Specifically, Bitcoin combined several well-known concepts from cryptography to form the so-called PoW. This refers to the right to create a new block from a subset of queued transactions when one finds a solution to a cryptographic, computationally intensive puzzle. This results in coupling the voting weight to a scarce resource — computing power and thus energy — and hence prevents Sybil attacks.
The mining process is economically incentivized in that participants are rewarded for every valid block that is found and disseminated. The reward typically consists of a certain amount of the associated cryptocurrency and the fees for the associated transactions.
In turn, this has led to an enormous energy consumption associated with the underlying PoW blockchains. It is essential to note that the high energy consumption of PoW blockchains is neither the result of inefficient algorithms nor of outdated hardware. Consequently, the more valuable a PoW cryptocurrency is, the better it is protected against attacks, confirming that PoW is, indeed, a thoughtful design.
However, results regarding the energy consumption of PoW cryptocurrencies and blockchain technology in general are rare. Determining the exact value for the energy consumption of a multitude of open, distributed networks is a hard task because the precise number of participants, the properties of their hardware, and the effort which they put into mining are unknown.
This gives a lower bound of the energy consumption of an arbitrary PoW blockchain:. This estimate indicates the lower bound, reflecting the likelihood that more solutions are found than disseminated, that further computations — in addition to mining — are being carried out, and that not every miner has the most energy-efficient hardware.
Both the current hash rate of a public blockchain and the energy efficiency of the most efficient mining hardware can easily be retrieved from online material. However, one must be aware that mining hardware is in general blockchain-dependent because the algorithms used for hashing can differ.
On the other hand, Ethereum was designed to prevent the use of highly specific mining hardware, so general-purpose GPUs can be used for mining. Entering the current numbers — retrieved from Coinmarketcap and Coinswitch on — into 1 yields a lower bound for power consumption of 6. Alternatively, one could, of course, also integrate the time-dependent lower bound over the period under consideration.
One can also determine an upper bound for the energy requirement of the mining process for a PoW blockchain, assuming honest and rational miners whose utility from mining is solely financial profit: Participation in the mining process is only profitable as long as the expected revenue from mining is higher than the associated costs:.
As hardware costs represent a substantial part of the costs side, and electricity prices vary significantly around the globe, we cannot assume that the upper bound is very tight. The block reward, i. Market capitalization and the computed bounds on energy consumption for the 5 highest valued Proof-of-Work cryptocurrencies. Note the logarithmic scale on the y-axis. We see that the lower and upper bounds are, in general, quite close and, therefore, represent a meaningful estimate of the actual energy consumption for each of the 5 major PoW cryptocurrencies.
Consequently, we learn that we cannot take for granted that the given upper bound holds forever; it merely represents a snapshot for the current economic situation. Moreover, the total market capitalization for all other PoW cryptocurrencies is significantly lower than that of Bitcoin itself. This indicates that the total energy consumption of all PoW cryptocurrencies other than Bitcoin will fall below our upper bound for the energy consumption of Bitcoin.
A more precise estimate could be obtained by applying 2 to all remaining PoW cryptocurrencies. In both estimates, we have, so far, only taken into account the energy consumption involved in mining, i. This is, in fact, a reasonable approximation: for the lower bound, we only lose some tightness. Since energy costs outweigh hardware costs in the long run, participants with improved hardware can solve more puzzles at the same energy costs.
Other participants have to follow suit with the competition. This, in turn, involves higher overall computing power, and means that the difficulty of the puzzle needs to be increased so that it is, on average, solved as frequently as before.
Hence, it is only in the short-term conversion phase that positive effects are conceivable. In fact, competition in the mining hardware market, resulting from the hype around cryptocurrencies, has dramatically increased the energy efficiency of mining hardware in the last decade. In summary, our lower and upper bounds represent different approaches and use different quantities that have to be estimated.
Yet, these bounds are very consistent in the case of all of the cryptocurrencies we investigated. However, as cryptocurrencies currently process only few transactions per second, the theoretical limit is typically in the low two- or three-digit range, e. Accordingly, a single transaction currently requires enough electrical energy to meet the needs of the average size German household for weeks, or even months.
By contrast, traditional payment systems process, on average, thousands of transactions per second, and as many as tens of thousands at peak times. This means that, overall, there would be no noticeable increase in total energy consumption. In practice, however, the blocks cannot be enlarged at will. While in Bitcoin Cash, for example, the blocksize has been increased by a factor of 8 compared to Bitcoin without any problems, a significantly larger block size is currently not practicable.
This is because, the larger a block is, the longer it takes for it to be propagated by the worldwide blockchain network. Moreover, not every household can afford a high bandwidth and large hardware storage, so higher requirements can also lead to a lower degree of decentralization.
This trade-off has already been discussed, e. If, however, storage capacities hard disks and network speed continue to improve worldwide, a considerable increase in block sizes might be conceivable in the future. This would enable higher transaction rates without a noticeable increase in energy consumption. Finally, for most PoW blockchains, the block reward is not constant, but periodically halved, typically, every few years.
Hence, if the prices for crypto-coins and electricity prices remain at the same level, one could even expect that in the long run, the energy consumption of PoW blockchains will also halve in each of these periods, until the rewards from mining are comparable to the total transaction fees. We conclude that, although the energy consumption of PoW blockchains is arguably enormous in relation to their technical performance, it does not represent an essential threat to the climate, even if significantly more transactions are processed in the future.
Moreover, since the area of application of most blockchains — and, in particular, the major cryptocurrencies — is often far beyond payments, plenty of opportunities for new ecosystems and business models arise. An evaluation should therefore not only compare performance metrics and energy consumption, but also take into account the unique opportunities offered by this technology.
Fortunately, the PoW consensus mechanism, which — as already described — was designed to be energy-intensive, is not the only way to achieve consensus in a distributed system. The probably best-known alternative for the permissionless systems required for cryptocurrencies and other open decentralized applications is the so-called Proof-of-Stake PoS consensus mechanism. More precisely, there is a random mechanism there are no truly random number generators for classical computers, but, as a first approximation, this heuristics provides a good indication.
The deposit also incentivizes the node to stick to the rules of the network, as any misbehavior detected will lead to the node losing this deposit. The advantage of PoS is that it does not involve any computationally intensive steps such as solving the cryptographic puzzles in PoW.
The computational complexity of PoS consensus is low and, typically, insensitive to network size. It is, therefore, very energy-efficient for large-scale systems. Accordingly, based on our arguments regarding the energy consumption associated with operating transactions in Sect. It is primarily for this reason that the community of the cryptocurrency with the currently second-highest market capitalization, Ethereum, is trying to switch from PoW to PoS.
There are, however, controversial discussions in the community. However, one can also argue that PoS has less of a tendency to centralize mining has economies of scale and is, thus, more secure in the long run. We will not enter in this discussion up here but want to highlight that the outcome will likely decide which consensus-type for permissionless blockchains prevails and, therefore, impacts the energy consumption of future open decentralized applications.
On the other hand, blockchain technology can also be useful in constellations in which only a restricted group of participants take part in consensus. These are referred to as permissioned blockchains. Therefore, it is not necessary to tie voting weight to a scarce resource here, and one can reach consensus using some kind of election in which everyone has a single vote. Therefore, this kind of consensus mechanism is sometimes called Proof-of-Identity or, very often, Proof-of-Authority PoA. Popular implementations of such permissioned blockchains are Hyperledger Fabric and Quorum.
The more secure these PoA consensus mechanisms are, the greater their complexity and, therefore, the greater their energy consumption. For example, PBFT consensus overhead scales at least quadratically with respect to the number of nodes in the network and is hence — by contrast to PoW and PoS — highly sensitive on the network size. This, in turn, correlates with the energy consumption associated with consensus. Beyond these popular consensus mechanisms, there are several more, an overview of which is provided by Eklund and Beck An example is Proof-of-elapsed-time, which intends to establish trusted random number generators through secure hardware modules.
Since many of these types of consensus mechanisms are not currently prevalent in relevant applications, and because they usually have low energy requirements compared to PoW, we will not investigate these consensus mechanisms in more detail. The main result of the discussion about blockchains with alternative consensus mechanisms is that, by getting rid of energy intensity by design, their energy consumption is orders of magnitude lower compared to PoW-blockchains. Consequently, the energy consumption of non-PoW blockchains can hardly be considered problematic for the climate.
Yet, beyond PoW and, thus, on a completely different scale, the type of consensus mechanism can have a significant impact on energy consumption. We have seen that for PoW blockchains, the energy consumption related to consensus outweighs the energy consumption associated with operating transactions, so the redundancy aspect is usually not discussed in detail.
For non-PoW blockchains, however, the energy consumption related to consensus is no more enormous, and, therefore, the contribution to total energy consumption by redundant operations may be significant. Hence, it is not only alternative consensus mechanisms that one should look at to further reduce the energy consumption of blockchain technology, but also concepts which allow reduced operation redundancy. Generally speaking, the primary motivations behind all of the concepts presented in this section that may help to reduce redundancy are increased scalability, throughput, and privacy for blockchain solutions.
Conveniently, these all happen to reduce the degree of redundancy and, therefore, improve the overall energy consumption. We can distinguish between two approaches to reducing redundancy: reducing the degree of redundancy, i. In attempts to reduce the degree of redundancy, a concept called sharding is often mentioned.
How easily sharding can be achieved largely depends on the consensus mechanism. For example, sharding is very difficult to apply to PoW blockchains, because one has to make sure that, within a shard, computing power is roughly equally distributed to maintain a balance of voting weight among the associated nodes.
In a PoS blockchain, voting power is tied to the capital deposited by each node. This information is publicly available and can, therefore, be freely used in creation of shards. Other concepts to reduce the degree of redundancy include off-chain payment channels between two parties who repeatedly interact. Such channels usually require a transaction on the blockchain, in the course of which off-chain payment channels are created and terminated. Ideally, however, all interim transactions are operated purely bilateral and do not involve a transaction on the corresponding blockchain.
That is to say that, ideally, only balances, or accumulated deltas signed by the members on the payment hub, are periodically recorded on-chain. Payment hubs, a generalization of payment channels to multiple parties, e. A similar basic concept is the use of sidechains e. These are small blockchain networks which periodically refer to the main chain as a highly reliable root. Generally speaking, however, reducing the degree of redundancy also makes a blockchain network more centralized and must, therefore, be carefully weighed against concerns about security, liveness, and trust.
Finding a good compromise between these interests could enable a reduction of the total workload in the system, and, therefore, a reduction of its total energy consumption. On the other hand, the workload associated with redundant operations, e. One very straightforward improvement is, therefore, optimization of the computational complexity of the used cryptographic algorithms, e.
This could be significantly improved by storing and verifying only short correctness proofs on a blockchain and distributing the larger, plaintext data on another layer to the relevant participants. This is because, unlike methods that lower the degree of redundancy, these do likely not have a negative impact on security because every transaction is still verified by every node.
In summary, there are various ways to reduce the intrinsic redundancy of blockchains and, therefore, to reduce also their energy consumption. The relative energy saving potential is, however, negligible for PoW blockchains as the energy consumption of mining dominates all other contributions. However, it may still be relatively high for networks in which consensus is not energy-intensive, in particular, if the network is large. We can now use our results from the previous chapters to make a first comparison of the energy consumption of typical blockchain architectures.
The role of consensus has already been discussed in Sect. By contrast, for large systems consisting of many nodes, the natural redundancy in a blockchain can lead to much higher energy consumption. If a PoS or alternative non-PoW blockchain replaces Bitcoin or another PoW cryptocurrency in the future, we have to expect that there will still be tens of thousands of nodes. Although the energy consumption of such a network will be negligible compared to Bitcoin, it will, therefore, remain high compared to a non-blockchain centralized system with minimal redundancy i.
Our rationale is that the Brent Crude oil price is a publicly available daily value standardized around the world whereas electricity prices varies widely across different countries and suppliers. Note that there is a premium that electricity producers and distributors charge on the electricity price with respect to the oil cost and there can be also taxes.
These extra charges depends on countries and situations but they will add a certain percentage to our estimate of the mining cost based on oil prices. As another point of comparison, regional electricity prices were also used as a proxy for the energy cost. The average global electricity price used for mining was calculated based on the geographic distribution of hash rate on the Bitcoin network and the local industrial electricity price.
An overwhelming proportion of Bitcoins are mined in China so the data there is further stratified based on provinces. They are shown in Table 3. The three nations also publish government statistics regarding industrial electricity prices on a regular basis China: NEA, USA: EIA, Russia: Petroelectrosbyt which allowed for the annual weighted average electricity price for Bitcoin mining, E t , to be calculated as.
Table 3. Geographic distribution of the share of hash rate on the Bitcoin network, — A disproportionately large percentage of mining activity within China was based in provinces with lower than average electricity prices so where provincial data were not available, a 0. Regional share of hash rate and electricity prices were not available for USA or Russia so similar adjustments weren't possible. Another limitation of electricity prices is that a growing proportion of Bitcoin mining uses low-cost stranded renewables Andoni et al.
Due to these other factors and the lack of historic data on electricity prices in several other countries around the world, the majority of this paper will focus on energy pricing using the Brent Crude oil index. A comparison of ratio between the cost of mining and Bitcoin transaction volume is presented in Figure 6 to show the standardized oil prices as a measure of energy cost yield similar results to using regional electricity prices.
For the purpose of estimating a lower bound to the energy costs of Bitcoin mining, we considered at any point in time that the entire network is adopting the most energy efficient machine available at that time. In situations where a mining hardware has different power setting options in which the user may choose to increase or decrease the hashing speed of the machine along with energy consumption, the most efficient power setting is used for calculation. The lower bound of the energy costs of Bitcoin mining is estimated from total number of hashes times the energy cost of hashing by the most energy efficient Bitcoin mining hardware available on the market at any give time, divided by the conversion factor between energy and barrel of oil and multiplied by the cost of the oil.
Specifically, the lower bound for daily mining cost, C t , is:. H t is the daily number of hashing operations in Th on day t ;. Table 2 reports a list of the Bitcoin mining hardware which consumed the least energy per hash operations at the time of their release to the market. In a previous work a power-law model was proposed by Kristoufek However, the exponential model is more consistent with what is commonly expected for the rate of technology growth, according to the Moore's Law Moore, Figure 1.
Figure 2 displays the total number of hashing operations per day. We note that the number of daily hashes have increased from 10 15 to 10 25 in the period between September to May when this paper was written. Daily hashes have been growing at exponential rates linear trends in semi-log scale , which is in agreement with previous observations O'Dwyer and Malone, However, we can see from the figure that there are four, very distinct, periods with different grow rates.
Specifically: i mid to mid ; ii mid to early ; iii early to early ; iv early to early The estimated best-fit doubling times in these periods are respectively: 1 33 days; ii days; iii 38 days; iv days. Figure 2. Daily hashes computed by the Bitcoin network. The lines are best-fits with exponential growth laws in the corresponding sub-periods. Doubling times are respectively i 33 days, during mid to mid ; ii days, during mid to early ; iii 38 days during early to early ; iv days, during early to early Figure 3 shows the variations of the energy price per gigajoule in the period — computed from the Brent Crude spot prices.
One can notice that the cost of one gigajoule of energy has two distinct levels—around 20 USD from to mid and around 10 USD from late to early Oil prices has since collapsed under the coronavirus pandemic, dropping to below 3 USD per gigajoule of energy. However, while large, the rate of change in energy price is several orders of magnitude smaller than the rate of change in the number of hashes. Figure 3. The lower bound of the total energy costs of Bitcoin mining is estimated as the minimum energy cost of each hash multiplied by the total number of hashes computed over a given period of time a day in our case.
Note that this is the lower bound estimate and the actual cost is presumably much larger. The growth in mining costs is affected by both the changes in energy cost see Figure 3 and by the increase in the hashing rate in the Bitcoin network see Figure 2.
We note that the variations in energy cost oscillates in a much narrow band with respect to the changes in the daily number of hashes and therefore, the minimum Bitcoin mining costs Figure 4 mostly mirrors the growth in the total number of hashes. Figure 4. During the last 10 years the Bitcoin network activity has also increased with increasingly larger amount of money transferred daily through the network. Figure 5 reports the total transferred value per day in the Bitcoin network specified in USD.
One can see that the total daily volume of transactions has grown from about one thousand USD in to nearly one billion USD in for an increase by six orders of magnitude. Figure 6 reports the ratio between the daily mining cost C t and daily transaction volume V t. The largest variations occurred in the first few years then, after , the ratio value has stabilized into a plateau with then a jump to a higher plateau at the end of presumably due to the large decrease in Bitcoin price from over 19, USD in December to just a little over 3, USD in December Despite the change in this relation between mining costs and transaction volume in —18 and the change in Bitcoin prices in the same period, we note that in general this ratio is not correlated with the price of Bitcoin.
There is actually a small negative correlation between the two for the daily variations. Using regional electricity prices to calculate the mining costs shows a similar pattern over time, though on a slightly higher level after with the mean ratio being 0. Note that this band of oscillation is within one order of magnitude whereas the underlying quantities C t and V t vary of six orders of magnitude during the same period.
If we limit our analysis to the last period after the end of , we obtain a mean ratio of 0. Figure 6. The band is the region between the first and tenth decile and the center line is the mean value, which is 0. The proof of work allows a network of anonymous and untrustful parties to operate together without central authority control.
It is a powerful instrument to keep a distributed system secure from malicious attacks. However, it has a high cost. We estimate that presently at least a billion USD per year is burned by the Bitcoin network for the proof of work. This amount corresponds to a one million times increase with respect to the costs in Using data from to , this paper quantifies the lower bound for the energy costs of Bitcoin mining and examines the relationship between this bound to the total value of transactions over time.
We reveal that the ratio between mining cost and total transaction volume has not increased nor decreased over the last 10 years despite Bitcoin mining activity having increased by ten billion times during the same period. Such an overall constant ratio is consistent with an argument, introduced by Aste , suggesting that such a ratio must be a sizable fraction of the transaction volume and it corresponds to the minimum fraction that an attacker must double spend to make a profit the quantity p in Equation 2.
This being a lower bound estimate that realistically could be an order of magnitude larger if all extra costs, beside the oil equivalent cost of mining energy, are included. We could therefore conclude that in the Bitcoin network the cost of proof of work is not at all too high.
On the contrary it is actually too low to protect against double spending attacks. However, the proof of work is not the sole mechanism that provides protection of the Bitcoin network. The system also depends upon the high entry barriers in terms of mining hardware and facilities costs. Further, Bitcoin value is built upon community trust so once a majority attack has been detected, the Bitcoin value is likely to collapse together with the potential attacker gains.
Finally, an attack involving a large fraction of the Bitcoin volume would be most likely detected by the network before its completion. Distributed systems and Blockchains can be secured through several other mechanisms that do not require computationally intensive proof of work.
Indeed the proof of work is a mechanism introduced to produce qualified voters in a system of anonymous untrustful parties. Any mechanism that can verify identity of the voters' or that can in any other way avoid uncontrolled duplications of the voters can reduce or eliminate completely the cost and even the need of a proof of work. However, these other mechanisms must relax also some other properties, such as anonymity, openness, or equalitarian distributed verification.
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. TA proposed the research, supervised and contributed to the data collection, performed the data analytics, and co-drafted the paper. Y-DS collected, processed and analyzed the data, and co-drafted the paper.
Both authors gave final approval for publication and agree to be held accountable for the content of the work. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. This article has been released as a pre-print to arXiv as Song and Aste Akcora, C. Andoni, M. Blockchain technology in the energy sector: a systematic review of challenges and opportunities.
Energy Rev. Aste, T. Blockchain technologies: the foreseeable impact on society and industry. Computer 50, 18— Market Capitalization. Google Scholar. Chan, W. Holding bitcoin longer: the dynamic hedging abilities of bitcoin. Finance 71, — Derks, J.
From chaining blocks to breaking even: a study on the profitability of bitcoin mining from to Markets 28, — Gervais, A. Is bitcoin a decentralized currency? IEEE Secur. Grobys, K. Cryptocurrencies and momentum. Kristoufek, L.
Bitcoin and its mining on the equilibrium path. Energy Econ. Bitcoin mining: a global review of energy and power demand. Energy Res.
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|Sports bet brownlow||Full size image. As hardware costs represent a substantial part of the costs side, and electricity prices vary significantly around the globe, we cannot assume that the upper bound is very tight. We estimate in this paper that this hashing activity currently corresponds to an energy cost of around 1 million USD per day and around a billion USD over the past year. A non-PoW permissionless blockchain with a large number of nodes can already exhibit a significantly increased energy consumption due to the high degree of redundancy. Google Scholar. How easily sharding can be achieved largely depends on the consensus mechanism. A more precise estimate could be obtained by applying 2 to all remaining PoW cryptocurrencies.|
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