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Implausible projections overestimate near-term Bitcoin CO 2 emissions

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https://doi.org/10.1038/s41558-019-0535-4
1Department of Mechanical Engineering, Northwestern University, Evanston, IL, USA. 2Department of Chemical and Biological Engineering, Northwestern
University, Evanston, IL, USA. 3Energy Analysis & Environmental Impacts Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. 4Faculty of
Management, Science & Technology, Open University of the Netherlands and Radboud University, Nijmegen, The Netherlands. 5Rocky Mountain Institute,
Basalt, CO, USA. 6Ericsson Research, Ericsson AB, Stockholm, Sweden. *e-mail: eric.masanet@northwestern.edu
Bitcoin mining is an increasingly energy-intensive process13 for
which the future implications for energy use and CO2 emissions
remain poorly understood. This is in part because—like many IT
systems—its computational efficiencies and service demands have
been evolving rapidly. Scenario analyses that explore these implica-
tions can therefore fill pressing knowledge gaps, but they must be
approached with care. History has shown that poorly constructed
scenarios of future IT energy use (often a result of overly simplistic
extrapolations of early rapid growth trends) can spread misinfor-
mation and drive ill-informed decisions46. Indeed, the utility of an
energy demand scenario is proportional to its credibility, which is
demonstrated through careful attention to technology characteris-
tics and evolution, analytical rigour and transparency, and design-
ing scenarios that align with plausible future outcomes.
While we believe that Mora etal. had the right motivations in
developing Bitcoin CO2 emissions scenarios7, we respectfully argue
that their scenarios lack such credibility. We arrived at our conclu-
sion by replicating Mora and colleagues’ methods in detail, which
revealed key flaws in the design and execution of their analysis (as
documented in the Supplementary Information). We describe the
five most important issues below.
First, the use of transactions as the driver of future Bitcoin emis-
sions is questionable, given the tenuous correlation between trans-
actions and mining energy use. It is well established that energy use
is driven by the computational difficulty of the blocks mined13,
whereas the number of transactions per block can evolve (for exam-
ple, via SegWit)8 with no direct effect on block mining difficulty.
The authors themselves7 calculate Bitcoin energy use and emissions
in 2017 on the basis of block difficulty, not the number of trans-
actions (Supplementary Equation (1)). Without explanation, the
authors switch to transactions as the driver for projecting future
emissions, undermining their methodological consistency and the
integrity of their projections.
Second, all three Bitcoin adoption scenarios designed by Mora
etal. represent sudden and improbable departures from historical
trends in Bitcoin transactions; over the preceding five years annual
growth ranged from 1.3× to 2.3× (Supplementary Figs. 3 and 4)9.
Specifically, Mora et al. assume that Bitcoin transactions—which
totalled 104 million in 2017, representing a mere 0.03% of global
cashless transactions—would abruptly leap to 78 billion by 2019 in
the fast scenario (a 750× increase in only 2 yr), to 11 billion by 2020 in
the median scenario (a 108× increase) and to 8 billion by 2023 in the
slow scenario (a 76 × increase). All three adoption scenarios follow
steep logarithmic growth trajectories thereafter, which are conspicu-
ously inconsistent with historical trends (Supplementary Fig. 4) and
mathematically can only lead to large near-term emissions increases.
The authors base their scenarios on adoption rates of 40 arbitrarily
selected technologies, the social utilities of which vary widely. The
authors do not explain why such comparisons are valid, nor do they
justify the plausibility of the very abrupt changes in Bitcoin transac-
tion levels and growth trajectories that result from such comparisons.
Third, Mora etal. applied outdated values for mining rig efficien-
cies and electric power CO2 intensities, which inflated their estimated
2017 Bitcoin energy use and CO2 emissions values considerably. When
estimating the direct electricity use of Bitcoin mining, the authors
included in their selection pool many old and inefficient rigs that
were no longer economically viable in 2017 (Supplementary Fig. 5).
Furthermore, Mora etal. provided equal weighting when selecting
a rig from their pool as the sole rig type to mine a block, thus over-
representing slower, inefficient rigs and creating scenarios that require
physically impossible rig counts. When we excluded unprofitable rigs
in our replicated analysis, Mora and colleagues’ model produced an
estimate of 28 TWh in 2017 (Supplementary Fig. 6), which is one-
quarter of their original estimate of 114 TWh. Furthermore, they
applied 2014 CO2 intensities (in gCO2 kWh1) to calculate 2017 emis-
sions, ignoring non-negligible grid decarbonization improvements
in the intervening years (Supplementary Fig. 7)10, despite sufficient
data being available at the time of their study for reasonable estimates
of 2017 power mixes11,12. Applying more reasonable 2017 electricity
use and CO2 intensity values in their model produced an estimate of
15.7 MtCO2e, far lower than their original estimate of 69 MtCO2e.
Fourth, by analytical design, Mora etal. applied 2017 per-trans-
action energy use and CO2 emissions values in all future years,
multiplied by annual transactions (Supplementary Equation (2)).
This decision effectively held both mining rig efficiency and grid
CO2 intensities constant for the next 100 yr (Supplementary Fig. 7).
This unprecedented choice ignores the dynamic nature of mining
rig and power grid technologies and violates the widely followed
practice of accounting for technological change in forward-looking
energy technology scenarios10,11. In acknowledging their static grid
intensity assumption, they point to at least one reference containing
credible grid intensity outlooks10 but failed to make use of them.
Estimating the future energy efficiency of mining is certainly more
difficult, but the authors do not explain why they simply ignored this
Implausible projections overestimate near-term
Bitcoin CO2 emissions
Eric Masanet 1,2*, Arman Shehabi3, Nuoa Lei1, Harald Vranken4, Jonathan Koomey5 and
Jens Malmodin6
arising from Mora, C. etal. Nature Climate Change https://doi.org/10.1038/s41558-018-0321-8 (2018)
NATURE CLIMATE CHANGE | VOL 9 | SEPTEMBER 2019 | 653–654 | www.nature.com/natureclimatechange 653
Content courtesy of Springer Nature, terms of use apply. Rights reserved
... The idea is simple: a higher number of transactions involving Bitcoin and Ether increases significantly the energy/electricity required to validate these transactions. Such perspective has been questioned by some technical works (Dittmar and Praktiknjo, 2019;Masanet et al., 2019;Schinckus et al., 2020Schinckus et al., , 2021 explaining that the number of transactions can actually be increased in a block computationally validated by the blockchain technology. These studies suggested (but did not provide empirical evidence) to study the electricity consumed by the blockchain technology in relation to the dynamics of its hashrate (i.e. the network's computational power) required to validate these transactions and not in relation to the trading volumethe novelty of this article is to provide an empirical analysis about the potential link between hashrate and energy consumption. ...
... validation of a block of transactions) are strongly related to the hashrate, as the higher hashrate, the higher computational power required for a new block to be mined\validated is. In other words, a higher hashrate implies a higher electricity consumption (Dittmar and Praktiknjo, 2019;Masanet et al., 2019) so that the baseline function can be summarized as follows: ...
... The existing literature acknowledges that an increasing number of transactions (trading volume) dealing with Bitcoin and Ether have an environmental impact through the high consumption of electricity required to validate these transactions. This perspective has been questioned by some technical works (Dittmar and Praktiknjo, 2019;Masanet et al., 2019;Schinckus, 2020 claiming that the validation (mining) is not operated on the transactions themselves but instead at the computational block level (each block compiling several transactions). Given the fact that the number of transactions can be increased in a block, a more sophisticated analysis is required. ...
Article
Purpose Given the growing importance of cryptocurrencies and the technique called “SegWit” that allows to compile more transactions in a mined block, the electricity consumed per block might potentially decrease. The purpose of this study is to consider that the difficulty to mine a block might be a better indicator of the Bitcoin\Ether’s electricity consumption. Design/methodology/approach This study applies the vector error correction model to investigate data related to primary energy consumption and electricity production, supply and consumption for Bitcoin and Ether hashrates from 2016M1 to 2021M5. Findings The hashrate (difficulty of solving the cryptographic problem related to the validation of a transaction) is found to have a positive cointegration with energy and electricity consumption. Despite the launch of the Segregation Witness (SegWit) mechanism allowing blocks to handle a higher number of transactions per block, this Bitcoin and Ether growing need in electricity has significantly been increasing since October 2019. Originality/value The major contribution of this study is to investigate a more relevant indicator, namely, hashrate (computational difficulty to solve cryptographic enigma associated with cryptocurrencies-related transaction). The approach of this study can be justified by the fact that there exists a technical solution consisting in increasing the number of transactions per blocks so that less electricity might be required to validate a transaction.
... Mora et al. (2018) estimated that the 2017 carbon footprint of Bitcoin reached 69 million metric tons of CO 2 equivalent (MtCO 2 e), forecasting a violation of the Paris COP21 UNFCCC Agreement 2 by 2040 due to Bitcoin's cumulative emissions alone. At the heart of the controversy sparked, with various contributions revising downward the projections obtained by Mora et al. (2018) (e.g., Houy 2019Masanet et al. 2019;Stoll et al. 2019), lies the difficulty in measuring the power consumption of the Bitcoin mining network and the associated carbon emissions (De Vries 2018. Bitcoin miners are globally geo-located, facing very different energy costs, and employ hardware with unknown energy intensities. ...
... 5 Relative to the aforementioned literature, the reported point estimates (and PIs) also represent a downward revision of the results reported by Mora et al. (2018) and are broadly in line with figures from Foteinis (2018), reporting global emissions for Bitcoin and Ethereum for 2017 of 43.9 MtCO 2 , or from Stoll et al. (2019), reporting annual carbon emissions for Bitcoin mining in 2018 in the range from 22.0 to 22.9 MtCO 2 . Our estimates further revise downward the 2017 estimates provided by Houy (2019) or Dittmar and Praktiknjo (2019), reporting 15.5 MtCO 2 e for 2017, or those from Masanet et al. (2019), who reported, for 2017, an estimate of 15.7 MtCO 2 e. What makes them nevertheless worrying is recent evidence, e.g., from integrated weather-climate models (CMIP6), feeding into the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) 2021 reported in Williams et al. (2020). ...
... A deep neural network with rectified linear unit activation functions (ReLU DNN) exploits a comprehensive set of inputs to (i) estimate the Bitcoin mining carbon footprint associated with a realistic level of electricity consumption and energy efficiency (Stoll et al. 2019) as target output and (ii) assess its statistical reliability, conveyed by 95% prediction intervals (PIs) (see Gal and Ghahramani 2016). For a comparison with the literature (Mora et al. 2018; Houy 2019; Masanet et al. 2019;De Vries 2018, the current "top-down" approach to the output target construction is presented first and evaluated with "clean energy" carbon intensities (Stoll et al. 2019), to then present our novel (partial) "bottom-up" techno-economic approach. ...
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Building on an economic model of rational Bitcoin mining, we measured the carbon footprint of Bitcoin mining power consumption using feed-forward neural networks. We found associated carbon footprints of 2.77, 16.08 and 14.99 MtCO2e for 2017, 2018 and 2019 based on a novel bottom-up approach, which (i) conform with recent estimates, (ii) lie within the economic model bounds while (iii) delivering much narrower prediction intervals and yet (iv) raise alarming concerns, given recent evidence (e.g., from climate–weather integrated models). We demonstrate how machine learning methods can contribute to not-for-profit pressing societal issues, such as global warming, where data complexity and availability can be overcome.
... Old data shows that the electricity consumption caused by Bitcoin calculation alone (138 TWh) exceeds the sum of lighting and television in the United States (60 & 60 TWh), and also exceeds the national electricity consumption of Ukraine and Norway [6]. In 2018, Masanet simulated the increase in electricity growth caused by Bitcoin mining and believed that the popularity of Bitcoin would lead to uncontrollable global temperature changes [7]. Krause and Tolaymat provided an estimated range from 3 to 15 Mt CO 2 for the first half of 2018 [8]. ...
... However, due to the large number of high hash rate/power ASIC mining machines, the power consumption of Bitcoin accounts for only 58% of all cryptocurrencies (as of September 2021). This study puts the previous estimates of the power consumption of cryptocurrencies (all Bitcoin) and the results of this article into Figure 5 [6][7][8][10][11][12][13][20][21][22][23][24]. As shown in the figure, before 2019, the growth trend of Bitcoin was generally in line with the expectations of researchers. ...
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The energy consumption and carbon footprint of cryptocurrencies have always been a popular topic. However, most of the existing studies only focus on one cryptocurrency, Bitcoin, and there is a lack of long-term monitoring studies that summarize all cryptocurrencies. By constructing a time series hash rate/power model, this research obtained the 10-year time series data on energy consumption dataset of global top-25 cryptocurrencies for the first time. Both the temporal coverage and the spatiotemporal resolution of the data exceed previous studies. The results show that Bitcoin’s power consumption only accounts for 58% of the top-25 cryptocurrencies. After China bans cryptocurrencies, the conservative change in global CO2 emissions from 2020 will be between −0.4% and 4.4%, and Central Asian countries such as Kazakhstan are likely to become areas of rapid growth in carbon emissions from cryptocurrencies.
... On the contrary, several studies have asserted that cryptocurrency mining will not significantly contribute to the global CO 2 emission levels because the main mining centers are located in areas with plentiful and affordable renewable energy. Masanet et al. (2019) argue that there are some flaws in the calculations by Mora et al. (2018), and results of CO 2 emissions leading to an increase in global temperature are not that catastrophic. Furthermore, a new report from financial services company Square Bitcoin Clean Energy Initiative (2021) claims that Bitcoin miners are unique energy buyers who require cheap and abundant energy sources. ...
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This book aims to fill the literature gap on digital instruments and FinTech in enhancing green finance. Technological innovation can increase transparency, accountability, and speed, decentralize the financial system, improve risk management, increase competition, lower costs, improve efficiency, increase cross-sectoral collaboration and integration, and scale up green finance. Artificial intelligence (AI), distributed ledger technologies (DLT) or blockchain, peer-to-peer lending platforms, big data, Internet-based and mobile-based payment platforms, Internet of Things (IoT), matchmaking platforms including crowdlending, tokenizing green assets are potential means to scale up the green finance for achieving the SDGs. The COVID-19 pandemic, the economic downturns, and the uncertainties shrank the new investments in renewable energy projects globally. Low investment in renewable energy projects could threaten the expansion of green energy needed to provide energy security and meet SDG7 and SDG13. Investments in renewable energy projects are scarce because of several risks and a low rate of return. Although several new green financing solutions such as green bonds, green banks, green credit guarantee, carbon taxation, carbon trade, village funds, and community trust funds have been established in different countries, these are insufficient, and alternative ways to finance projects are required. The book provides several high-quality studies on utilizing digitalization, FinTech, financial innovations, and other new technologies to fill the finance gap of green projects to meet the SDG goals. The chapters are written by scholars in diverse countries and regions and include practical policy recommendations.
... In 2018, Mora et al. [24] claimed that bitcoin emissions could push global warming above two centigrades. The analysis and results of the paper by Mora et al. have been debunked at least by Houy [25], Masanet et al. [26], and Dittmar et al. [27]. According to Houy, rational mining limits Bitcoin emissions, and the average of a list of 62 ASIC miners used by Mora De Vries [28] estimated in 2018 that the Bitmain company, with a claimed market share of 70%, could produce up to 6.5 million bitcoin mining machines (Antminer S9) in 2018. ...
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Bitcoin miners consume a reasonable amount of energy-and it’s all worth it
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