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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.
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Commentary:
Bitcoin’s Growing Energy Problem
Alex de Vries,
1
,
2
,*
Joule 2, 801809, May 16, 2018. DOI: https://doi.org/10.1016/j.joule.2018.04.016
Abstract
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.
1
School of Business and Economics, Vrije Universiteit Amsterdam, The Netherlands
2
Founder of Digiconomist, Almere, The Netherlands
*Correspondence: alex@digiconomist.net
Introduction
When Bitcoin was introduced in 2008, Satoshi Nakamoto presented a solution for the double-spending
problem in digital cash. As with any digital information, 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
relative to other currencies. In turn, this would compromise user trust in the currency.[1] 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.[2] The number 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 express 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 allowed 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 calculations, so new blocks are only created once every 10
minutes on average. Nakamoto 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 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). 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 quintillion hashing operations are
performed 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
primary fuel for each of these calculations is electricity.
Figure 1: The estimated number of tera hashes per second (trillions of hashes per second) the Bitcoin network is performing.
(Source: blockchain.info)
0
5000000
10000000
15000000
20000000
25000000
30000000
TH/s
Estimated Bitcoin Network Hashrate
Network Power Usage
Trying to measure the electricity consumed by the Bitcoin mining machines producing all those hash
calculations remains a challenge to date. Even though we can easily estimate the total computational
power of the Bitcoin network, it provides only little information 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 1372 W, or more than half a million Playstation 3 devices running on 40 MW (as a
single Playstation 3 device 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 consumption of the Bitcoin network using the efficiency for different
hardware has been a common approach for years. In particular, the information on the total network
computational power can be used in determining a lower bound for Bitcoin’s electricity consumption.
With publicly available Bitcoin mining machines achieving advertised efficiencies 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.
Table 1: Examples of recent Bitcoin ASIC miner machine types. (Source: Bitmain, Bitfury & Canaan)
Machine
Hashrate
Power Use
Power Efficiency
14 TH/s
1372W
0.098 J/GH
12.5 TH/s
1576W
0.126 J/GH
10.5 TH/s
1332W
0.127 J/GH
4 TH/s
1027W
0.257 J/GH
4.73 TH/s
1293W
0.273 J/GH
11 TH/s
1200W
0.109 J/GH
8.8 TH/s
1320W
0.150 J/GH
7.3 TH/s
1150W
0.160 J/GH
55 TH/s
5600W
0.11 J/GH
47 TH/s
6400W
0.13 J/GH
Cooling and Other Electricity Costs
Even though the previous approach is very useful since it provides a minimum level for Bitcoin’s
electricity consumption, it always leaves us with a very bare consumption estimate, first of all because
the network doesn’t contain a single type of machine, but also because it doesn’t take cooling
requirements into account. A majority of the total Bitcoin network hashrate originates from mining
machines that are clustered together in mining facilities. This was observed in 2017 when 48 miners
participated in a study by Hileman & Rauchs. Eleven of these were designated as large mining
operations, and were estimated to contribute to more than half of the global Bitcoin network hashrate.[3]
These facilities are likely to have more power expenditures. With each of the machines generating as
much heat as a portable heater, the additional electricity expenditure to simply get rid of all this heat can
potentially be significant, depending on factors such as climate and chosen cooling technology.
Mining facilities tend to keep their operations behind closed doors, so little is known about their power
usage effectiveness (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 independently 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 evaporative 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%[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[5], while Tech in Asia reported on 33.33 MW (800 MWh per day).[6] It was
reported that the facility was using 21,000 Bitcoin mining machines, which were “almost exclusively
Antminer S9 machines. [4] Along with 4,000 L3+ (Litecoin) mining machines (running at 800 W each)
we would expect a total energy use of around 32 MW, suggesting a worst-case PUE of 1.25. In any case,
this facility would only be representative of less than 1% of the global network hashrate today. For the
majority of the network no information is 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 mining.
Expected Electricity Consumption
Hashrate-based approaches also offer no insight in future electricity consumption. To obtain an idea
about this, we instead can approach Bitcoin’s electricity 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.[7] 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 product 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 10 minutes on average plus 37 coins in
fees for the full day).
The marginal costs of mining are expected to tend to the latter amount, as rational agents would
undertake mining while the marginal costs are lower. At the same time, they would presumably decide
to remove themselves 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 structure of these marginal costs in equilibrium. Hayes argues that
these are primarily 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 prospective
costs are). Although true, this seems to be something of an oversimplification, 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 manufacturer of new Bitcoin mining machines with a claimed
market share of 70%[8]), 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 machine 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 provided 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 price of an S9 bottomed out at $1,161 per machine at the start of June 2017,
we can at least take this as an upper bound for the production costs.
In April 2017, an attempt to figure out Bitmain’s profitability was made by Bitcoin developer and
entrepreneur Jimmy Song, who looked into the production 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 Semiconductor 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.[9] 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 Micree Zhan.[8]
To calculate how these production costs compare with electricity costs it is a prerequisite to establish
the expected lifetime of an Antminer machine. The longer the expected lifetime, the bigger the share of
electricity costs in the total lifetime costs will be. Knowing that the Antminer 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 US cents per kWh for
its facility in Inner Mongolia.[4],[6]
When combining the resulting electricity costs over a 2 year period with the production costs from
before, we find that electricity costs make up a little more than 70% of the total lifetime costs of an
Antminer S9. Using stricter lifetime assumptions (Table 2) electricity 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 electricity consumption
of the Bitcoin network in an equilibrium where not even Bitmain is capable of earning a profit. Assuming
an electricity price of 5 US cents per kWh, and 60% of the marginal product ($15.34 million) going to
electricity in equilibrium, we would thus expect a total electricity consumption of 7.67 GW.
Table 2: Estimated lifetime costs for an Antminer S9 under various lifetime assumptions and a production cost of US$500
(assuming electricity costs 5 US cents per kilowatt-hour)
Machine
Expected
Lifetime
(years)
Estimated
Production
Costs
Lifetime
Electricity
Use
Lifetime
Electricity
Costs
Total
LIFETIME
Costs
Electricity
Costs/Total
Costs
Antminer
S9
2
$500
24,037
KWh
$1,202
$1,702
70.6%
Antminer
S9
1.5
$500
18,028
KWh
$901
$1,401
64.3%
Antminer
S9
1
$500
12,019
KWh
$601
$1,101
54.6%
Thanks to its simplicity, the aforementioned approach became the foundation of the Bitcoin Energy
Consumption Index[10], but knowing where Bitcoin’s electricity consumption is heading does not
provide us with a final estimate for the network’s current consumption. 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 sufficient amount of time for
the production 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 TSMC instead. TSMC supplies the chips for
Bitmain, making it possible to come up with a chip-based production potential. Specifically, Morgan
Stanley estimated that TSMC had orders 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 16nm wafers a month specifically (used for building the Antminer S9
and T9).[11]
With each 16-nm wafer capable of supplying chips for about “27-30 Bitcoin mining rigs”, Bitmain could
produce around half a million of its most efficient Bitcoin mining machines per month.[12] Assuming
20,000 wafers per month and 27 machines per wafer, and given that these production rates are
maintained throughout the year, Bitmain could produce up to 6.5 million Antminer S9 machines in 2018.
These machines would have a combined electricity consumption of 8.92 GW. This exceeds the expected
electricity consumption of 7.67 GW from before, which therefore seems to be within Bitmain’s
production potential for 2018. It is worth noting that these machines might not all be finalized and
delivered in 2018, but at the same time Bitmain, with a claimed market share of 70% [8], 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.
Limitations
Lastly, it is important to keep in mind that all of the methods discussed assume 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 are stolen or abused. In one case a 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.[13] More recently, a mining facility in Russia
(with 6,000 devices) was shut down after “not paying for several million kilowatt-hours of
electricity”.[14]
Less malicious reasons for an agent to mine Bitcoin at a loss might include motivations such as being
able to obtain Bitcoin completely anonymously, libertarian ideology (supporting a payment network that
does not rely on a central authority), or speculative reasons. None of these situations would be properly
captured by any of the discussed methods.
Conclusion
This paper has outlined various methods that are currently used in determining the current and future
electricity 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 comparable with countries such as Ireland (3.1 GW) and Austria (8.2 GW).[15] Additionally,
economic models tell us that Bitcoin’s electricity consumption will gravitate toward the latter figure. A
look at Bitcoin miner production estimates 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 community is experimenting with solutions such as the
Lightning Network to improve the throughput of the network, which may alleviate the situation. But for
now, Bitcoin has a big problem, and it’s growing fast.
References
[1] Chohan, U. The double spending problem and cryptocurrencies. 2017.
https://doi.org/10.2139/ssrn.3090174.
[2] Nakamoto, S. Bitcoin: a peer-to-peer electronic cash system. 2008. https://Bitcoin.org/Bitcoin.pdf.
[3] Hileman, G., and Rauchs, M. Global cryptocurrency benchmarking study. 2017.
https://doi.org/10.2139/ssrn.2965436.
[4] IEEE Spectrum. Why the biggest Bitcoin mines are in China. 2017.
https://spectrum.ieee.org/computing/networks/why-the-biggest-Bitcoin-mines-are-in-china.
[5] Huang, Z., and Wong, J.I. The lives of Bitcoin miners digging for digital gold in Inner Mongolia.
2017. https://qz.com/1054805/what-its-like-working-at-a-sprawling-Bitcoin-mine-in-inner-mongolia/.
[6] Tech in Asia. Cheap electricity made China the king of Bitcoin mining. The government’s stepping
in. 2017. https://www.techinasia.com/inner-mongolia-Bitcoin-mine.
[7] A.S. Hayes. Cryptocurrency value formation: An empirical study leading to a cost of production
model for valuing Bitcoin. Telematics and Informatics, 34 (2017), pp. 1308-1321
[8] Quartz. China’s Bitmain dominates Bitcoin mining. Now it wants to cash in on artificial
intelligence. 2017. https://qz.com/1053799/chinas-bitmain-dominates-Bitcoin-mining-now-it-wants-
to-cash-in-on-artificial-intelligence/.
[9] Song, J. Just how profitable is Bitmain? 2017. https://medium.com/@jimmysong/just-how-
profitable-is-bitmain-a9df82c761a.
[10] Digiconomist. Bitcoin Energy Consumption Index. 2018. http://Bitcoinenergyconsumption.com.
[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.com/jwangARK/status/954429531678543872.
[12] Morgan Stanley. Bitcoin ASIC production substantiates electricity use; points to coming jump.
2018. https://ny.matrix.ms.com/eqr/article/webapp/b974be48-effb-11e7-8cdb-6e28b48ebbd3.
[13] R. Tahir, M. Huzaifa, A. Das, M. Ahmad, C. Gunter, F. Zaffar, M. Caesar, N. Borisov
Mining on someone else’s dime: mitigating covert mining operations in clouds and enterprises
Res Attacks Intrusions Def (2017), pp. 287-310
[14] Cointelegraph. Crypto farm with 6000 miners shut down in Russia for overdue electricity bill.
2018. https://cointelegraph.com/news/crypto-farm-with-6000-miners-shut-down-in-russia-for-
overdue-electricity-bill.
[15] International Energy Agency. World Energy Statistics 2017. 2017.
https://www.iea.org/publications/freepublications/publication/KeyWorld2017.pdf.
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... A nem megfelelően kontrollált digitalizációs folyamatok például a politikai, jogi vagy pénzügyi rendszerek alapvető megzavarásához vezethetnek (Schulz & Feist, 2021). Más szerzők pedig arra hívják fel a figyelmet, hogy a digitalizáció negatívan befolyásolhatja a fenntarthatósági célok megvalósítását, a digitális technológiák széles körű alkalmazásához szükséges megnövekedett energiaigény miatt (de Vries, 2018;Mora et al., 2018). ...
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A klímavédelmi törekvésekhez kapcsolódóan napjainkban egyre nagyobb figyelmet kapnak az ún. zöld pénzügyek, amelyek elsődleges célja a fenntarthatósággal összefüggő célkitűzések, projektek finanszírozása. A zöld pénzügyek elterjedését azonban számos tényező gátolhatja. Az egyre intenzívebb digitális transzformáció és az új technológiák nagymértékben támogathatják a zöld pénzügyek fejlődését. Jelen cikkben a szerző két fintech – zöld robottanácsadás, zöld közösségi finanszírozás – és két blokklánc alapú megoldást – felhasználók közötti finanszírozási és befektetési, valamint felhasználók közötti kereskedési platformok – mutat be, amelyek segíthetik a zöld finanszírozás minél szélesebb körben történő elterjedését. A vizsgált megoldásokról összességében elmondható, hogy megkönnyítik a pénzügyi forrásokhoz való hozzáférést, bővíthetik a befektetői bázist – beleértve a kisbefektetőket és a magántőkét –, valamint új finanszírozási mechanizmusokat kínálhatnak a zöld projektek ötletgazdái számára. A cikk feltárja azt is, hogy az ismertetett megoldások a zöld pénzügyek elterjedését gátló tényezők közül főként a pénzügyi akadályok mérséklésében nyújthatnak segítséget.
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Public Key Infrastructure (PKI) is the most widely accepted cryptography protocol to enable secure communication over the web. PKI comprises digital certificates managed by the certificate authorities (CAs) to verify the user’s identity, thus providing secure communication channels. However, the security of PKI is profoundly reliant on the reliability of these third-party CAs, which serves as a single point of failure for PKI. Over the past, there have been several incidents of popular CA breaches, where the centralized operation model of CAs caused numerous targeted attacks due to the spread of rogue certificates. In this paper, we aim to make the CA pool completely decentralized and concurrently building our decentralized solution cooperative with established PKI standards (i.e., X.509) for effective real-world integration. In particular, we harness the blockchain technology to propose a decentralized PKI framework named ProofChain, which provides complete trust among a decentralized group of CAs. Our proposed solution provides all the traditional X.509 PKI operations (i.e., registration, validation, verification, and revocation), making it compatible with existing PKI standards. We have also evaluated ProofChain against popular security standards (i.e., CIA triad model) and PKI adversarial attacks. Besides, to demonstrate the practicality of our proposed system, we have also evaluated the performance of the ProofChain by implementing it on the private testbed of the Ethereum network across various real-world PKI scenarios.
Chapter
Permissionless blockchain systems are highly dependent on probabilistic decision models, for example, the block addition process. If it were possible to use blockchain systems as pseudo-random number generators, they could be used to select, for example, new block proposers. The first step in this process is to embed random number seeds in the blockchain for use in pseudo-random number generation. This paper proposes transient random number seeds (TRNS), which produce random number seeds as part of each transaction. TRNS, belonging to each recipient in a transaction and are confidential, tamper-resistant, unpredictable, collision-resistant, and publicly verifiable. TRNS enable recipients to produce pseudo-random numbers to participate in any process where the blockchain system depends on random selection. The TRNS protocol is highly scalable with constant computational complexity and space complexity linear in the number of transactions per block.
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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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.
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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.
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.
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://www.techinasia.com/inner-mongolia-Bitcoin-mine.
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. https://qz.com/1053799/chinas-bitmain-dominates-Bitcoin-mining-now-it-wantsto-cash-in-on-artificial-intelligence/.
Just how profitable is Bitmain?
  • J Song
Song, J. Just how profitable is Bitmain? 2017. https://medium.com/@jimmysong/just-howprofitable-is-bitmain-a9df82c761a.
Bitcoin Energy Consumption Index
  • Digiconomist
Digiconomist. Bitcoin Energy Consumption Index. 2018. http:// bitcoinenergyconsumption.com.