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Renewable Energy Will Not Solve Bitcoin's Sustainability Problem



In this paper we find that the Bitcoin network, with an electrical energy footprint of 491.4 to 765.4 kWh per transaction on average, is relatively much more energy-hungry than the traditional financial system. Even though it has been argued that renewable energy may help mitigating the environmental impact of this, we find that there exist fundamental challenges in uniting variable renewable energy production with the consistent demand of Bitcoin mining machines. Moreover, we find that the environmental impact of Bitcoin mining reaches beyond its energy use. Continuous increasing energy (cost) efficiency of newer iterations of mining devices ensures that older ones will inevitably be disposed on a regular basis. The resulting electronic waste generation could equal that of a small country like Luxembourg, with a staggering average footprint of four light bulbs worth of electronic waste per processed Bitcoin transaction. Bitcoin will therefore have to address its sustainability problem in another way. This may consist of replacing its mining mechanism with a greener alternative like Proof-of-Stake.
Renewable Energy Will Not Solve Bitcoin’s
Sustainability Problem
Alex de Vries,
Joule Volume 3, Issue 4, P893-898, March 14, 2019. DOI:
In this paper we find that the Bitcoin network, with an electrical energy footprint of 491.4 to 765.4 kWh
per transaction on average, is relatively much more energy-hungry than the traditional financial system.
Even though it has been argued that renewable energy may help mitigating the environmental impact of
this, we find that there exist fundamental challenges in uniting variable renewable energy production
with the consistent demand of Bitcoin mining machines. Moreover, we find that the environmental
impact of Bitcoin mining reaches beyond its energy use. Continuous increasing energy (cost) efficiency
of newer iterations of mining devices ensures that older ones will inevitably be disposed on a regular
basis. The resulting electronic waste generation could equal that of a small country like Luxembourg,
with a staggering average footprint of four light bulbs worth of electronic waste per processed Bitcoin
transaction. Bitcoin will therefore have to address its sustainability problem in another way. This may
consist of replacing its mining mechanism with a greener alternative like Proof-of-Stake.
School of Business and Economics, Vrije Universiteit Amsterdam, The Netherlands
Founder of Digiconomist, Almere, The Netherlands
Bitcoin was introduced as a “peer-to-peer version of electronic cash”, allowing for financial
transactions without the need for a financial institution (or trusted third parties in general). [1]
Bitcoin’s underlying technology, called “blockchain”, is a cryptographically secured distributed ledger,
where these transactions are continuously (and publicly) being recorded. The addition of new (blocks
of) transactions happens in a process called “mining”, where machines are engaging in a competitive
process that involves “scanning for a value that when hashed, such as with SHA-256, the hash begins
with a number of zero bits”. After a node collects new transactions in a block, a nonce in the block is
incremented until a value is found that satisfies the required number of zero bits. The finished block is
broadcasted to the rest of the network, where other nodes express their acceptance by building the
next block on top of it. The creator of a block is rewarded with new coins, as an incentive to support
the network.
In the early days of Bitcoin, mining was done using the central processing units (CPUs) of hardware. By
the end of Bitcoin’s first year (2009) it was realized that mining could also be done using graphic
processing units (GPUs). Just like repetitively generating hashes in mining, video processing is a lot of
repetitive work. To this purpose, GPUs are equipped with more arithmetic logic units (ALUs) than CPUs.
The same ALUs are used in Bitcoin mining to generate SHA-256 hashes. As a result, GPUs mine Bitcoin
faster than CPUs. Not long after (2011), miners started to shift to field programmable gate arrays
(FPGAs). Then, in 2013, miners started using application-specific integrated circuits (ASICs) for mining
Bitcoins. As implied by the name, ASIC chips are hardwired to perform one type of calculation only
(unlike FPGAs which can be reprogrammed to mine anything). This ensures that all resources are
optimized for the task of generating hashes.
Energy Expenses
All of these types of machines require the expense of electricity for the task of generating hashes. We
cannot estimate exactly how much electricity is used for Bitcoin mining, as it is not possible to establish
how many (or which) mining machines are active in the network. It is, however, possible to create an
estimate based off the total computational power in the network, or the total mining reward available
to miners. [2] Both methods are featured in the Bitcoin Energy Consumption Index. [3] The latter shows
that the full Bitcoin mining network consumed at least 40.0 TWh, and possibly as much as 62.3 TWh,
of electrical energy over the full year of 2018 (Figure 1). This is comparable to the amount of electricity
consumed by countries like Hungary (40.3 TWh) and Switzerland (62.1 TWh). [4]
Figure 1: Cumulative Minimum and Estimated Bitcoin Mining Network Electricity Consumption in 2018 Based on the
Bitcoin Energy Consumption Index (
Of course, Bitcoin is not a country, and a better perspective of Bitcoin’s energy requirement can
therefore be obtained by comparing it to that of the traditional financial institutions. McCook
estimated that the entire banking sector could be consuming as much as 650 TWh of energy per year.
[5] Critically, this number includes not just the data centers that process transactions, but also
branches and ATMs. At the same time, we are only considering the energy use by Bitcoin mining, while
the digital currency (contrary to the original purpose of Bitcoin) has spurred the development of Bitcoin
ATMs and a new range of trusted third parties. This includes exchanges, wallets and payment solution
providers. More than 80% of transactions occurring on the network now have a counter-party that is
a third-party service. [6]
Focusing purely on data centers, it can be found that all of the world’s data centers were estimated to
consume 194 TWh of electricity in 2014, with an expected growth of only 3% to 200 TWh in 2020. [7]
It is unknown what share is used by the financial sector, but we can establish that the facilities used
for Bitcoin mining already require at least (40 TWh / 200 TWh) 20% of this amount. On top of this, the
financial sector is significantly bigger than the Bitcoin network. Bitcoin processed only 81.4 million
transactions in 2018. [8] This means the average electricity footprint per unique transaction ranges
from (40 TWh / 81.4M) 491.4 kWh to (62.3 TWh / 81.4M) 765.4 kWh. The global banking industry, by
contrast, is processing 482.6 billion non-cash transactions per year. [9] The average electricity footprint
for processing these transactions can only be (200 TWh / 482.6B) 0.4 kWh at most.
Environmental Impact
Even though the Bitcoin network can thus be considered extremely energy-hungry, we also need to
consider the environmental impact this causes. According to the Bitcoin Energy Consumption Index,
Bitcoin’s energy use in 2018 translates to a carbon footprint of 19.0 to 29.6 million metric tons of CO2
(475 gCO2/kWh). [3] The average carbon footprint per transaction would then range from 233.4 to
363.5 kg of CO2. By comparison, the average carbon footprint for a VISA transaction equates to 0.4 g
of CO2, while a Google search is the equivalent of 0.8 g. [10] But proponents of the digital currency
argue that the ultimate environmental impact is limited. Their primary argument is that the majority
of Bitcoin mining is mainly powered by what would otherwise be a wasted surplus of renewable
energy. [11] Although miners may indeed be able to take advantage of cheap quantities of
hydropower, limited environmental impact is not a foregone conclusion. To understand why it is not,
let us first consider the economics of Bitcoin mining and its consequences for energy demand.
Economics of mining
Within the Bitcoin network, all of the participating mining machines are competing with each other for
the reward of generating a new block for Bitcoin’s underlying blockchain. At the current time, this
reward consists of 12.5 Bitcoins per block, plus any fees that were included in the processed
transactions. Regardless of the total computational power in the network, new blocks are generated
non-stop and every 10 min on average. The network self-adjusts the difficulty of generating a block
after every 2,016 blocks, ensuring a steady production.
Adding new computational power to the network therefore does not increase the total size of the
rewards, but primarily changes the distribution. As the chance of creating a new block for the
blockchain is proportional to one’s share of the total computational power, each newly added mining
unit marginally dilutes the expected income of all others. This effect can be clearly observed when we
look at the performance over time of several ASIC mining machines since their release (Figure 2A). As
the total network computational power increases, the number of Bitcoins a single one of these
machines is expected to mine per day tends to decline rapidly.
Figure 2: Time Series of Bitcoin ASIC Miner Income and Profitability Per Day of the Antminer S4, S5, S7, and S9 Mining
Machines Since Their Release. Antminer machines are produced by Bitmain, the biggest manufacturer of Bitcoin ASIC miners.
(A) Bitcoin’s total computational power (hashrate) in TH/s versus the amount of BTC that can be mined per day for a single
unit of a specific ASIC miner. The Antminer S4, S5, S7, and S9 have advertised hashrates of 2, 1.155, 4.73, and 14 TH/s,
respectively. (B) Profitability per day for a single unit of a specific ASIC miner. To determine profitability in USD, the average
daily rewards in BTC are multiplied with the USD exchange rate for that day. The cost of power is assumed to be 5 cents per
kWh. The Antminer S4, S5, S7, and S9 have an advertised power requirement of 1,380, 590, 1,293, and 1,372 W, respectively.
Source: and Bitmain.
In such an environment, miners can only compete in terms of cost efficiency. Since mining machines
require energy for the task of generating hashes, the efficiency of this hardware is determined by the
amount of electricity required to complete a certain amount of computations. The more computations
per unit of energy, the more profitable a machine can be. This has caused a rat race to develop more
efficient mining hardware, and explains why Bitcoin mining is now done with ASICs rather than CPUs.
As “market forces drive the industry toward an equilibrium whereby firms will earn zero economic
profit [2], we expect that only the most cost-efficient machines can remain economically viable for
mining. A rational agent will shut down a less efficient machine once its energy costs exceed the value
of the Bitcoin generated with it. Before this point is reached, time is of the essence, and machines will
have to run non-stop to generate the maximum profit (and to have the best odds of earning back the
money invested in the machines in the first place). This also means that mining machines will have a
constant energy demand at every time of the day throughout the year, increasing the baseload
demand on a grid.
Challenges in uniting renewable energy with Bitcoin mining
As the most cost-efficient machines will generate the biggest profits, agents are incentivized to not
just use the most efficient hardware, but also to seek out the cheapest electricity. According to
Coinshares, one popular area for such cheap electricity is the province of Sichuan in China. It is
suggested that 48% of the global mining capacity is now situated here. [11]
The southwest of China is capable of producing large amounts of hydropower, while local demand is
substantially lower. Unfortunately, “China’s grid infrastructure is currently a bottleneck for renewable
power generation”. [12] Because of insufficient grid penetration and a lack of high-quality grid
infrastructure, the power export capacity of the region is also limited. This leaves the Sichuan and
Yunnan provinces with an abundance of hydropower, which lures in energy-hungry and polluting
industries trying to take advantage of the low rates. Bitcoin mining is one these industries.
Unlike the power demand of Bitcoin mining machines, which is consistent all year long, the production
of hydropower is subject to seasonality. In an extensive report, China Water Risk (CWR) explains that
“hydroelectricity cannot be generated year-roundbecause of “variations in water availability through
rain/floods/droughts”. [12] Production of hydropower is high wet season is during the summer months
and low in the dry season during the winter months. As a result, seasonal variability in hydropower is
already higher than 30%, and expected to increase further because of climate change.
In Sichuan specifically “the average power generation capacity during the wet season is three times
that of the dry season. These fluctuations in hydroelectricity generation need to be balanced out with
other types of electricity. CWR adds that this “is usually coal”, and as a consequence, this renewable
option is “not technically ‘100% green’”. It should thus be no surprise that the carbon emission factor
of purchased electricity in Sichuan ranges from 265 to 579 gCO2/kWh, depending on the chosen
method. [13] This is more comparable to the GHG emissions of generating electricity from natural gas
(469 gCO2/kWh), than it is to the GHG emissions of generating hydropower (4 gCO2/kWh). [14]
The former reveals the challenges in uniting “green” renewable energy with Bitcoin mining. Miners
may indeed be able to take advantage of (temporary) excesses of hydroelectricity, but they effectively
increase the baseload demand on a grid throughout the year. This demand has to be met with energy
from alternative sources, when seasonality causes production of this renewable energy to fall. In the
worst-case scenario, it presents an incentive for the construction of new coal fired power stations to
fulfil this purpose.
Environmental Impact Beyond Energy Use
The previously described challenge is not the only challenge in trying to address Bitcoin’s sustainability
problem with renewable energy. One thing that renewable energy cannot solve at all for Bitcoin’s
environmental footprint, is what happens to the mining machines once they reach the end of their
economic lifetime. For ASIC mining machines there is no purpose beyond the singular task they were
created to do, meaning they immediately become electronic waste (e-waste) afterward. To estimate
the total e-waste potential of the Bitcoin network, we need to determine first the quantity of mining
equipment in the network and, second, the rate at which this equipment becomes obsolete.
As mentioned before, there is no way to determine the exact composition of the Bitcoin network. Since
we can estimate the total computational power in the network, we can use this to derive a quantitative
estimate of the total mining equipment. We can observe that at its peak (in October 2018) the Bitcoin
network was estimated to process around 54.7 exahashes per second (Figure 1). We can subsequently
establish that it would require at least 3.91 million Antminer S9 machines, with an advertised output
of 14 terahashes per second, to produce that amount of computational power. The combined weight
of these machines would amount to 16,442 metric tons. This number represents the minimal quantity
of mining equipment in the network, as the Antminer S9 had the least amount of weight per unit of
computational power at this time (Table 1).
Table 1: Examples of Bitcoin ASIC Miner Machine Types. Source: Bitmain, Bitfury, and Canaan.
With the release of the new (more cost-efficient) Antminer S15 in December 2018, we can expect all
of this equipment to become obsolete in the very near future. The recent drop in total network
computational power (Figure 2A) following a decreasing Bitcoin price and mining machine profitability
(Figure 2B), suggests that this process is well underway. From October to December 2018 the total
computational power in the network decreased by 19.9 exahashes per second, meaning at least 5,973
metric tons of mining equipment were removed from the network. Although this does not mean they
were immediately disposed.
In general, we can expect mining equipment to become obsolete in roughly 1.5 years, which would
follow from Koomey’s law, and the observation that only the most cost-efficient machines can remain
economically viable for mining. Koomey et al. observed that the electrical efficiency of computing
(the number of computations that can be completed per kilowatt-hour of electricity)” has “doubled
about every 1.5 years” over a period of 65 years. [15] The developments in Bitcoin ASIC mining
equipment have easily kept up with this pace (Table 1).
If Bitcoin cycles through 16,442 metric tons of mining equipment every 1.5 years, the annualized e-
waste generation would amount to 10,948 metric tons. This amount of e-waste is comparable to the
total e-waste generated by a country like Luxembourg (12kt). [16] Moreover, it amounts to a staggering
average footprint of 134.5 g per transaction processed on the Bitcoin network in 2018 (81.4 million).
This is as heavy as two “C” size batteries (130 g) or four standard sixty-watt light bulbs (136 g).
We do not know the amount of e-waste generated by the banking sector, but we can find that a
financial institution like VISA has a significantly lower e-waste output. VISA does not disclose its exact
e-waste production, but provides that it has two data centers for processing its transactions. [10] The
largest one consists of seven independent physical pods, containing 376 servers, 277 switches, 85
routers and 42 firewallseach. [17] We can assume that the total equipment in each of these pods
weighs around 40 metric tons (putting the weight of a single server over 100 kg). The combined weight
for all pods would then amount to 280 metric tons. Even though the second data center is only half
the size of the first one, we assume an equal amount of equipment. This brings the total equipment
estimate for both of VISA’s data centers to 560 metric tons. If we then assume this equipment would
be replaced in full every year, the average e-waste footprint per processed transaction (124.3 billion
in total for 2018 [18]) would still only amount to 0.0045 g.
It is important to note that even though price movements may lengthen or shorten the economic
lifetime of a specific ASIC miner machine type (the effects of price movements on machine profitability
can be observed in Figure 2B), Bitcoin’s e-waste generation would still continue even under a stable
price because of continuous hardware efficiency improvements. The latter ensures that older
hardware will inevitably be disposed on a regular basis.
The discussed method with regard to Bitcoin’s e-waste generation only considers the e-waste output
resulting from mining equipment being disposed. Other equipment present in mining facilities, like
cooling, has not been taken into consideration.
Another potential limitation is that there exist other types of transactions, which are not directly
recorded on the Bitcoin blockchain. As the amount of transactions processed this way is not known,
these could not be accounted for. Off-chain transactions include transactions that are processed
internally by trusted third parties in the Bitcoin ecosystem. The very existence of this type of
transaction within the Bitcoin ecosystem is counter-intuitive, as Bitcoin was created to “allow online
payments to be sent directly from one party to another without going through a financial institution”.
[1] The Bitcoin community is therefore developing second layer protocols like the so-called Lightning
Network, which would allow for off-chain transactions without the need for a trusted intermediary.
This could reduce the environmental footprint per transaction.
Given the fundamental challenges in uniting Bitcoin mining with renewable energy, along with the fact
that energy use is not the only way in which Bitcoin impacts the environment, we should conclude that
renewable energy is not the answer to Bitcoin’s sustainability problem. Alternatives to Bitcoin’s mining
mechanism, such as Proof-of-Stake, are already available and used by an array of alternative
cryptocurrencies (e.g. Dash and NXT). In these systems, participating machines do not have to use their
computing power. This prevents both extreme energy use, as well as the incentive to develop
specialized (singular purpose) hardware, and showcases that blockchain technology does not
necessarily have a significant environmental impact. What is left is for Bitcoin to follow the example
set by others.
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[2] de Vries A. Bitcoins Growing Energy Problem. Joule 2018;2:8015.
[3] Digiconomist. Bitcoin Energy Consumption Index. Digiconomist n.d.
[4] International Energy Agency. Key world energy statistics 2017 n.d.
[5] McCook H. An Order-of-Magnitude Estimate of the Relative Sustainability of the Bitcoin
NetworkAn Order-of-Magnitude Estimate of the Relative Sustainability of the Bitcoin Network
[6] Forbes. How Chainalysis Helps Solve Crimes: Jonathan Levin Tells All - Ep.62. n.d.
[7] International Energy Agency. Digitalization and Energy 2017 n.d.
[8] Total Number of transactions. BlockchainCom n.d.
[9] Capgemini, BNP Paribas. World Payments Report 2018. n.d.
[10] VISA. Sustainability & the Environment n.d.
[11] Bendiksen C, Gibbons S, Lim E. The Bitcoin Mining Network. Coinshares 2008.
[12] China Water Risk. Towards A Water Energy Secure China 2015.
[13] Qu S, Liang S, Xu M. CO2 Emissions Embodied in Interprovincial Electricity Transmissions in
China. Environ Sci Technol 2017;51:10893902. doi:10.1021/acs.est.7b01814.
[14] Moomaw W, Burgherr P, Heath G, Lenzen M, Nyboer J, Verbruggen A. Methodology. In: von
Stechow C, Hansen G, Seyboth K, Edenhofer O, Eickemeier P, Matschoss P, et al., editors.
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Cambridge: Cambridge University Press; 2011, p. 9731000.
[15] Koomey J, Berard S, Sanchez M, Wong H. Implications of Historical Trends in the Electrical
Efficiency of Computing. IEEE Ann Hist Comput 2011;33:4654. doi:10.1109/MAHC.2010.28.
[16] Baldé CP, Forti V, Gray V, Kuehr R, Stegmann P. The Global E-waste Monitor 2017 n.d.
[17] Kontzer T. Inside Visas Data Center. Netw Comput 2013.
[18] VISA. Annual Report 2018. n.d.
... In the current state of mining hardware, they become obsolete after roughly every 2 years due to continuous advancements in the energy efficiency of miners and have no use beyond bitcoin mining. The annualized e-waste generated from bitcoin mining could amount to 10,948 t. 60 ...
... They used the network hash rate as a starting point to estimate the whole Bitcoin network's energy consumption. Alex de Vries started by explaining the Bitcoin network as severely energy-hungry to predict an incrementing power demand for the future [44] . De Vries calculates indicated that the network has to perform 8.7 quintillion hashes at the current mining circumstances for processing one transaction [6] . ...
Full-text available
An unprecedented emergence has occurred for the cryptocurrencies among enterprises, customers, and investors as a result of the growing number of internet connections worldwide. The most popular cryptocurrency is Bitcoin representing the rise of digital payment systems. Though, harsh criticism has been also created for cryptocurrencies about their environmental sustainability and power consumption, decelerating the acceptance of bitcoin by consumer as a means of payment. The ecological impact or footprint of a process is determined mainly through life-cycle-assessment (LCA) quantifying all material flows’ inputs and outputs for a process or product and their effect on the environment. This study provides LCA-based framework to show the environmental impacts of Bitcoin mining from top ten miner countries (China, USA, Kazakhstan, Russia, Iran, Malaysia, Canada, Germany, Ireland, Norway). The results show that with the share of 53.3% of the world’s mining, China has the most negative environmental impact specially in marine ecotoxicity with 26.8 kg 1,4-DCB and human health with 0.0043 DALY but with the equal mining ratio Germany and Kazakhstan have the most negative environmental impacts.
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What do we know about the interrelations between economic inequality, ecology and the increased use of Bitcoin? The aim of the paper was to empirically test the relationship between economic and ecological effects related to the increase in Bitcoin’s network hashrate in a selection of countries that have the highest influx of crypto-mining. To test these three types of relationships, I collected a dataset concerning Bitcoin indicators, economic indicators and ecological indicators that were obtained from multiple trustworthy sources: OECD, World Bank, Fred Data, World Inequality Database (WID). Handling the data challenges, I used this unique panel dataset to explore the relationship between Bitcoin’s hashrate and two types of outcomes: (i) economic outcomes (such as the GDP which as we know relates to inequalities through the Kuznets curve) or direct measures of inequality (such as, income inequality (GINI) and the share of people with top 1% of income and 1% of wealth), and (ii) ecological outcomes (such as carbon emissions, carbon footprint and electronic waste). I found that the Bitcoin currency associates with certain redistribution of wealth, but the accumulation of crypto-currency-related wealth itself remains still concentrated in the wealth of the top 1%. Also, there is evidence for certain nonlinearities in the relationships with the ecological degradation, echoing the concept of the Kuznets curve.
The present study proposes to investigate the influence of the covid-19, on the adjusted closing price of the digital currency based on energy consumption during the process of mining. The study employed the secondary data analysis of top ten market capitalization of cryptocurrencies with the combination of high energy consume mechanism (proof of work) and low energy consume mechanism (proof of stake). Statistical tools like Descriptive analysis,Augmented Dickey-Fuller (ADF) test, ARCH, and GARCH models were used in the study. The present study finds that the prices of cryptocurrencies were highly volatile. This study could assist investors towards better understanding of the dynamics of the cryptocurrency market based on energy consumption which helps them to make more effective decisions, on investing cryptocurrencies with a scientific approach.
Studying the antecedents and future challenges of blockchain is the major goal of this chapter. This contribution examines some of the heuristics that seem to persist in the collective consciousness, especially the ones commonly associated with blockchain as a cryptocurrency, as a potential source of energy/environmental imbalances, and as a tool for cybersecurity. The goal of this chapter is to highlight that these heuristics do not fully capture the complex implications of blockchain, and the technology should be viewed as having a much broader spectrum of implications for economic activity and society. While the disruption of financial institutions by cryptocurrencies and the decentralization of transactions remain prominent in the minds of many observers and commentators, the potential of blockchain technology goes far beyond these features, along with consequences that may be counterintuitive at first glance. This chapter explores the limitations and challenges of blockchain technology, providing a more complete understanding of its potential impact.
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Human–Computer Interaction (HCI) researchers have increasingly been questioning computing’s engagement with unsustainable and unjust economic growth, pushing for identifying alternatives. Incorporating degrowth, post-development, and steady-state approaches, post-growth philosophy offers an alternative not rooted in growth but in improving quality of life. It recommends an equitable reduction in resource use through sensible distributive practices where fulfillment is based on values including solidarity, cooperation, care, social justice, and localized development. In this paper, we describe opportunities for HCI to take a post-growth orientation in research, design, and practice to reimagine the design of sociotechnical systems toward advancing sustainable, just, and humane futures. We aim for the critiques, concerns, and recommendations offered by post-growth to be integrated into transformative HCI practices for technology-mediated change.
Whereas prior studies quantify absolute measures and correlational tests in Bitcoin’s greenhouse gas (GHG) emissions, the elasticity behaviour of Bitcoin’s pollution is yet to be understood. To inquire whether Bitcoin’s environmental risk is inflated, the study borrows the economics method of time series decomposition and accepts carbon dioxide (CO\(_2\)) emission as a proxy for environmental pollution. The empirical objective of the study is to quantify the elasticity of GHG emissions with respect to Bitcoin electricity usage in total and disaggregated by fossil fuels. The results show that total electricity has an elasticity of less than one, while natural gas and oil have an elasticity of greater than one for both short and long runs. Also, all fossil fuels have an elasticity coefficient greater than one in the long run. The results reveal that assessing the Bitcoin environmental impact in aggregated electricity data is not a good idea. Another revelation is that even though coal energy is a higher pollutant, CO\(_2\) emissions are less responsive to Bitcoin’s coal usage than oil and natural gas. These results need to be digested with caution due to the data limitation problem that leads to using average shares to desegregate total electricity time series. The outcome of this study should empower environmental activists, policymakers, and sustainable investment investors with practical information.
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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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The electrical efficiency of computation has doubled roughly every year and a half for more than six decades, a pace of change comparable to that for computer performance and electrical efficiency in the microprocessor era. These efficiency improvements enabled the creation of laptops, smart phones, wireless sensors, and other mobile computing devices, with many more such innovations yet to come. The Web Extra appendix outlines the data and methods used in this study.
Existing studies on the evaluation of CO2 emissions due to electricity consumption in China are inaccurate and incomplete. This study uses a network approach to calculate CO2 emissions of purchased electricity in Chinese provinces. The CO2 emission factors of purchased electricity range from 265 g/kWh in Sichuan to 947 g/kWh in Inner Mongolia. We find that emission factors of purchased electricity in many provinces are quite different from the emission factors of electricity generation. This indicates the importance of the network approach in accurately reflecting embodied emissions. We also observe substantial variations of emissions factors of purchased electricity within sub-national grids: the provincial emission factors deviate from the corresponding sub-national-grid averages from -58% to 44%. This implies that using sub-national-grid averages as required by Chinese government agencies can be quite inaccurate for reporting indirect CO2 emissions of enterprises' purchased electricity. The network approach can improve the accuracy of the quantification of embodied emissions in purchased electricity and emission flows embodied in electricity transmission.
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.
An order-ofmagnitude estimate of the relative sustainability of the Bitcoin Network
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