Finally, to interact with a traditional financial system, owners, exchanges, and miners need a bank e. In such a case, a transaction fee has to be paid to the bank flow 7. Note that the model abstracts from the flow of bitcoins which are needed for end-user transactions e. This follows from our focus on the miners in their value system and not on consumers who use bitcoin for the purchasing of products and services, or speculation. They are important for the correct functioning of the network, but carry no financial compensation so that monetary flows to those nodes are by definition zero.
Moreover, since the number of full nodes is not known at all, it is impossible to include them in the analysis. We assume that the other actors e. Manufacturers of hardware and electricity power companies have also other customers and can easily calculate the price of their products and service such that a net positive flow results.
Pools are a kind of insurance for miners to ensure that, over time, they will have positive revenues. Pools are an effective risk sharing mechanism and base their fees on insurance policies; hence we assume they are capable of generating a positive net cash flow. Similarly, exchanges just trade bitcoins for traditional money. We assume that the losses and profits average over time, and result in a modest net positive cash flow.
Although we assume for most actors that they have a net positive cash flow, we nevertheless have to know their cash flow, since miners either have to pay or receive cash. For example, miners have to pay to the power company a fee for electricity. Below, we briefly introduce how the fees are calculated, which is discussed in more detail in Section 5. Mined bitcoins: Bitcoins obtained as a result of mining. The aggregate information about mining results is publicly available, which is sufficient for the analysis.
Hardware investments: these are unknown. In the next section, we present an approach to estimate the installed base of mining hardware over the period of analysis. Electricity expenses: these directly relate to the installed hardware base.
Footnote 2 Therefore, once we know which hardware is deployed during which period, we can estimate the total electricity power expenses over time, assuming that mining hardware is always on. Since most hardware is dedicated, this is a realistic assumption. Footnote 3 value flow 4 of Fig. Bitcoin transaction fees: from the bitcoin user to the miners whose numbers are publicly available.
Exchange fees: we assume an average of 0. This is similar to the range of fees exchanges charge per transaction like 0. Footnote 4 value flow 6 of Fig. Bank fees: differ per bank and usually contain a fixed and a variable amount. For this research we assume it is similar to the exchange fee with 0.
The first version of the e 3 value model was presented to the interviewed parties and discussed. All of the interviewees agreed on the bridging role of banks and exchanges between bitcoin and fiat money. The co-founder of a bitcoin payment provider concluded that while bitcoins are created by the miners, the actual monetary value is assigned once it is sold via exchanges and turned into fiat money via bank accounts.
The business manager at a bank noted the scalability of the amount of transactions the bitcoin network can handle is a weakness. The bitcoin consultant underlined the importance of energy prices to mining and predicted a movement toward regions with lower energy prices like China and lower cooling costs like Iceland.
The retired bitcoin miner mentioned the centralization occurring with bitcoin mining as the initial investment is increasing continually. The interviewees agreed on the proposed bitcoin value model. One interviewee proposed additional actors that were cost factors for the payment providers, but the interviewee agreed it was not a cost factor to the miners, so these were not added to the model.
After drafting the value model the interviewees were contacted again for comments. The four interviewees had nothing to add. Thus, we consider the e3 value model sufficiently supported by the literature and by the option of experts. To assess the sustainability of the network, the money flows have to be quantified for actors for which we cannot safely assume a positive net cash flow.
As Section 5 explains, we focus on the miner, since the miner is the enabler for the bitcoin system, and has significant expenses mainly hardware and energy. For quantification, we rely on publicly available information about bitcoin trade volume, mining revenues, electricity prices, etc. For some data, we have to make estimates. Specifically, the installed mining hardware base is unknown over time but an important cost to actors. We therefore develop a method to estimate this installed base.
The way of estimating is an important contribution of this paper. Finally, we analyze the results for sustainability. Concerning data collection, a significant amount of publicly available data is an advantage of the bitcoin system. In particular, we use data retrieved from blockchain. For the analysis of sustainability, we first look at the expenses and revenues of miners and the resulting value flows from these. We start by inferring which mining hardware is in use during which specific period.
This is necessary as the hardware investment represents a large cash outflow for the miners. Third, the computing performance of specific hardware directly determines the expected number of bitcoins mined by that hardware. Formally, we solve an equation that models the total bitcoin hash rate on each day as a function of the hardware in operation. From the hardware in operation we can deduce the hardware spending and the electricity costs.
Other expenses pool expenses, bank costs and exchange fees follow from the total production of bitcoins. Starting from the observed total bitcoin hash rate, TH t on day t , it must be the case that. As long as no better type is available, the machines stay in operation to produce the total hash rate that we observe in the data. At a first increase in the hash rate, the number of machines increases to reach the total hash rate.
At a decrease in the hash rate, we assume that new machines are throttled back or old machines are turned off. Footnote 5. Once a new machine becomes available, we assume that buyers choose between hardware types by picking the machine with the lowest estimated payback time. This way of calculating the attractiveness of an investment is common practice Berk and DeMarzo and the simplicity of the technique fits the dynamism and fast-changing nature of the bitcoin miners.
For each machine on the market, the payback time is computed using the day moving average of the bitcoin price:. Existing machines stay in operation as long as the marginal profit is positive, i. If that is not the case, we assume that they are switched off on that day. They can come online again if they become profitable again, for example, when the bitcoin price increases. The combination of machines in operation on any given day is then simply equal to the number in operation on the previous day, minus machines that have become unprofitable, plus new machines of the type that have the lowest payback time.
Then, we have that. Although the hash rate is increasingly almost continuously in our sample period, there are a few instances where the hash rate declines. We allocate those decreases to the most recent machines that we assume are throttled back proportionally.
Footnote 6 Since declines in the hash rate are rare and small see Fig. We now turn to the data that is fed into Eqs. Figure 4 shows the hash rate and difficulty of the bitcoin network increasing by a factor of more than , from to There are two reasons why this happens. First, faster hardware is added to replace slower running hardware for which electricity expenses outnumber mining and transaction revenues. Second, new hardware is added to increase production, as bitcoin mining becomes increasingly popular.
In both cases, we attribute the increase in computing power in the bitcoin network to new hardware. Regarding the purchasing of mining hardware, we assume that miners behave rationally and therefore buy the hardware with the lowest payback time.
During the year the payback time of the cost-efficient hardware is shorter than that of energy-efficient hardware. Payback time for most energy-efficient en. During the first 6 months of , the payback time is so high, it would take decennia to earn back the hardware. At the beginning of our analysis period, we assume that the AMD is installed, which was the best available hardware at that time.
Regarding the operation of mining hardware, we assume that mining hardware remains in operation until the daily electricity expenses related to that hardware is equal or higher than the expected revenues for that day, namely the value of the mined bitcoins and the transaction fees. In other words: after initial investment, the only incentive for miners to turn their hardware off is that the marginal expenses for mining electricity outweigh the marginal revenues.
The energy cost for a particular type of hardware is known. Therefore, in order to calculate the payback period, we must know the expected revenue. This assumes that miners possess no superior timing ability, which seems sensible. Given the assumptions on purchasing and operations we can estimate the hardware in use over time. As the market of mining hardware is not transparent, the archived pages Footnote 8 of a public wiki page Footnote 9 are used to select the most cost-effective hardware over the period to This data was cross-referenced with discussions on the public forum bitcointalk.
The results are in Table 1. Since the performance of the bitcoin network is known, we can calculate the upfront hardware investment, if we assume all hardware was the AMD at that time. Then, for each subsequent day we can infer the hardware purchases using the increase in hash rate and available hardware on that day.
With the assumption of positive marginal revenues, we also can calculate when new hardware is added or retired. Note that, because the hardware is tailored to bitcoin mining, we consider the residual value of hardware zero as it cannot be used economically for other tasks. Now that we know which specific kind of hardware is into operation during which specific period, we can also calculate the electricity consumption of that hardware, and related to that, the electricity expenses.
We assume that mining is always running during the period of operation. Figure 7 shows the rapidly increasing energy usage of the bitcoin network from to This seems sensible, given the hash rate ultimo of 2 bln. It does question the earlier estimate of O'Dwyer and Malone , who find a number that is close to the electricity use 3GW of Ireland in Their estimates, however, are based on a theoretical estimate of the hash rate instead of the real rate, and is a mid-point estimate of a wide range of possibilities.
Figure 8 gives a graphical representation of our estimates of when certain hardware was in use. The sudden drops of profitability during periods like the fourth quarter of and the second quarter of , suggest the predicted gradual linear and exponential profit declines of online mining calculators are an unreliable tool for net cash flow prediction. Assuming that all mined bitcoins and earned transaction fees are immediately exchanged for dollars, exchange and bank expenses directly relate to the amount of bitcoins transferred and mined each day.
The expenses are summarized in Table 3 , by hardware type. Table 4 summarizes the expenses and revenues, and calculates per hardware the estimated generated net cash flow. As can be seen from the table, the first part of our analysis period shows a positive net cash flow for miners. The numbers of the flows in Table 4 correspond to the numbered value transfers in Fig. However, the last two periods have a loss. At the end of the measurement period, only the Antminer S9 was still running on a profitable basis, so the losses might be compensated in the later periods.
Table 4 also shows that in some time periods the investments in hardware have been very profitable, such as with the Avalon 1 in Most of the income stems from the generated bitcoins, while most of the costs are due to the hardware investments. The hardware expenses are by far the biggest expense to bitcoin miners. This upfront investment in hardware, combined with a high daily energy cost leads to considerable losses in the later years.
Figure 9 shows the day moving average of total revenues and expenses. As can be seen, the expenses related to bitcoin mining approach the revenues, which is also predicted by economic theory: under full competition, marginal revenue approaches marginal costs. This holds for normal goods as well as for virtual goods and currencies as bitcoin. Figure 10 shows the marginal expenses not counting the upfront hardware purchases compared to marginal revenues. During and these lines approach each other, leading to very little profits.
This makes it very difficult to have a return on investment on the acquired hardware. The sudden drop in revenue and expenses in mid is likely a result of the blockchain halving, where the bitcoin reward was halved from 25 to Marginal daily expenses and revenues on a logarithmic scale of Figure 11 shows the cumulative net cash flow that resulted from Fig. Positive flows are followed by periods where money is invested in new hardware, leading to temporarily negative net cash flows. By mid, the high revenues of and are countered by high expenses, leading to a negative net cash flow from that moment on.
It can be seen that this results in a positive net cash flow, but due to necessary new investments, the total net cash flow drops with each innovation. Energy prices determine the profitability of mining hardware, so it could be argued that these prices heavily influence the resulting profits. It is therefore meaningful to do a sensitivity analysis with respect the energy prices. A question we can ask is what the exchange rate of the bitcoin should have been in order to reach the break-even point for the modes.
The estimates in Table 6 should be interpreted with care. It is likely to expect that a change in the exchange rate would influence other parameters too, e. Since our analysis is based on factual data of the bitcoin network, we cannot compensate for these effects.
To do so, a proper simulation model of the bitcoin network should be developed to include the market dynamics. An important question is how reliable our estimates are. Our calculation relies on the one hand on publicly available data which are factual e. Understanding of the installed base is important, because the kind of hardware installed determines the expenses by miners, namely the initial hardware investment and the expenses for energy. A recent other study by De Vries also aims to estimate the total energy consumption for the bitcoin, although a different analysis period is used Feb 10th — present, see the Bitcoin Energy Consumption Index BECI Footnote 13 , which displays the results of their installed base estimate model.
In our calculation, at June 20 , the electricity power consumption was The BECI estimates for February 10th the first date of analysis the yearly energy consumption as 9. The difference of The BECI uses a fairly straightforward model: it assumes that hardware remains in production by miners until it reaches its minimum sales price. Our model supposes a rational behaving miner in the sense that 1 at each point of time, the miner buys the hardware that has the shortest payback time, and 2 the miner takes hardware out of production and replaces it by newer hardware if the marginal expenses for mining electricity outweigh the marginal revenues.
Given the purpose of this paper, namely to argue that the bitcoin network is not sustainable on the long term, our estimate of the installed base is conservative; using the hardware estimation method of the BECI would result in higher energy costs and therefore in increased losses for the miner. This is what economic theory predicts for a market with profit-maximizing companies under full competition. A comparison could be drawn with the value of the Somali shilling between and Luther documents how, in the absence of a central monetary authority, Somali clans produced currency themselves or imported it from foreign producers of paper money.
Similarly, the pattern in Fig. Given that bitcoins can be mined by everyone and everywhere, this is a direct result of the competitive pressure on mining bitcoins. Once hardware has been purchased, it becomes a sunk cost and only the marginal costs need to be covered.
At the same time, the operators of mining hardware need to make an average profit over the lifetime of the hardware, taking into account the wildly fluctuating prices of bitcoin. This makes them reluctant to switch off the hardware, even at very low rates of profitability. Actual loss-making operations are of course irrational, but could reflect the fact that a sizeable fraction of miners in the bitcoin industry are not financially literate and might underestimate the electricity costs that they are incurring, for example.
There are a number of ways how the bitcoin can be made economically sustainable. Unfortunately, none of these possibilities are very realistic. First, the energy price could drop significantly world-wide, for example to 0. Then there would a slight profit for the miners. But even in Inner Mongolia, which is considered to have one of the lowest energy prices 0. Additionally, reducing energy consumption use could be achieved by introducing predefined and trustful parties to operate the consensus mechanism and the release of additional coins , which can be done in a far more energy-efficient way.
Although this contradicts the design philosophy of the bitcoin somewhat, i. Finally, a more efficient consensus mechanism could be used, including proof-of-stake consensus should only be reached by parties who own the most bitcoins, since they have the most interests in trust in the currency Narayanan , Byzantine fault tolerance a voting mechanism in distributed systems, e.
Bitcoin-NG Eyal et al. Sieve, as used in Hyperledger Cachin ; Cachin et al. However, other limitations and hurdles to the acceptance of bitcoin as an efficient payment mechanism will remain. For example, it is not clear whether any distributed ledger mechanism could rule out multiple equilibria, Biais et al. Also, some consensus mechanism e. Byzantine fault tolerance do not scale to millions of users.
Second, the bitcoin price may increase substantially, which happened in , which however outside our analysis period. The recent history however has shown that the bitcoin exchange rate is very volatile. Actually, bitcoin is nowadays used as a very high risk speculation instrument, rather than a payment instrument.
Therefore, speculating on the increase of the bitcoin exchange rate is very risky, and therefore not reliable enough to justify long-term economic sustainability. Third, another solution might be to increase the transaction fees that miners get if they include transactions in the blockchain.
However, if we take the numbers of for example, the transaction should be increased dramatically: the earnings from transactions fees were In other words, the income for transaction clearing is neglectable compared to mining. Moreover, a substantial raise of the transaction fees would change the business model of the bitcoin significantly: from neglectable transaction costs to high transaction costs. Finally, it can be doubted whether the bitcoin is a significant and desirable payment solution at all, compared to traditional payments as offered as banks.
Take for example the transaction volume of VISA Footnote 14 alone, which is billion transactions in In that same year, the bitcoin platform processed about 83 million transactions. Footnote 15 This implies that the bitcoin is neglectable as it comes to the world wide transaction volume.
This paper analyzed the long term financial sustainability of proof-of-work mining for the bitcoin network. We have considered the profitability of the miner, expressed by a sustainable net positive cash flow, as the key factor in judging bitcoin sustainability. By reverse-engineering the type and number of computers that have been mining bitcoin, we found a negative net cash flow for most of the measurement period. This answers research question 2: on the long term, miners can not be sustainable.
Since the miners are crucial for the correct functioning of the bitcoin network, this endangers the sustainability of the bitcoin network itself research question 1. In terms of future research, an important question is how to build a payment service that is 1 economically sustainable, and 2 can scale up to transaction volumes handled by the traditional banks, and 3 fully decentralized, that is, without any intermediate party such as banks.
A key component of the answer is a consensus mechanism that is very scalable and economically sustainable. Clearly, Proof-of-work is not economically sustainable, as argued in this paper. Finding such a consensus mechanism is ongoing work, although important steps are taken. PoET claims to be highly scalable and energy friendly. We leave out the costs of internet connectivity, since mining is a very low bandwidth activity. Therefore, internet service providers are not included in the model.
The total network hashrate can fluctuate on a daily basis, but in general it increased by an average of 1. In reality, a decrease in the hash rate could be due to start-up problems of new machines due to overclocking, decommissioning of older hardware, negative price shocks in the value of bitcoin, or alternative use of hardware, for example, to mine other cryptocurrencies. Alt, R. The rise of customer-oriented banking-electronic markets are paving the way for change in the financial industry.
Electronic Markets, 22 4 , — Barber, S. Bitter to better - how to make bitcoin a better currency. Keromytis Ed. Berlin: Springer. Google Scholar. Berk, J. DeMarzo Corporate Finance. Third and global edition. Biais, B. Bouvard and C. Casamatta The Blockchain folk theorem.
Working paper TSE, Toulouse school of economics. Bouoiyour, J. What does Bitcoin look like?. Cachin, C Architecture of the Hyperledger blockchain fabric. Mimeo, IBM research - Zurich. Courtois, N. Visited August 13th, Cachin, C. The unreasonable fundamental incertitudes behind bitcoin mining.
Davies, S. Financial Times, January 20, Decker, C. A fast and scalable payment network with bitcoin duplex micropayment channels. In: Symposium on Self-Stabilizing Systems pp. Springer International Publishing. Edgar Fernandes, N. Ember, S. Jitters after bitcoin exchange suspends services.
European Central Bank Virtual currency schemes — a further analysis. Eyal, I. Bitcoin-ng: A scalable blockchain protocol. Forte, P. Gervais, A. On the privacy provisions of bloom filters in lightweight bitcoin clients. Goldman S. All about bitcoin. However, this is not an easy thing to do. The hash — a digit code in which each digit can be any one of 16 different characters — is usually described as the solution to a complex computational maths problem. However, as there is a huge amount of possibilities, that complexity cannot be understated.
As a result, mining involves a process of producing lots of different possible hashes very quickly, until the right one is found. Right now, the chances of finding the right hash is one in 14 trillion. And so, given that there is a reward — i. Bitcoin — for finding the hash first, you need to produce millions of hashes a second to find the correct one.
This is why you need the gear. Specifically, this is hardware that can produce these hashes as quickly as possible. The faster the better, because you are competing with about a million different miners, according to one estimate. The slim odds of finding the right hash, and the competition that you are up against, makes mining cryptocurrency a difficult game — and there is no guarantee that you will ever be rewarded.
In this way, Bitcoin mining invites comparisons to literal mining for gold. But is it worth the effort? After the initial investment in the hardware, there are running costs to Bitcoin mining. This is related to the cost of energy in each country, because Bitcoin mining uses a serious amount of computer power.
As a result, how worthwhile Bitcoin mining will be will depend on which country you are in. There are legal prohibitions on Bitcoin mining in a small handful of countries worldwide at the time of writing. These include Bolivia, Nepal, Algeria, and Morocco, where cryptocurrency is banned completely, according to a report prepared by the Law Library of Congress.
Where crypto itself is banned, crypto mining is prohibited, too. However, according to the same report, Russia, whilst open to crypto, has placed taxes on any Bitcoin miner who exceeds a given amount of energy consumption due to mining. So, what is Bitcoin mining? It does two things: it ensures that every transaction on the cryptocurrency network is verified, and it brings into the system new currency.
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Paramount to the decentralized nature of Bitcoin is the requirement that millions of dedicated computer systems throughout the world are competing with one another to conduct transfers. The term mining is typically used to describe this process where the computers may be awarded the valuable coin in proportion to the amount of computer processing power used. The massive amount of computer work needed by the currency is purposely done to give the digital asset value, however, whereas traditional currency like the United States Dollar sees very slow value changes, Bitcoin has seen huge variations.
With so many systems competing against one another to conduct Bitcoin transfers, no one person or entity can control the transfer market. This guarantees the decentralized nature that is a tenet of Bitcoin. It also allows for the creation of additional Bitcoin tokens to be added to the system. This will not be permanent though, as there are currently roughly 18 million Bitcoin tokens in the market and the system calls for a cap of 21 million tokens.
Full capacity of the facility is Megawatt MW , and almost 7, units of the latest-generation mining hardware, utilizing more than 14 MW, were in operation at the time. The facility has asked to expand, and if approved, would allow for the construction of four buildings to house additional Bitcoin mining computer systems. Opposition to the expansion has cited concerns with lake water temperatures due to the use of Seneca Lake water to cool the power generation equipment and greenhouse gas emissions from the natural gas power plant.
Under this scheme, you can earn Bitcoins as a mining reward without even owning a single mining machine. This makes up another way you can invest in Bitcoin mining without having to quit your job or being present at the mining farm to inspect those machines.
By cloud-mining Bitcoin, your physical presence is most likely not needed, as the machines run from a remote location. Hence, this will enable you to auto-mine Bitcoin without directly investing in or buying mining hardware or software. All you need to do is purchase a mining contract from companies offering cloud mining service. Cloud mining saves you a lot of stress and challenges that come with running your mining farm.
You can go about your job, holiday, and even travel abroad without necessarily worrying about hardware and software maintenance, electricity supply, and mining farm downtime issues. Noteworthily, cloud mining also comes with disadvantages, which you would want to consider before buying any cloud mining contract. Firstly, you do not have ownership of the mining equipment, so you cannot guarantee that these machines will run as long as you want.
When such situations arise, these miners resort to selling off their bitcoin holdings, which in turn can affect the market negatively. Secondly, the cloud mining provider will charge a commission for the service rendered.
It would help if you also exercised caution, as there are many illegal bitcoin mining investment companies also promoting Bitcoin cloud mining services. They typically claim to sell investment plans and offer highly robust referral programs that are only designed to send more victims their way. Some even run campaigns via Google Ads to attract unsuspecting investors. Please, stay away from them. If you must invest in bitcoin mining via cloud mining companies, ensure that you do due diligence and possibly involve a legal expert.
You can check cloud mining platforms like Minergate. Since your chances of competing with large-scale bitcoin mining investment companies are very slim, miners came up with the idea of pulling their resources together, thereby raising their hash power. By joining a Bitcoin mining pool, you will contribute your computing power with that of other miners in the pool to generate a block, which comes with a reward.
These rewards are being shared amongst the miners evenly. So, you ought to decide whether to stand alone or pull your resources together with other miners. Note that mining pools have a greater chance of generating blocks because of their superior hashing power. Thus, the miners that invest in bitcoin mining via pools are likely to receive more income from time to time.
Many think that there is free Bitcoin mining without investment; however, the answer to this question is pretty much a NO. Another factor that often determines profitability is how soon you sell your bitcoins after mining them. Under bullish market conditions where transaction fees spike, bitcoin miners make more profits as well. Using this tool, you can invest in bitcoin mining with an estimated profit ratio in mind. In this article, we covered vital information about how to invest in Bitcoin mining.
We also disclosed that there is free Bitcoin mining without investment. Anyone tell you that there is a way to mine Bitcoin without investing is probably going to rip your off your money. You must also keep an eye out for the many fake bitcoin mining investment companies out there, promise unrealistic returns over a short period.
We believe that this guide will help you as you consider how to invest in bitcoin mining.