Bitcoin's Growing Energy Problem

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DOI: 10.1016/j.joule.2018.04.016
Cite this publication
The electricity that is expended in the process of mining Bitcoin has become a topic of heavy debate over the past few years. It is a process that makes Bitcoin extremely energy-hungry by design, as the currency requires a huge amount of hash calculations for its ultimate goal of processing financial transactions without intermediaries (peer-to-peer). The primary fuel for each of these calculations is electricity. The Bitcoin network can be estimated to consume at least 2.55 gigawatts of electricity currently, and potentially 7.67 gigawatts in the future, making it comparable with countries such as Ireland (3.1 gigawatts) and Austria (8.2 gigawatts). Economic models tell us that Bitcoin’s electricity consumption will gravitate toward the latter number. A look at Bitcoin miner production estimates suggests that this number could already be reached in 2018.
Bitcoin’s Growing
Energy Problem
Alex de Vries
The electricity that is expended in the
process of mining Bitcoin has become
a topic of heavy debate over the past
few years. It is a process that makes
Bitcoin extremely energy-hungry by
design, as the currency requires a
huge amount of hash calculations for
its ultimate goal of processing financial
transactions without intermediaries
(peer-to-peer). The primary fuel for
each of these calculations is electricity.
to consume at least 2.55 gigawatts of
electricity currently, and potentially
7.67 gigawatts in the future, making
it comparable with countries such as
Ireland (3.1 gigawatts) and Austria
(8.2 gigawatts). Economic models tell
us that Bitcoin’s electricity consumption
will gravitate toward the latter number.
A look at Bitcoin miner production esti-
mates suggests that this number could
already be reached in 2018.
When Bitcoin was introduced in 2008,
Satoshi Nakamoto presented a solution
for the double-spending problem in
digital cash. As with any digital informa-
tion, a digital token may be reproduced
relatively easily. If this were to happen
in Bitcoin it would lead to inflation in
the digital currency and devalue it rela-
tive to other currencies. In turn, this
would compromise user trust in the cur-
Nakamoto’s solution involved
‘‘timestamping transactions by hashing
them into an ongoing chain of hash-
based proof-of-work.’’ The proof-of-
work was specifically said to involve
‘‘scanning for a value that when hashed,
such as with SHA-256, the hash begins
with a number of zero bits.’’
The num-
ber of attempts to find such a hash,
made every second, is what we call
the hashrate. Once a node finds a
hash that satisfies the required number
of zero bits, it transmits the block it was
working on to the rest of the network.
The other nodes in the network then ex-
press their acceptance by starting to
create the next block for the blockchain
using the hash of the accepted block.
The finder of the block is rewarded for
the efforts with a special transaction.
Creators of a block are currently al-
lowed to send 12.5 newly created coins
to an address of their choosing. This
is a fixed reward that halves every
four years (210,000 blocks). On top of
the fixed reward a variable amount of
transaction fees is received as well.
The reward provides an incentive to
participate in this type of network. The
more computational power one has,
the bigger the share of all distributed
rewards that go to that miner. To keep
the flow of rewards stable, the network
self-adjusts the difficulty of hash calcu-
lations, so new blocks are only created
once every 10 min on average. Naka-
moto compared the creation of new
coins in this way with gold mining
(hence the term Bitcoin mining), and
noted that ‘‘in our case, it’s CPU time
and electricity that is expended.’’
The electricity that is expended in the
process of mining Bitcoin has become
a topic of heavy debate over the past
Bitcoin extremely energy-hungry by
design, as the currency requires a
huge amount of hash calculations
for its ultimate goal of processing finan-
cial transactions without intermediaries
(peer-to-peer). We cannot observe this
hashrate directly, but it is possible to
derive this number from the observable
difficulty and the actual time required
to mine new blocks for the blockchain.
As per mid-March 2018, about 26 quin-
tillion hashing operations are per-
formed every second and non-stop by
the Bitcoin network (Figure 1). At the
same time, the Bitcoin network is only
processing 2–3 transactions per second
(around 200,000 transactions per day).
This means that the ratio of hash
calculations to processed transactions
is 8.7 quintillion to 1 at best. The pri-
mary fuel for each of these calculations
is electricity.
Network Power Usage
Trying to measure the electricity
consumed by the Bitcoin mining ma-
chines producing all those hash calcula-
tions remains a challenge to date. Even
though we can easily estimate the total
computational power of the Bitcoin
network, it provides only little informa-
tion on the underlying machines and
their respective power use. A hashrate
of 14 terahashes per second can either
come from a single Antminer S9 running
on just 1,372 W, or more than half a
million Playstation-3 devices running
on 40 MW (as a single Playstation-3 de-
vice has a hashrate of 21 megahashes
per second and a power use of 60 W).
It is also not possible to observe the
exact number of connected devices.
The Bitcoin network is estimated to
have around 10,000 connected nodes,
but a single node in the network can
represent either one or many machines.
Still, estimating the power consump-
tion of the Bitcoin network using the
Joule 2, 801–809, May 16, 2018 ª2018 Elsevier Inc. 801
efficiency for different hardware has
been a common approach for years.
In particular, the information on the to-
tal network computational power can
be used in determining a lower bound
for Bitcoin’s electricity consumption.
With publicly available Bitcoin mining
machines achieving advertised effi-
ciencies of 0.098 joule per gigahash
(Table 1), and the total Bitcoin network
producing 26 quintillion hashes per
second, we find that this lower bound
should be around 2.55 GW.
Cooling and Other Electricity Costs
Even though the previous approach is
very useful since it provides a minimum
level for Bitcoin’s electricity consump-
bare consumption estimate, first of all
because the network doesn’t contain
because it doesn’t take cooling require-
ments into account. A majority of the
total Bitcoin network hashrate origi-
nates from mining machines that
are clustered together in mining facil-
ities. This was observed in 2017 when
48 miners participated in a study by
Hileman and Rauchs. Eleven of these
were designated as large mining opera-
tions, and were estimated to contribute
to more than half of the global Bitcoin
network hashrate.
These facilities are
likely to have more power expendi-
tures. With each of the machines gener-
ating as much heat as a portable heater,
the additional electricity expenditure to
simply get rid of all this heat can poten-
tially be significant, depending on fac-
tors such as climate and chosen cooling
Mining facilities tend to keep their op-
erations behind closed doors, so little
is known about their power usage effec-
tiveness (PUE). Bitfury claims to have
built a data center that achieves a
PUE of 1.02 with the help of immersion
cooling, but this has not been inde-
pendently verified. Certainly not every
mining operation uses this cooling
technology. For example, Bitmain’s
mining facility in the Inner Mongolian
desert (China) makes use of an evapora-
tive cooling system. This was shown by
a small group of journalists who were
granted access to this facility in the
fall of 2017, which was responsible
for about 4%
of the Bitcoin network
hashrate at the time (6 exahashes per
second). Unfortunately, they produced
conflicting reports regarding the
facility’s exact electricity use. Quartz
reported the facility was running on
40 MW,
while Tech in Asia reported
was reported that the facility was using
21,000 Bitcoin mining machines, which
were ‘‘almost exclusively’’ Antminer S9
Along with 4,000 L3+ (Lite-
coin) mining machines (running at
energy use of around 32 MW, suggest-
ing a worst-case PUE of 1.25. In any
case, this facility would only be repre-
sentative of less than 1% of the global
network hashrate today. For the major-
available at all. At this time, it therefore
cannot be ruled out that hashrate
simply does not reflect a large part
of the electricity consumed in Bitcoin
Expected Electricity Consumption
Hashrate-based approaches also offer
no insight in future electricity consump-
tion. To obtain an idea about this, we
instead can approach Bitcoin’s elec-
tricity consumption from an economic
angle. Doing so is possible because
Bitcoin can be considered a ‘‘virtual
commodity with a competitive market
of producers,’’
as asserted by Adam
Hayes. In his paper Hayes explains
that, if this is true, we expect that
‘‘miners will produce [hash calculations]
until their marginal costs equal their
marginal product.’’ The marginal prod-
uct of mining (‘‘the number of Bitcoins
found per day on average multiplied
by the dollar price of Bitcoin’’) can be
observed from the Bitcoin blockchain,
as it includes information on which
blocks have been mined at what time,
as well as the included reward for
each. On March 16, 2018, this marginal
product was equal to US$15.34 million.
We find this number based on an
average price of $8,351 times 1,837
coins (12.5 coins per block every
fees for the full day).
The marginal costs of mining are ex-
pected to tend to the latter amount,
as rational agents would undertake
mining while the marginal costs are
Figure 1. The Estimated Number of Terahashes per Second (Trillions of Hashes per Second)
Performed by the Bitcoin Network
802 Joule 2, 801–809, May 16, 2018
lower. At the same time, they would
presumably decide to remove them-
selves from the mining pool if they
would be operating at a marginal loss.
These market forces drive the industry
toward an equilibrium whereby firms
will earn zero economic profit.
The next step is to determine the struc-
ture of these marginal costs in equilib-
rium. Hayes argues that these are pri-
marily made up of electricity costs, as
hardware costs and small costs (such
as maintenance) can be ignored. The
reason Hayes ignores hardware costs
is that these represent a sunk cost
component in each unit of mining
effort, which are therefore not relevant
in the decision to mine (only prospec-
tive costs are). Although true, this
seems to be something of an oversim-
plification, as the acquisition of new
machines will always be considered
in the long run. This could be the result
of increasing revenues, or simply
because machines reach the end of
their technical lifetime.
To be able to take hardware costs into
account, we first need to put a figure
on it. We know that, in equilibrium,
not even Bitmain (the largest manufac-
turer of new Bitcoin mining machines
with a claimed market share of 70%
should be able to generate a profit.
However, we don’t know much about
the costs of hardware other than that
the retail price of an Antminer S9 is
currently around $1,900 per machine
(after peaking above $2,700 per ma-
chine at the end of December 2017),
and of course the retail price doesn’t
equal the production cost. Bitmain’s
profit margins, however, are not pro-
vided by the company. We do know
that Bitmain is able to sell the S9 at a
retail price of $1,200 per machine
and still turn in a profit, as this was
indicative of the retail price of an S9
machine for most of 2017 (April
through October). Since the retail
$1,161 per machine at the start of
June 2017, we can at least take this
as an upper bound for the production
In April 2017, an attempt to figure
out Bitmain’s profitability was made by
Bitcoin developer and entrepreneur
Jimmy Song, who looked into the pro-
duction cost of an Antminer S9 to this
purpose. Song concluded that the
production cost of an Antminer S9 was
‘‘roughly $500.’’ To reach this figure,
Song first motivates how Bitmain is
likely paying its supplier Taiwan Semi-
conductor Manufacturing Company
(TSMC) about $8,000 per wafer of
TSMC’s 16-nm process, from which it
can get 5,158 chips. Given that each
S9 requires 189 chips, each wafer can
make enough chips for a little over
27 machines. This results in almost
$300 worth of chips per S9. Song adds
that chip fabrication is ‘‘generally the
most expensive part of the miner
build,’’ and uses expert judgment to
arrive at the remaining production costs
at $200.
On a retail price of $1,161 this
would still imply a profit margin of
56.9%. Such a margin is not uncommon
for Bitmain, which had a profit margin of
50% on the earlier Antminer S5 model
according to company co-founder Mi-
cree Zhan.
To calculate how these production
costs compare with electricity costs it
is a prerequisite to establish the ex-
pected lifetime of an Antminer ma-
chine. The longer the expected life-
time, the bigger the share of
electricity costs in the total lifetime
costs will be. Knowing that the Ant-
miner S9 was first sold mid-2016 and
remains one of Bitmain’s primary
products almost 2 years later, we
will consider a lifetime of up to 2 years.
The costs of electricity are assumed
to be 5 US cents per kWh on
average, which is a conservative
pick based on the knowledge that
Bitmain was already paying just
4 cents per kWh for its facility in Inner
When combining the resulting elec-
tricity costs over a 2-year period with
the production costs from before, we
find that electricity costs make up a lit-
tle more than 70% of the total lifetime
costs of an Antminer S9. Using stricter
lifetime assumptions (Table 2), elec-
tricity costs continue to make up the
majority of the machine’s total lifetime
costs, so henceforth we will assume an
electricity cost share of 60%. We can
subsequently use this number to
obtain a ballpark estimate for the elec-
tricity consumption of the Bitcoin
network in an equilibrium where not
even Bitmain is capable of earning a
profit. Assuming an electricity price of
5 cents per kWh, and 60% of the mar-
ginal product ($15.34 million) going to
electricity in equilibrium, we would
Table 1. Examples of Recent Bitcoin ASIC Miner Machine Types
Machine Hashrate (TH/s) Power Use (W) Power Efficiency (J/GH)
Antminer S9 14 1,372 0.098
Antminer T9 12.5 1,576 0.126
Antminer T9+ 10.5 1,332 0.127
Antminer V9 4 1,027 0.257
Antminer S7 4.73 1,293 0.273
AvalonMiner 821 11 1,200 0.109
AvalonMiner 761 8.8 1,320 0.150
AvalonMiner 741 7.3 1,150 0.160
Bitfury B8 Black 55 5,600 0.11
Bitfury B8 47 6,400 0.13
Source: Bitmain, Bitfury, and Canaan.
Joule 2, 801–809, May 16, 2018 803
thus expect a total electricity con-
sumption of 7.67 GW.
Thanks to its simplicity, the aforemen-
tioned approach became the founda-
tion of the Bitcoin Energy Consumption
but knowing where Bitcoin’s
electricity consumption is heading
does not provide us a with final esti-
mate for the network’s current con-
sumption. It is important to note that
the index assumes it may currently
take around a year before the expected
electricity consumption is actually
reached. Especially after strong price
increases, one needs to allow for a suf-
ficient amount of time for the produc-
tion of new hardware.
Bitcoin Miner Production
Bitcoin miner manufacturers tend to be
very secretive about their production
output, but Morgan Stanley managed
to work around this problem by looking
at the TSMC instead. TSMC supplies
the chips for Bitmain, making it possible
to come up with a chip-based produc-
tion potential. Specifically, Morgan
Stanley estimated that TSMC had or-
ders from Bitmain ‘‘for 15–20k wafer-
starts per month’’ for the first quarter
of 2018. This figure was independently
confirmed by Ark Invest analyst James
Wang, who wrote that ‘‘Bitmain is
buying 20k 16 nm wafers a month’’
specifically (used for building the Ant-
miner S9 and T9).
With each 16-nm wafer capable of sup-
plying chips for about ‘‘27–30 Bitcoin
mining rigs’’, Bitmain could produce
around half a million of its most efficient
Bitcoin mining machines per month.
Assuming 20,000 wafers per month
and 27 machines per wafer, and given
that these production rates are main-
tained throughout the year, Bitmain
could produce up to 6.5 million
Antminer S9 machines in 2018. These
machines would have a combined elec-
tricity consumption of 8.92 GW. This
exceeds the expected electricity con-
sumption of 7.67 GW from before,
which therefore seems to be within
Bitmain’s production potential for
2018. It is worth noting that these ma-
chines might not all be finalized and
delivered in 2018, but at the same time
Bitmain, with a claimed market share
of 70%,
is not the only contributor to
the industry’s total production potential
this year.
The aforementioned also marks the
first time that Bitcoin miner production
has been estimated with the help of
upstream (chip) production numbers.
Given the ongoing secrecy of Bitcoin
miner manufacturers, this could prove
to be a valuable addition to the toolkit
for substantiating trends in Bitcoin’s
electricity consumption.
Lastly, it is important to keep in mind
that all of the methods discussed as-
sume rational agents. There may be
various reasons for an agent to mine
even when this isn’t profitable, and in
some cases costs may not play a role
at all when machines and/or electricity
researcher misused National Science
Foundation-funded supercomputers to
mine $8,000–$10,000 worth of Bitcoin.
The operation ended up costing the
university $150,000.
More recently,
a mining facility in Russia (with 6,000
devices) was shut down after ‘‘not
paying for several million kilowatt-
hours of electricity.’’
Less malicious reasons for an agent
to mine Bitcoin at a loss might include
motivations such as being able to
obtain Bitcoin completely anony-
mously, libertarian ideology (support-
ing a payment network that does not
rely on a central authority), or specula-
tive reasons. None of these situations
would be properly captured by any of
the discussed methods.
This paper has outlined various
methods that are currently used in
determining the current and future elec-
tricity consumption of the Bitcoin
network. These methods tell us that
the Bitcoin network consumes at least
2.55 GW of electricity currently, and
that it could reach a consumption of
7.67 GW in the future, making it compa-
rable with countries such as Ireland
(3.1 GW) and Austria (8.2 GW).
tionally, economic models tell us that
Bitcoin’s electricity consumption will
gravitate toward the latter figure. A
look at Bitcoin miner production esti-
mates suggests that this figure could
already be reached in 2018. With the
Bitcoin network processing just
200,000 transactions per day, this
means that the average electricity
consumed per transaction equals at
least 300 kWh, and could exceed
900 kWh per transaction by the end of
2018. The Bitcoin development com-
munity is experimenting with solutions
such as the Lightning Network to
improve the throughput of the network,
which may alleviate the situation. For
Table 2. Estimated Lifetime Costs for an Antminer S9 under Various Lifetime Assumptions and a ProductionCostofUS$500(AssumingElectricity
Costs 5 US Cents per Kilowatt-Hour)
Machine Expected Lifetime
Estimated Production
Costs (US$)
Lifetime Electricity
Use (kWh)
Lifetime Electricity
Costs (US$)
Total Lifetime
Costs (US$)
Electricity Costs/Total
Costs (%)
Antminer S9 2 500 24,037 1,202 1,702 70.6
Antminer S9 1.5 500 18,028 901 1,401 64.3
Antminer S9 1 500 12,019 601 1,101 54.6
804 Joule 2, 801–809, May 16, 2018
now, however, Bitcoin has a big prob-
lem, and it is growing fast.
1. Chohan, U. The double spending problem
and cryptocurrencies. 2017.
2. Nakamoto, S. Bitcoin: a peer-to-peer
electronic cas h system. 2008. https://bitcoin.
3. Hileman, G., and Rauchs, M. Global
cryptocurrency benchmarking study. 2017.
4. IEEE Spectrum. Why the biggest Bitcoin
mines are in China. 2017. https://spectrum.
5. Huang, Z., and Wong, J.I. The lives of Bitcoin
miners digging for digital gold in Inner
Mongolia. 2017.
6. Tech in Asia. Cheap electricity made
China the king of Bitcoin mining. The
government’s stepping in. 2017. https://
7. Hayes, A.S. (2017). Cryptocurrency value
formation: An empirical study leading to a
cost of producti on model for valuing bitcoin.
Telematics and Informatics 34 , 1308–1321.
8. Quartz. China’s Bitmain dominates Bitcoin
mining. Now it wants to cash in on artificial
intelligence. 2017.
9. Song, J. Just how profitable is Bitmain? 2017.
10. Digiconomist. Bitcoin Energy Consumption
Index. 2018. http://
11. Wang, J. TSMC—the world’s largest
chip factory—is all about crypto all of
a sudden. Bitmain is buying 20k
16nm wafers a month. That’s more than
Nvidia. Twitter Post. 2018. https://twitter.
12. Morgan Stanley. Bitcoin ASIC production
substantiates electricity use; points to
coming jump. 2018.
13. Tahir, R., Huzaifa, M., Das, A., Ahmad, M.,
Gunter, C., Zaffar, F., Caesar, M., and
Borisov, N. (201 7). Mining on someone else’s
dime: mitigating covert mining operations in
clouds and enterprises. Res Attacks
Intrusions Def, 287–310.
14. Cointelegraph. Crypto farm with6000 miners
shut down in Russia for overdue electricity
bill. 2018.
15. International Energy Agency. World Energy
Statistics 2017. 2017.
publications/ freepublications/publication/
Experience Center of PwC, Amsterdam, the
Opportunities for
Utilization and
Negative Emissions
at the Gigatonne
Arun Majumdar
and John Deutch
Arun Majumdar is the Jay Precourt Pro-
fessor in the Department of Mechanical
Engineering and Co-director of the Pre-
court Institute for Energy at Stanford
University. He served as the Founding
Director (2009–2012) of the Advanced
Research Projects Agency – Energy
(ARPA-E) and the Acting Undersecre-
tary for Energy (2011–2012) under Sec-
retary Steven Chu in the US Department
of Energy. He also served as Vice Chair
of the Advisory Board (2014–2017) to
Secretary Ernest Moniz. Arun Majumdar
has published widely on the science
and engineering of conversion, stor-
age, and transport of energy, especially
in nanostructured materials and
devices. He is a member of the US
National Academy of Engineering
and the American Academy of Arts
and Science.
John Deutch is an emeritus Institute
Professor at the Massachusetts Insti-
tute of Technology where he has
been a member of the faculty since
1970. He has served as Chairman of
the Department of Chemistry, Dean
of Science, and Provost. In the Carter
Administration, he served as Director
of Energy Research (1977–1979),
Acting Assistant Secretary for Energy
Technology (1979), and Undersecre-
tary (1979–1980) in the US Department
of Energy. He has been a member of
the President’s Nuclear Safety Over-
sight Committee (1980–1981), the
White House Science Council (1985–
1989), the President’s Committee of
Advisors on Science and Technology
(1997–2001), and the Secretary of the
Energy Advisory Board (2008–2016).
Joule 2, 801–809, May 16, 2018 ª2018 Elsevier Inc. 805
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    With the advent of revolutionary technologies, such as virtualization and softwarization, a novel concept for 5G networks and beyond has been unveiled: Network Slicing. Initially driven by the research community, standardization bodies as 3GPP have embraced it as a promising solution to revolutionize the traditional mobile telecommunication market by enabling new business models opportunities. Network Slicing is envisioned to open up the telecom market to new players such as Industry Verticals, e.g. automotive, smart factories, e-health, etc. Given the large number of potential new business players, dubbed as network tenants, novel solutions are required to accommodate their needs in a cost-efficient and secure manner. In this paper, we propose NSBchain, a novel network slicing brokering (NSB) solution, which leverages on the widely adopted Blockchain technology to address the new business models needs beyond traditional network sharing agreements. NSBchain defines a new entity, the Intermediate Broker (IB), which enables Infrastructure Providers (InPs) to allocate network resources to IBs through smart contracts and IBs to assign and redistribute their resources among tenants in a secure, automated and scalable manner. We conducted an extensive performance evaluation by means of an open-source blockchain platform that proves the feasibility of our proposed framework considering a large number of tenants and two different consensus algorithms.
  • ... Energy communities aim to improve local energy efficiency, yet blockchains are in fact not known for providing an energy efficient operation (de Vries, 2018;Vranken, 2017). It is estimated that the cumulative energy consumption of blockchains already exceeds the energy consumption of medium-sized countries. ...
    Conference Paper
    The European Union's Clean Energy Package introduces two kinds of energy communities, namely the Renewable Energy Community (REC) in the Renewable Energy Directive of 2018 and the Citizen Energy Community (CEC) in the Electricity Directive of 2019. They aim for local improvements of energy efficiency, increasing integration of renewable energy sources, and a reduction of greenhouse gas emissions, to be achieved by jointly producing, temporarily storing, sharing, consuming, and selling locally generated energy. Households and individuals shall thus be enabled to take an active part in the energy transition. When utilizing blockchain technology for the implementation of such energy communities, as proposed in current research projects, a focus must be laid on the technology-inherent area of conflict with privacy issues, especially since data on households' energy consumption count as personal data.
  • Chapter
    Blockchain is fundamentally a distributed database and open source where anyone can change the underlying code and see the current status of an operation. It is actually a peer‐to‐peer network. Think of it as a massive global database that runs on zillions and zillions of computers. It doesn't require any controlling intermediaries to authenticate the transactions. Cryptocurrency and blockchain technology are the most disruptive technologies of the contemporary e‐era. The scope of blockchain has the potential to disrupt key barriers to efficiency, commitment and scaling. It could record any structured information end to end. In the case of settling trillions of real‐time transactions in banks, blockchain extensively supports settlement systems. The aim of this chapter is to provide concrete knowledge about cryptocurrency, the state‐of‐the‐art of cryptography, blockchain and distributed systems, and to emphasize the synthetic sketch of environmental issues raised by the development of new disruptive technologies. There are key challenges to be focused on if the blockchain is to work in the environmental sector; for example, blockchain systems, especially the use of cryptocurrencies, are enormously energy‐intensive. Moreover, there are many natural resources and environmental services considerations that are relevant to low‐ and middle‐income countries while engaging these in a global framework.
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    Full-text available
    The present work investigates the impact on financial intermediation of distributed ledger technology (DLT), which is usually associated with the blockchain technology and is at the base of the cryptocurrencies' development. “Bitcoin” is the expression of its main application since it was the first new currency that gained popularity some years after its release date and it is still the major cryptocurrency in the market. For this reason, the present analysis is focused on studying its price determination, which seems to be still almost unpredictable. We carry out an empirical analysis based on a cost of production model, trying to detect whether the Bitcoin price could be justified by and connected to the profits and costs associated with the mining effort. We construct a sample model, composed of the hardware devices employed in the mining process. After collecting the technical information required and computing a cost and a profit function for each period, an implied price for the Bitcoin value is derived. The interconnection between this price and the historical one is analyzed, adopting a Vector Autoregression (VAR) model. Our main results put on evidence that there aren't ultimate drivers for Bitcoin price; probably many factors should be expressed and studied at the same time, taking into account their variability and different relevance over time. It seems that the historical price fluctuated around the model (or implied) price until 2017, when the Bitcoin price significantly increased. During the last months of 2018, the prices seem to converge again, following a common path. In detail, we focus on the time window in which Bitcoin experienced its higher price volatility; the results suggest that it is disconnected from the one predicted by the model. These findings may depend on the particular features of the new cryptocurrencies, which have not been completely understood yet. In our opinion, there is not enough knowledge on cryptocurrencies to assert that Bitcoin price is (or is not) based on the profit and cost derived by the mining process, but these intrinsic characteristics must be considered, including other possible Bitcoin price drivers.
  • The lives of Bitcoin miners digging for digital gold in Inner Mongolia
    • Z Huang
    • J I Wong
    Huang, Z., and Wong, J.I. The lives of Bitcoin miners digging for digital gold in Inner Mongolia. 2017. what-its-like-working-at-a-sprawlingbitcoin-mine-in-inner-mongolia/.
  • Cheap electricity made China the king of Bitcoin mining. The government's stepping in
    • Asia Tech In
    Tech in Asia. Cheap electricity made China the king of Bitcoin mining. The government's stepping in. 2017. https://
  • China's Bitmain dominates Bitcoin mining. Now it wants to cash in on artificial intelligence
    • Quartz
    Quartz. China's Bitmain dominates Bitcoin mining. Now it wants to cash in on artificial intelligence. 2017. chinas-bitmain-dominates-bitcoin-miningnow-it-wants-to-cash-in-on-artificialintelligence/.
  • Just how profitable is Bitmain?
    • J Song
    Song, J. Just how profitable is Bitmain? 2017.
  • Bitcoin Energy Consumption Index
    • Digiconomist
    Digiconomist. Bitcoin Energy Consumption Index. 2018. http://
  • Conference Paper
    Covert cryptocurrency mining operations are causing notable losses to both cloud providers and enterprises. Increased power consumption resulting from constant CPU and GPU usage from mining, inflated cooling and electricity costs, and wastage of resources that could otherwise benefit legitimate users are some of the factors that contribute to these incurred losses. Affected organizations currently have no way of detecting these covert, and at times illegal miners and often discover the abuse when attackers have already fled and the damage is done.
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    Full-text available
    This paper aims to identify the likely determinants for cryptocurrency value formation, including for that of bitcoin. Due to Bitcoin’s growing popular appeal and merchant acceptance, it has become increasingly important to try to understand the factors that influence its value formation. Presently, the value of all Bitcoins in existence represent approximately $7 billion, and more than $60 million of notional value changes hands each day. Having grown rapidly over the past few years, there is now a developing but vibrant marketplace for bitcoin, and a recognition of digital currencies as an emerging asset class. Not only is there a listed and over-the-counter market for bitcoin and other digital currencies, but also an emergent derivatives market. As such, the ability to value bitcoin and related cryptocurrencies is becoming critical to its establishment as a legitimate financial asset.
  • Article
    A purely peer-to-peer version of electronic cash would allow online payments to be sent directly from one party to another without going through a financial institution. Digital signatures provide part of the solution, but the main benefits are lost if a trusted third party is still required to prevent double-spending. We propose a solution to the double-spending problem using a peer-to-peer network. The network timestamps transactions by hashing them into an ongoing chain of hash-based proof-of-work, forming a record that cannot be changed without redoing the proof-of-work. The longest chain not only serves as proof of the sequence of events witnessed, but proof that it came from the largest pool of CPU power. As long as a majority of CPU power is controlled by nodes that are not cooperating to attack the network, they'll generate the longest chain and outpace attackers. The network itself requires minimal structure. Messages are broadcast on a best effort basis, and nodes can leave and rejoin the network at will, accepting the longest proof-of-work chain as proof of what happened while they were gone.