suggestions. We also
thank the Luxembourg National Research Fund (FNR) and PayPal for their support
of the PEARL project “P17/IS/13342933/PayPal-FNR/Chair in DFS/Gilbert
Fridgen”, which made this article possible in the first place.

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  *[PoW]: Proof-of-Work
  *[PoS]: Proof-of-Stake