mrb's blog

Serious faults in Digiconomist's Bitcoin Energy Consumption Index

Keywords: beci critic debunking bitcoin mining electricity energy estimation

The author of the Bitcoin Energy Consumption Index makes fundamentally flawed assumptions, causing it to demonstrably overestimate the electricity consumption of Bitcoin miners by 1.5× to 2.8×, and likely by 2.2×.

BECI starts with calculating average mining revenues based on a 439-day (variable) moving average of the Bitcoin and Bitcoin Cash prices. Then BECI assumes a fixed 60% share of these revenues are spent on electricity costing $0.05/kWh. That is it. There is nothing sophisticated about his model. His first error is that 60% is not representative of current hardware; real-world data shows the lifetime average percentage is between 6.3% and 38.6%. His second error is that the averaging period is excessively variable and poorly justified: it has increased from 60 and 439 days and changed by twofold his electricity consumption figures.

This motivated me to research and publish my own energy estimate using a model built from real-world data: Electricity consumption of Bitcoin: a market-based and technical analysis estimates 14.19/18.40/27.47 TWh/yr (lower bound/best guess/upper bound) vs. BECI claiming 40.24 TWh/yr as of 11 January 2018.

[This post is a live document. Last update on 21 March 2018.]

Flaw #1: BECI fails to apply economic theory

[Update: I removed flaw #1. BECI’s author addressed it: he used to assume 100% of mining revenues were spent on electricity. However read flaw #2…]

Flaw #2: BECI’s author misread a working paper and arbitrarily decided the ratio of electrical opex to mining revenues was “65%” then “60%”

Initially, BECI’s author assumed that 100% of mining revenues were spent on electricity, leaving miners unable to recover capex, let alone to make profits.

I pointed out that 16nm ASIC miners are spending a small fraction (~15%) of mining revenues on electricity. So in his “final release” he made a first attempt to find the fraction of mining revenues spent on electricity. He found and quoted an SSRN working paper by Tomaso Aste:

Tomaso Aste (2016) […] implies that the average costs of mining are closer to 55 percent of the available miner income. Aste, however, doesn’t provide many details along with his estimate. To be on the conservative side, the average cost percentage used to calculate the Index is set at 65 percent

However Aste never implies 55%. Aste writes:

it can be estimated that, with current hardware, the computation of a billion of hashes consumes, with state-of-the-art technology, between 0.1 to 1 Joule of energy. This implies that currently about a billion Watts are consumed globally every second (1GW/sec [sic]) to produce a valid proof of work for Bitcoin.

At the time of Aste’s writings in June 2016 the network’s hash rate was 1500 PH/s, therefore “0.1 to 1 Joule” implies 150 MW to 1.5 GW, which is within order of magnitude of 1 GW. The hourly cost of 1 GW is $50k assuming $0.05/kWh, or $8333 per 10-minute Bitcoin block, which Aste compares to the per-block income of $15000 at the time, which is where the 55% comes from: 8333 / 15000 = 55%.

But all these figures are merely orders of magnitude computed from “0.1 to 1 Joule.” I even emailed Aste to erase any doubt; he replied:

Yes Marc, we do not know which is the average consumption per GH, it depends on the hardware, we can only produce order of magnitude estimates.

All Aste implies is that the electrical consumption is 150-1500 MW, which corresponds to a per-block electricity cost of $1250-12500, or 8.3-83.3% of the mining income.

BECI’s author misread a working paper by interpreting an order of magnitude as an exact estimate (55%) and arbitrarily picked “65%” when in fact the paper merely implies the percentage is somewhere between 8.3 and 83.3%.

I emailed BECI’s author. He never replied. Some time later he silently removed references to Aste’s paper without letting me know. Around April 2017 I checked his site, saw the references removed, but he still stuck with “65%” with a second attempt to justify his chosen percentage. In his update in Live Energy Consumption Index he assumes:

  • Lifetime electrical costs of Antminer S9 = $745.20-1159.20 over 450-700 days
  • Only other non-electrical cost is the production cost of the hardware = $500
  • 60-70% of costs appear to be spent on electricity, therefore approximately 65% of Bitcoin revenues are spent on electricity

This flawed logic is ridden with multiple errors:

  1. The majority of S9 rigs are purchased by customers, and certainly not at the production cost of $500. The launch price in June 2016 was $2100, in May 2017 it was at its lowest at $1058, then it increased, in December 2017 it reached $2320, etc. If we assume the average S9 costs $2000, it changes the percentage to 27-37% (instead of 60-70%).

  2. This logic assumes not only miners acquiring S9 rigs under market price at $500, but also making zero profits (hardware capex + electrical opex = mining revenues.) In reality mining is highly profitable. This is why the global hashrate has been shooting up for years! I modeled profitability for all mining rigs released in the last 3 years in the CSV files published in Economics of mining; they show the real-world share of revenues spent on electricity for the S9 is currently 16.2% (lifetime average is 12.3%) as of 11 March 2018.

  3. Somewhat less important, but worth mentioning: he wrongly assumes all miners will operate their machines until their very last profitable day (day 450 or 700.) In fact miners often upgrade before reaching the true end-of-life. For example Economics of mining shows it makes sense to upgrade S5 units with S7 units after only 397 days in operation, at a time where the S5 spends only 37.1% of its daily revenues on electricity.

Around April 2017 BECI silently replaced 65% with 60%1 (his third change) but it is insufficient in addressing the above errors. BECI’s author defends his choice by saying “eventually” electricity costs will converge to 60% of revenues. In other words his model is not based on current real-world data/percentages but on his personal future expectations.

Flaw #3: BECI wrongly assumes electricity consumption and Bitcoin price are correlated over a “few weeks”

BECI’s author used to calculate the moving average of the Bitcoin price over a “few weeks.” This average price is used to determine average mining revenues, from which a 60% share is assumed to be spent on electricity.

I pointed out it takes months for miners to to plan, permit, finance, build and launch a typical industrial mining farm. Therefore it was unrealistic for his model to assume electricity consumption trailed price by a “few weeks.”

He later clarified the exact averaging period: it was 60 days, which is perfectly fine in my opinion. However read flaw #3.1…

Flaw #3.1: BECI’s energy estimate can double/halve based on the author’s frequent changes

BECI’s author estimates electricity costs (consumptiom) from 2 parameters:

Average revenues × 60% = electricity costs

As flaw #2 explained, he should have decreased his 60% parameter. But instead, he attempted to (insufficiently) reduce his overestimation by reducing the other parameter, average revenues. He has been calculating the moving average of the Bitcoin price over a variable period of time that keeps getting longer and longer:

Between 2017-05-24 and 2017-05-31 BECI’s author changed the averaging period from 60 days to 120 days. When asked on what day the change was made, he replied vaguely “It may vary per day.”

Another change was made between 2017-05-31 and 2017-06-20; the average is now computed over 150 days.

Another change was made between 2017-07-10 and 2017-07-21; the average is now computed over 200 days. I asked him when was this change made and he replied again vaguely “Let’s say mostly during the first two weeks of June.”

Another change was made between 2017-07-29 and 2017-08-14; the averaging period is no longer disclosed but reverse engineering the numbers indicate it appears to still be 200 days.

As of 2018-01-15 the average is still undisclosed but now appears to be computed over 300 days.

As of 2018-03-12 I was told the average (still undisclosed on the site) is now computed over 439 days.

The trend of Bitcoin’s price has generally been upward, therefore lengthening the averaging period decreases the average price, decreases the average revenues, and decreases his electricity consumption estimate. For example, lengthening the averaging period from 60 to 439 days, as he has done, halves BECI’s estimate.2

BECI’s author effectively took the liberty to change his electricity consumption estimate twofold by tweaking a parameter neither disclosed, nor documented, nor justified.

After asking the author 5 times to clarify his constant changes to the moving average period he eventually replied in a private email it was determined as such:

Average of (Maximum of (volatility t-1, decay factor × decaying volatility t-1) × reference days)

With a decay factor so low that without a new volatility peak it’s practically static. So here’s why I’m saying it’s fine to use a static number for most parts, and I update this on the BECI page when the difference is interesting enough [ed.: the BECI page no longer documents it] > there will always be some fine-tuning mismatches on multiple parameters. The information provided always allows for getting close enough in reproducing, as shown it did. If the fine-tuning is actually relevant it’s a different story, but again, that really depends on the purpose.”

His vague and cryptic explanation answers nothing and raises even more questions:

  1. First and foremost, the author provides no evidence to justify his formula’s choice. What is the rationale behind it? What makes 439 days the right averaging period? Why not 200 or 800 days? The author picked an arbitrary value that “felt right” without explanations.

  2. The formula is grossly ill-specified. What time period is t-1? What is reference days? What is decay factor? His underspecification makes BECI unreproducible and unverifiable.

  3. The author’s vague answers as to when he made changes to the averaging period indicates he does not even properly track them. In an email he told me getting this information would require “to go through thousands of page versions to retrieve this”. These changes should be made public.

Fundamentally, the averaging period is a kludge the author needs as his model estimates electricity consumption based solely on the very volatile Bitcoin price, instead of based on the hashrate which is a real-time indicator of electricity consumption.

Furthermore, the averaging period kludge tries to achieves two incompatible goals:

  1. It tries to represent the fact that miners’ investments in hashrate capacity—hence electricity consumption—usually trails Bitcoin price by a few months.

  2. It tries to counterbalance BECI’s excessive overestimate that 60% of revenues are spent on electricity. I demonstrated in Electricity consumption of Bitcoin: a market-based and technical analysis that BECI still overestimates consumption by 2.2×, therefore BECI would technically need to change the averaging period from 439 days to approximately 1000 days.2

Herein lies the incompatibility: an averaging period of a few months would overestimate electricity consumption, while an averaging period of 1000 days would be on average correct but would completely smoothes out fluctuations of the real-world hashrate and would therefore sometimes overestimate/underestimate electricity consumption.

BECI’s author’s insistence on not changing his unrealistic 60% parameter forces him to resort to a kludge—the averaging period—that cannot accurately represent real-world electricity consumption.

Flaw #4: BECI uses misleading terminology

A more minor complaint I have about BECI is that it claims to compare Bitcoin to the energy used by countries (electricity + oil + coal + gas…) when in fact it compares it only to their electricity usage. BECI references the International Energy Agency for these numbers, and in my opinion BECI should follow this agency’s example and use proper terminology regarding energy vs electricity. Notably it should title the chart “Electricity consumption by country”, and should say “The entire Bitcoin network now consumes more electricity than a number of countries”. I reported this to the author on 30 December 2016, but he does not seem to want to fix it…

Flaw #5: BECI’s supplemental material is also flawed

On 17 April 2017, the author published supplemental material that continues to make the same mistakes, and introduces new ones.

His upper bound for the total electricity consumption still ignores capex by assuming miners spends 100% of their revenues on electricity (see flaw #1).

His lower bound assumes mining machines are always operated until their very last profitable day. In fact miners routinely decomission older hardware that is still slightly profitable and replace it with more efficient and significantly more profitable hardware.

Flaw #6: Author misrepresents Morgan Stanley’s reports

In January 2018, BECI’s author added a Criticism and Validation section, quoting a report from analysts at Morgan Stanley: Bitcoin ASIC production substantiates electricity use; points to coming jump (source & my review here). BECI’s author ignores part of the report and selectively takes quotes out of context to deceive his readers and make them believe the report validates BECI.

For example the report presents a chart showing 2500 MW, see page 1, exhibit 1:

Chart of Bitcoin electricity consumption

The chart agrees with my estimate of 1620/2100/3136 MW and disagrees with BECI’s 4600 MW.3 BECI’s author chooses to ignore and not mention the chart.

Here is a the meat of the first section of Morgan Stanley’s report:

The analysts wrote there are “published assumptions [mrb: mine] that Bitcoin is using the equivalent of 2500 megawatts/hour.”

However “economic/pricing-based attempts to estimate electricity consumption [mrb: Digiconomist BECI] have indicated that actual usage levels could be even 50% more than that (~4000 megawatts/hour)

And they conclude: “as a bottom-up check, we have looked at reports of Bitcoin mining ASIC revenue and orders, rig power consumption and forecasts, and conclude that current use estimates are probably in the right general range.”

Clearly they do not confirm an exact figure but just the “general range” which is 2500 to more than 4000 MW. BECI’s author quotes only the last few words (“current use estimates are probably in the right general range”) to misrepresents the report’s conclusion as validating BECI.

Furthermore, my critic of the Morgan Stanley report uncovered multiple errors which are repeated by BECI’s author:

  • the hash-rate methodology uses a fairly optimistic set of efficiency assumptions”: this is false.
  • the most efficient mining rigs used by Bitmain in its facilities are not yet widely available”: this is false.
  • [Ordos] implies total hourly Bitcoin electricity consumption is well more than 2700 megawatts/hour (23 terawatt hours/year)”: the analysts’ numbers are incorrect, the Ordos mine implies 1690-2020 MW (14.8-17.7 TWh/yr).
  • many data centers around the world have 30 to 40 percent of electricity costs going to cooling [mrb: PUE=1.43-1.67]”: no studies support this, data points show otherwise.
  • may not allow enough for electricity consumption by cooling and networking gear”: electrical overhead is typically small, eg. PUE of 1.11 to 1.33, therefore my upper bound is still likely an accurate “upper bound” as it is unrealistically pessimistic by a lot more than 11 or 33% (it assumes all miners use the least efficient ASICs!)

Flaw #7: Fabricated forecast chart

BECI’s author made up an energy forecast chart in Criticism and Validation that is literally fabricated to tend toward the same same numbers as Morgan Stanley’s forecast of 120 TWh/yr by January 2019.

(Note that the Morgan Stanley analysts made various errors, eg. multiplying instead of dividing, their corrected model actually forecasts 60 TWh/yr.)

Fabricated forecast

Remember that BECI’s model assumes 60% of mining revenues are spent on electricity, and revenues are calculated from a moving average of the Bitcoin price. But he cannot forecast the price a year from now, so he picked an arbitrary high price ($15900) that made his chart lines tend toward 120 TWh/yr. There is no other way BECI can “forecast” an energy consumption. Given 2 static parameters (“60%” and “$0.05/kWh”) the only other parameter he can manipulate is the Bitcoin price. Blatant deceitfulness.

Flaw #8: “Bitcoin Cash” included in BECI

The cryptocurrency community makes a clear distinction between Bitcoin and Bitcoin Cash as they are two separate projects, two separate blockchains. However the Bitcoin Energy Consumption Index deceptively includes Bitcoin Cash miners:

Note that the Index contains the aggregate of Bitcoin and Bitcoin Cash (other forks of the Bitcoin network are not included).

The author should either not include Bitcoin Cash or should maintain a separate index for it. But, as he has demonstrated so far, he will do anything to inflate his electricity consumption figures.

[Update: On 01 October 2019 he claimed to have removed Bitcoin Cash from the index. Of course BECI being badly flawed means this did not change his electricity consumption figures: 73.121 TWh/year was reported for the day before, the day of, and the day after the change. A change of -3% would have been expected given the BCH hashrate was 3% of the BTC hashrate at this date. Go figure.]

Flaw #9: Claim that CBECI supports BECI

Researchers at Cambridge who also perceived BECI as flawed decided to launch the Cambridge Bitcoin Electricity Consumption Index (CBECI). On, or shortly after, 26 July 2019, the author of BECI claimed that CBECI “failed to produce significantly different estimates” and “the Bitcoin Energy Consumption Index and the Cambridge Bitcoin Electricity Consumption Index are mostly in perfect agreement with each other.

This is a lie. CBECI and BECI are far from being in “perfect agreement”:

  • During the entire year 2018, BECI was on average 51% higher than CBECI.
  • From the first available BECI historical figures to the day this claim was made (2017-02-10 to 2019-07-26,) BECI was on average 39% higher than CBECI.
  • During 2017-2018, there were 125 days where the difference was 60% or greater.

See cbeci-vs-beci.csv for comparison data. On Twitter the author of BECI further lied and claimed the overall difference was 20% and avoided the discussion when confronted 4 times in a row.

Flaw #10: Same electricity consumption reported for months at a time

BECI reported a constant consumption of 73.12146138 TWh/year for more than 9 months: from 31 July 2018 to 18 November 2018, and from 17 July 2019 to this day (06 January 2020.) Why is BECI stuck at 73.12146138 TWh/year is a mystery. I questioned the author who vaguely explained that sometimes he “adjusts things manually” and refused to provide further details. Obviously this is evidence BECI is not a real-time estimate, but a result of the author’s haphazard and undocumented hacks.

How to fix BECI?

Firstly, BECI should average the Bitcoin price over a fixed period of time (eg. 60 days) instead of frequently adjusting it between 60 and ~439 days.

Secondly, BECI should accurately estimate the percentage of mining revenues spent on electricity instead of hand-picking 60%. We can calculate the real-world percentage for various models of mining rigs. I have done this for all models released between December 2014 and March 2018 in Economics of mining, and found all lifetime average percentages were between 6.3% and 38.6%, with the percentage of the S9 (most popular mining machine making 70-80% of the market share) at 12.3%.

Then we can refine the percentage bounds by estimating the worst-case and best-case market share distribution of mining rig models that would lead to the highest or lowest energy consumption.

However with a market share distribution, the model can be vastly simplified: we get the global average energy efficiency (joule per gigahash), we multiply it with the hashrate, and we obtain the real-time global energy consumption. Simple. This is precisely the approach I followed in Electricity consumption of Bitcoin: a market-based and technical analysis. As of 11 January 2018 my analysis estimates 14.19/18.40/27.47 TWh/yr (lower bound/best guess/upper bound) which is 2.8×/2.2×/1.5× lower than BECI’s claim of 40.24 TWh/yr.


BECI’s author claims his economics-based model is superior to a hashrate/hardware-based model. But the truth is there is no way around the latter. His core parameter (60%) is not validated by evidence and must be determined from the market share of hardware models of mining rigs.

Furthermore, his other main parameter (439-day averaging period) is arbitrary, unjustified, undocumented and undisclosed, making BECI’s figures themselves arbitrary.

As I previously offered, if your are a journalist, analyst, or anyone who wants to write about Bitcoin’s energy consumption, send me a note. I will be happy to review your work and provide feedback. It is professionally unacceptable to publish analyses that are so far off reality like BECI.


  1. BECI’s author was unable to remember when he changed the hardcoded “65%” to “60%” (he said it was done “probably around the end of March/start of April [2017].”) 

  2. As of 14 March 2018, Bitcoin’s 60-day average is $10.3k, the 439-day average is half that at $5.1k, and the 1000-day average is half that at $2.5k.  2

  3. Figures as of Januray 3rd or 11th, 2018. 


Digiconomist wrote: First of all I'd like to state that the critism on the Bitcoin Energy Consumption Index (BECI) contains false information. It is claimed that BECI runs on the assumption that miners " never recover their investments (capex)". This has never been stated or implied by the information provided on BECI, and may relate to a misunderstanding. BECI assumes that the entire network is running at roughly break even, but this doesn't mean this is the case for every miner part of it. New machines may still earn themselves back easily under this assumption.

Second I'd like to add that the case laid out in this article is extremely optimistic on the electricity consumption of the Bitcoin network. It is stated that " the network's average efficiency falls between 0.055 and 0.27 J/GH". When this article was published, the best publicly available miner was the Antminer S7, running at ~0.25 J/GH. This one was released just a few months before. The Antminer S5+ was released just a little bit before at ~0.44 J/GH (in August). These machines wouldn't even have hit the market if the estimates in this article were true, as they would have been producing a loss as of day 1.
01 Feb 2017 21:35 UTC

mrb wrote: Digiconomist: You *do* imply the average miner never recovers their investments (capex), precisely because you imply the average miner barely stays afloat of his electrical costs only (opex). See quote from your own site in 3rd paragraph.

BECI implies the entire network recoups opex, but loses 100% of the capex. So *not* break even. To break even you'd need to recoup capex+opex.

Do you understand the difference between capex & opex?

Also you are wrong: many mining ASICs online as of Jan 2016 (when I wrote which is where our discussion started) beat 0.25 J/GH. It seems you didn't read my post. I provide links and references to each one of them in section 1 (

1. BitFury's latest 16nm chip achieves 0.055-0.07 J/GH (a 40MW data center of them launched in Dec 2015:
2. KnC's 16nm Solar 0.07 J/GH,
3. Spondoolies's 28nm PickAxe 0.15 J/GH,
4. Bitmain's 28nm BM1385 0.18-0.26 J/GH,
5. Avalon's 28nm A3218 0.27 J/GH.

It seems you don't know the market of ASICs very well.
01 Feb 2017 21:55 UTC

Digiconomist wrote: I don't think you can collect a random set of hardware and say "this is the J/GH" without even bothering to consider the economics surrounding that because it "seems fair" - while using economic arguments to tackle BECI. Some consistency would be nice.

You also published this post a bit too soon making it hard to discuss. If you had contacted me in advance I could have told you I was collecting data to cover 1 adjustment period in order to account for blocks being created faster (or slower) than 10 minutes on average. This held up the release of version 3, which includes some other adjustments as well. In particular the average costs mentioned here has been relaxed quite a bit. Looking at it from the bright side, you might like the updates. :)
03 Feb 2017 16:09 UTC

mrb wrote: But it's not a random collection of hardware. I didn't hand pick the most efficient ones to prove my point. It's all the known manufacturers of ASICs in the *world*. Literally.

Now, there are a lot of companies that never released silicon, failed, ran out of money, etc, see:

But nowadays all the known manufacturers of mining ASICs can be counted on the fingers of one hand (the list I gave, minus KnC minus Spondoolies who have failed). And when *all* of them have been shipping ASICs doing 0.06-0.15 J/GH for a while it's pretty obvious BECI's claims of 0.427 J/GH is impossible.

I'll say it again: it seems you don't know the ASIC market and that's what prevents you from understand how far off reality BECI is.
04 Feb 2017 00:29 UTC

Digiconomist wrote: But you didn't check how your estimate works out economically. You're saying the network is running at something like 0.12 J/GH. 0.15 J/GH if there has been zero improvement for a year, but that would be odd. Today the network is at 3,100 PH/s, so we're talking about 3.26-4.07 TWh per year.

We can translate this to costs directly since we can assume miners get 1 KWh per 5 cents spent on costs (per your own numbers). We get that on 3.26-4.07 TWh that translates to USD 163-204 mio in ongoing costs.

Annual income available to miners is easy to estimate as well, and it comes down to 817 mio per year (including fees). What we get is that miners are thus implied to be spending 20-25% on ongoing costs on average.

Now, of course this is in line with your example where the farm is paying 21% in costs, but you realize very well profit margins don't stay at 70-80% during the entire lifetime. On Twitter you wrote: "When I was mining with 20kW of GPUs it was pointless to mine when elec costs were ~70% of my revenues." So in my opinion you're arguing against yourself here.

If you want this to work out you're going to need something like 50% in ongoing costs on average. That's not the number I'm going with (65%), but then we're suddenly talking about BECI being just ~1.3 times the resulting estimate. That wouldn't be a massive gap at all.
04 Feb 2017 14:13 UTC

mrb wrote: I wrote a new post:

And I fully rewrote this post to take into account your "final release" of BECI which is still flawed.

My numbers prove to you that it is IMPOSSIBLE that miners spend more than 42% of mining income on electricity.

I think you fail to visualize how efficiency averages out. To calculate the average efficiency you need to average PER UNIT OF HASH RATE. And at any point in time in general most of the network hash rate is provided by newer farms (perhaps this is the crucial insight that makes it hard for you to accept my numbers?) So if 80% of the hash rate is provided by miners spending 20% on electricity, and if the remaining 20% spend 80% on electricity, the average is not 50% but 32%. Do the math. Read my new analysis ( and let me know if you have any questions.
15 Mar 2017 22:31 UTC

Digiconomist wrote: Did you really write this all just to point out that my estimate should be on the bottom end of my own error margin? From today's numbers: revenue $957M per year, costs $523M per year, so ~55.5%. As stated: "within reasonable economic boundaries one might expect to find a number that is 25 percent higher or lower". 42% costs is within that range. Anyway, anyone is welcome to pick another number if they like. I'm just trying to establish a method that produces a number that is plausible economically and not just technically (if you look at past estimates you can easily see why). BECI does that just fine.

By the way, the price is averaged over 60 days. It's not like I don't take feedback seriously ;)
16 Mar 2017 19:41 UTC

mrb wrote: Your "25%" is another figure pulled out of thin air. And this error margin is wrong too: $523M ±25% is $392-654M which is not in the bounds of $142-339M from Remember anything above $339M is provably wrong. It assumes the worst possible case of miners deploying the least efficient hardware available at their time. So your entire range of $392-654M is *really* in the wrong. You cannot keep saying "this isn't economically plausible" when I present factual, verifiable data proving your model is invalid.

You don't seem to believe in the "economics of mining" so here are numbers for a real-world miner showing that it can be quite profitable to mine (new added section):

Do something about it. Fix BECI.

PS: ok it's good that you average over 60 days. 1 (out of many) issue fixed :)
17 Mar 2017 03:01 UTC

Digiconomist wrote: I found it really hard to understand why you insist there's no overlap until I released 42% in your story isn't the same as 42% in mine, as your total revenues are based on unadjusted block rewards only. I also include fees and adjust for increasing hashrate (blocks are mined faster than once per 10 minutes on average). That leaves a serious gap of $130M between our revenue assumptions lol.

If you take 42% of the actual revenue we're going to be a lot closer, unless of course you'd like to tweak your scientific bounds in that case.
17 Mar 2017 15:05 UTC

mrb wrote: There is no overlap. My calculation is that worst case electricity costs are $339M/yr regardless of fees. The amount of fees is not a variable in my model because the model is based on what ASICs are used by miners and what are their energy efficiency.

So $339M/yr represents 42% of miner income excluding fees, or 38-39% of miner income including fees.

Arguing that your lower bound being close to my upper bound makes your model "ok" is wrong. Your bigger problem is your upper bound that should be close to $339M/yr.
17 Mar 2017 19:10 UTC

Digiconomist wrote: Okay, I really appreciate the effort you're putting into all this, so I'm checking out the new article later. But seriously, please fix this post.

I come to this page, and the first thing I see is a statement that I'm making a mistake on marginal costs. All I can say to that is that marginal costs don't include fixed costs like depreciation on buildings/machines or salaries like you're stating. So you'll then find that it really is mostly electricity costs.

Then I also find that you're just leaving out $100M+ from the revenue, and on top of the previous that really makes my brain explode.
18 Mar 2017 09:40 UTC

mrb wrote: Marginal costs do include the cost of the hardware. What is debatable is whether they include buildings and salaries (I edited this part of the post, see the new footnote about marginal costs.) But you are categorically wrong that they do not include the cost of the hardware. A miner adding one unit of mining capacity certainly needs to pay for this hardware.

In fact, the hardware cost is the largest initial marginal cost of setting up a new farm. Look at the CSV file in : an Antminer S5 cost $418 and consumes only $0.71/day in electricity. It takes more than 1.5 years for the cost of electricity to surpass the cost of the hardware.

Finally, I am not leaving out $100M of fees revenue. See the new footnote explaining how 18-42% is calculated.

Let me know when you will have fixed BECI.
21 Mar 2017 03:30 UTC

Digiconomist wrote: You should really check out the paper by Hayes (2015)

Specifically this part is relevant:

“Each unit of mining effort has a fixed sunk cost involved in the purchase, transportation and installation of the mining hardware. It also has a variable, or ongoing cost which is the direct expense of electricity consumption.”

Since sunk costs (unrecoverable expenses) aren’t relevant to marginal costs (it's not like they're not paid for), that’s how he’s left with electricity consumption.
21 Mar 2017 10:19 UTC

mrb wrote: "Since sunk costs aren't relevant to marginal costs..." → you jump to this conclusion, but no one supports this conclusion. Not even this paper by Hayes. You quoted a part that just explains "there is capex, and there is opex" which is obvious to you and I, and which is not what we are arguing about.

You try to argue that if a miner purchases an Antminer S5 for $418 and powers it for $0.71 per day, then economic theory suggests that mining revenues will amount to $0.71 per day. That is false. In reality miners expect to recover $0.71/day plus the $418.
22 Mar 2017 01:21 UTC

Digiconomist wrote: That's basic economic theory and also the reason why Hayes subsequently ignores them in the rest of his paper...

Investopedia explains this very well:

"Since decision-making only affects the future course of business, sunk costs should be irrelevant in the decision-making process. Instead, a decision maker should base her strategy on how to proceed with business or investment activities on future costs."

So basically, the price that was paid for a miner isn't relevant because it's purely retrospective. Looking forward, only electricity consumption (and some other negligible costs) matters . Putting these 100% of revenues isn't that crazy from an economic PoV (after all, the optimal output if where marginal costs = marginal revenue).

Now you're stating; I can show a new farm starting with elec costs as low as 15%, so are you kidding me with the number of miners needed at 100%+ to compensate for that.

I do agree with you that it goes against intuition, but first let's examine some reasons mentioned by Hayes why costs could exceed 100%:

"Individual decision makers may operate regardless of cost if they believe that there is enough speculative potential to the upside. Bitcoin mining may draw in those who find the features of anonymity and lack of governmental oversight attractive. Some miners may decide to hoard some or all of their lot and not regularly engage in offering mined bitcoins in the open market, a sort of bitcoin 'fetishism'"

Now I'm obviously a bit skeptical about this myself, otherwise I wouldn't have lowered the percentage.

Hayes actually mentions an important reason too why 100% could simply be too much:

"Some miners may be subject to an opportunity cost whereby it would be more profitable to expend the same electrical capacity for some other pursuit"

Objectively it should be (close to) 100% though. At least from an economic PoV. If reality differs that's not a failure of correctly applying economic theory.
22 Mar 2017 10:56 UTC

mrb wrote: I agree this economic theory makes sense in a theoretical case.

But if it was in effect in the present situation, then the global hash rate would be stagnating: existing miners would continue to mine because they can recoup their electrical opex, and no new miners would join in because they expect to be unable to recoup the sunk cost of capex. Obviously that is not what is happening. The hash rate has been increasing for years precisely because new miners expect to recoup sunk costs. Therefore this theory cannot possibly apply to the present situation.

I edited "critic #1" to better explain all this.
26 Mar 2017 02:16 UTC

lorschd wrote: What an interesting dispute ;) The typical discussion between engineering bottom-up and economic top down in energy modelling.
Questions / comments on bottom-up: mrb you wrote that the PUE of mining farms is “typically very low for mining farms, 1.05 or so.” Really? I would appreciate some empirical evidence. The PUE values appear pretty low compared to other data centers ( e.g., ). I know about liquid cooling (eg. However, there might be also some overhead for power distribution & conditioning, redundancy (n+1) or backup supply etc. Do you really think these low PUE numbers are mining industry average? From my point of view, proper lower and upper bound estimations would include the most and least efficient PUE guesstimates.
On marginal costs:
We distinguish between short-term and long-term marginal costs. Before costs are sunk, i.e. before investing, decision makers will only invest if there long-term marginal costs are covered. Broadly speaking, long-term marginal costs are the average costs and as such naturally include capex. Btw capex should not only encompass the mining hardware, but also the data center and its site infrastructure. Once investment is done or in “fixed capacity” markets marginal costs equal market price rule may apply and miners aspire positive contribution margins. For capacity expansion, however, the potential investor needs to expect that long-run marginal costs are covered.
(see e.g. ; You may also confer the discussion about the “missing money problem” in electricity markets. ).
However I think in light of inherent economics characteristics of Bitcoin - non-renewable commodity with finite availability – we may also consider that Btc may rather follow the Hotelling rule for exhaustible resources (, which would imply an exponentially increasing bitcoin price (ofc with boom/bust trading cycles, but in the long-run exponential).
11 Apr 2017 18:34 UTC

mrb wrote: lorschd: About PUE, you found yourself that Bitfury self-reports 1.02. But it's mostly from acquaintances who run farms that I know they are around 1.05. Sorry I can't provide direct evidence. It makes sense for PUE to be super-optimized. Mining is a venture with such thin operating margins that a PUE of 1.15 would cut your profits by 10% compared to a competitor at 1.05, so there is an unusually strong incentive to optimize it as much as possible.

You see the result of this optimization in the design of mining farms: they either use liquid cooling or outside air cooling (never traditional CRACs), and bring relatively high voltages directly to servers (480/277 or 415/240 VAC) to eliminate some power conversion steps.

There is also typically no need for power redundancy or backup supply, so none of that affects the PUE. Backup supply/gas generators are too expensive per kWh to mine with. And redundancy is not needed because miners don't need high availability. They could tolerate even a subpar public utility having outages ~1% of the time. That would only cut their revenues by ~1% unlike traditional businesses that would be much more severely impacted.

Finally, I agree capex includes the facility and infrastructure. Ignoring/underestimating capex is one of the main errors that Digiconomist commits.
12 Apr 2017 23:25 UTC

Digiconomist wrote: Hmm... before investing "fixed cost" could indeed be marginal costs. But try asking the question; who is the investor? From a new miner's perspective these could be quite high, but it's less extreme for a manufacturer. In fact, it's an entirely different model there (their fixed costs are the machines to produce the miners). 15 Apr 2017 15:01 UTC

Digiconomist wrote: Added details with regard to the previous statement to the BECI page. IMO that covers #1 and #2 (the BECI was never based on Aste's work to begin with; Hayes deserves the credits). You're also very fixed on that 65% number; "the goal of the Index is not to produce a perfect estimate, but to produce an economically credible day-to-day estimate that is more accurate and robust than an estimate based on the efficiency of a selection of mining machines." The index cost % is capped at 65 and you should apply an error margin of 25% (supported by the lower bound in the supplement).

As for #3 and #4. These have been addressed. It's sufficiently clear that it concerns electricity consumption.

And with regard to #5. Well, I followed your method to determine the machines that should be active at the very least. The only economic part is that it is assumed that miners run while it's profitable to do so whether that is 1% or 99% profits. That's a lot safer than guessing what miners may or may not do.
26 Apr 2017 14:28 UTC

mrb wrote: You say "could indeed be marginal costs," but they *are*. We exchanged dozens of messages and so far you have failed to explain every single time why you ignore capex (this is critic #1). On Twitter you said they should be taken into account if new hardware is deployed, and my point is that new hardware is constantly being deployed: the hash rate is shooting up every day. At least it seems you are slowly taking into account capex by assuming, at least, that a fraction of mining revenues is spent on costs other than electricity...

About critic #2, I am "fixed" on this 65% because it is the single and only variable in your model that determines all your numbers, and the percentage is very wrong. You cannot justify it by having a disclaimer tucked down at the end of your page ("the goal of the Index is not to produce a perfect estimate"). This makes BECI highly deceptive. Neither is your 25% error margin sufficient to justify your position: 65±25 implies your index cost percentage is between 40-90%. I have shown in my article the real percentage is in the range 18-42%. So you assuming it could be anything beyond 42% is, simply put, grossly flawed.

You responded to my critic #3, thanks.

You still have one more fix needed to fully resolve critic #4. Replace energy with electricity in "the entire Bitcoin network now consumes more energy than a number of countries."

Regarding critic #5, you are missing the point of what a lower bound is. A lower bound means you are absolutely certain that miners cannot possibly consume less energy than a given bound. You make a (wrong) guess/assumption that *all* miners run *all* their mining farms until their very last profitable day. But to make it a lower bound, you must prove it. You fail to do so because your guess is incorrect. Ironically you say your model is "a lot safer than guessing what miners may or may not do" but you are doing precisely that: guessing! What do you think Bitfury did once their 16nm hardware rolled out of the factory floor? You think they were waiting for the 28nm gear to stop being profitable to upgrade to 16nm? You think they spent a fortune building *additional* 16nm farms besides their 28nm farms? The answer to both questions is: no! They decommissioned their 28nm hardware right away (even if still somewhat profitable) to replace it with much more profitable 16nm hardware.

You are not a miner. You have never talked to, researched, or studied the mining industry. You have never met and interviewed professional miners. If you had done so, you would realize how flawed your assumptions and entire model is.
15 May 2017 01:51 UTC

Digiconomist wrote: "BECI’s author’s implicit acknowledgment his earlier models would be overestimating electricity consumption by 1.57"

Okay, official reaction from BECI's author to this: I explicitely stated that BECI was fine IMO at any point in time, so this is BS, please don't spread lies.

Second, changes are far from random, even informed you of the method. The lag depends on volatility, so it if it was lower before that's because volatility was lower before. Again, you're spreading lies.

Also it's completely insane to state I would like to “keep BECI down”? What possible motive do I have???? There's no complete data on Bitcoin's actual energy consumption anywhere, so there's ZERO need for adjustments. You have offered NO information that would somehow lead me to think otherwise, and I've been trying to explain that for some time - but you don't seem to get it.

If you have to lower yourself to slandering to make a point that's absolutely pathetic.
03 Aug 2017 15:34 UTC

mrb wrote: You seem to be making 3 points (and the email dump you published seems to have also been made in response to the same 3 points):

(1) I said your moving period average changes (60 days → 120 days → 150 days → 200 days) implicitly acknowledge you overestimated consumption by 1.57×. I was documenting the positive changes you made, since in my opinion they brought BECI a little bit more in line with reality (not completely though). But instead you reject this mildly positive critic and insist BECI was fine at any point? Ok, I removed that sentence and edited critic #3.1.

(By the way, BECI was certainly not fine "at any point." For example: you used to assume 100% of revenues were spent on electricity.)

(2) You said you have no motive to "keep BECI down". Well I assumed good faith on your part: I assumed your "motive" to keep BECI down by increasing the moving average period was to try to shift BECI's numbers to be closer to reality.

(3) I said you do not track, archive, or document changes to the moving average period and that the changes look random. I said it because it took 5 emails where I kept asking when you made the changes, before finally obtaining your partial best answer (an ill-specified formula). You had not yet given me the formula at the time I had last updated this post. I have now updated it to remove the "random" critic. However as you said in an email, answering the question of when did you make the changes would force you "to go through thousands of page versions to retrieve this." Therefore my main criticism remains valid: you do not track the changes in an easy/proper way, you do not want to or cannot answer, and you certainly do not *document* the changes publicly. It is therefore impossible to verify/reproduce BECI's numbers, past and present (the current moving average period is no longer documented on the site.)

You say I have offered no information that would change your mind, but that is an understatement. You have yet to refute the (updated) information I gave you in critic #1, #2, #3.1, #4, and #5. Plus I gave you my other post full of facts, data, charts, and CSV files: I gave you many counter-arguments over the last few months, many of which you have never replied to.

I am certainly NOT slandering you, but instead presenting you with facts and data disproving your model.
31 Oct 2017 21:59 UTC

Digiconomist wrote: >I gave you many counter-arguments over the last few months, many of which you have never replied to.

I did, but you didn't seem to care about what the arguments were (per your emails you certainly didn't have any troubles publishing on an unfinished discussion). One way or another, it's not very pretty what you did here.

In any case, I've abandoned this discussion. We can discuss forever, but all we'd be doing would be arguing whether Bitcoin's energy consumption is insane or even more insane.
11 Dec 2017 15:10 UTC

mrb wrote: You claim you replied to my critics. This is an outright lie.

For example you have completely avoided replying to my main point (critic #2): the 4 logic errors you made in choosing your model's main parameter, the "60%" percentage. See the list items numbered 1-4 concluding critic #2. Zero response from you.

Seeing you abandon this discussion simply betrays your fundamental inability to defend your flawed model.

PS: This blog post is a live document, updated as our debate progresses. It will always be "unfinished".
07 Jan 2018 03:17 UTC

Digiconomist wrote: Please update it with Morgan Stanley's latest work. They did a production based analysis and concluded the cost of mining 1 Bitcoin is between $3,000 and $7,000. My model puts it at just $2,100 per coin at the same time. MS also assumes a bit higher price per KWh, so it's in the same energy range (although on the low side, but the model was supposed to be conservative). ;)
12 Jan 2018 22:15 UTC

mrb wrote: You continue to ignore and to not address the flaws in your model. See the list items concluding critic #2. I have been waiting for a reply on this for months.

And you failed to read/understand Morgan Stanley's report. Their math includes non-electrical (ie. hardware) costs. In fact they assume 1,232,448 S9 machines which total 15 TWh/year, a lot less than your 42 TWh/year... See

Alex, every comment you make contains false information. It does not make for a very productive discussion. Take my feedback and fix BECI.
17 Jan 2018 03:25 UTC

Digiconomist wrote: Well, actually they refer to me when it comes to current consumption, and to their other recent report "Bitcoin ASIC production substantiates electricity use". I'm guessing you didn't even see this report in full apart from some media snippets. 19 Jan 2018 22:48 UTC

mrb wrote: And last week Bloomberg quoted my research in their report[1] on Bitcoin mining. They estimate 20.5 TWh/yr, close to my best guess of 18.4 TWh/yr. Does MS quoting you or Bloomberg quoting me proves one of us is right? No. Besides tickling our egos, it doesn't prove anything. Instead you should be addressing the flaws in your model which you are still ignoring... See the list items concluding critic #2.

[1] Bloomberg report titled "Bitcoin in Energy Crisis as China Cracks Down", available on the terminal or on the web
22 Jan 2018 02:36 UTC

Digiconomist wrote: I think you're missing my point. There is nothing to fix. Morgan Stanley provided some solid work that shows my simple prediction model is doing a great job. Try reading: 25 Jan 2018 21:07 UTC

mrb wrote: Missing the point? This is the 5th time you ignore critic #2. Reply.

As to those Morgan Stanley reports, not only do they present a chart agreeing with my estimate and disagreeing with yours, but they also aren't flawless. In one computation, the analysts made a basic math error by multiplying instead of dividing. Because of this, their forecast is 2× off. Read

As to your energy forecast, you literally fabricated it. Read critics #6 and #7.
07 Feb 2018 20:19 UTC

Digiconomist wrote: I literally have no clue what you're trying to say. "Fabricated charts", "linear growth"of the Bitcoin price - dude, my index isn't even based on price, it's based on revenue - and stable revenue for this forecast ("the best prediction today of what a price will be tomorrow is the price today").

And regarding MS's error. Well, take it to them, but just FYI they revised their estimate for the chip-based production potential to 200 TWh per year (see "Bitcoin Critics Grab the Mic. And Electricity Use Keeps Going Up. But is it correlation?") - so you need a bigger error than off by 2.
05 Mar 2018 13:34 UTC

mrb wrote: This is the 6th time you ignore critic #2. Reply.

Claiming your index is not based on price is a lie... Fact check: Your index depends on revenue. Revenue depends on price. Therefore BECI depends on price. Even your site said it:

You said you assume "stable revenue." Bitcoin has demonstrated it is anything but stable. In the last 60 days its price varied 3× from $6k to $17k, so your "forecast" is nonsense as it varies 3× depending on which revenue or price figure you hand-pick. Your model cannot predict energy consumption any more so than it can predict price.

James Faucette (lead MS analyst) emailed me after they published the 3rd report. Hopefully their future reports will be corrected.
05 Mar 2018 19:54 UTC

Digiconomist wrote: >Your model cannot predict energy consumption any more so than it can predict price.

Half your post here is about the 60%, which has been a target param since the very beginning. You can see it's 2.6B vs 9.4B today, that's 28%. The prediction is 60%. So how do you mean it's not predictive? It's 100% a prediction model, I don't need to pick any fancy price for that. But you've been getting that wrong from the very beginning. You keep acting like I'm saying it's 60% today. No, it's not, just check the numbers for once.
06 Mar 2018 09:54 UTC

mrb wrote: Has it really been one full year since I wrote this post and you still do not understand that my text in critic #2 refers to your 60% PREDICTIVE percentage, not the 28% CURRENT percentage? Are you feigning ignorance?

Basically I showed that you overestimate energy consumption by 2.8×/2.2×/1.5× (lower bound/best guess/upper bound.) Therefore for your model to report accurate numbers you should set your PREDICTIVE percentage to 21%/27%/40%, which would make the CURRENT percentage 10%/13%/19%.

And therein lies the bigger flaw: because your model would need a dynamic (not static) PREDICTIVE percentage to accurately estimate energy consumption, it actually shows it is poorly predictive.
06 Mar 2018 10:00 UTC

Digiconimist wrote: Just to add a bit of basic economics. The reason it's predictive is because in general the point where marginal cost = marginal revenue is the point of optimal production. Production will always gravitate towards this point. 06 Mar 2018 10:01 UTC

Digiconomist wrote: And while it's gravitating to that point, yes, mining is hugely profitable. That should be apparent from my numbers as well (again, today's cost percentage is super low). That's why more machines will keep on being produced. Marginal costs are still < marginal revenue. When they get to that point, it's no longer profitable to produce more. And yet you counter my work by showing mining is CURRENTLY very profitable. That's not really countering anything. A bunch of arguments here just make no sense. 06 Mar 2018 10:26 UTC

Digiconomist wrote: Your CSVs could be more useful if they actually covered the complete lifetime of a machine. They would most likely support my lifetime assumption that helped me set the 60% target in the first place. But you've only got the S5, which is not even at the end of its lifetime yet ( So the only thing I can get from it now is that mining is very profitable, yeah, I know. 06 Mar 2018 10:43 UTC

mrb wrote: «it's predictive is because in general the point where marginal cost = marginal revenue»

Yes but you cannot predict future marginal revenue (future Bitcoin price is unpredictable) therefore you cannot predict marginal cost. That is why your chart in critic #7 is fabricated: you literally claim to know Bitcoin's price 9 months in the future(!)
06 Mar 2018 16:17 UTC

mrb wrote: I found yet another error in your work. 150 blocks were mined per day in last 48 hours, miners currently collect ~50 BTC in tx fees daily, therefore annualized mining revenues are:

(12.5 × 150 + 50) × 365 × $11k = $7.7B

But somehow your site claims $9.4B. You are $1.7 billion off. That is 22% off. If you cannot even correctly calculate marginal revenue, your energy estimate is also 22% off from this error alone (ignoring other flaws.) Fix your math.

PS: I am still waiting for a proper reply to critic #2. It is the 7th time you ignore it or feign ignorance.
06 Mar 2018 21:06 UTC

Digiconomist wrote: "Note that the Index contains the aggregate of Bitcoin and Bitcoin Cash (other forks of the Bitcoin network are not included)."

Has been this way since the fork and was announced back then - but I remember you cannot read my Twitter updates so I won't hold it against you. And actually, this explains both your comments. :)
07 Mar 2018 08:25 UTC

Digiconomist wrote: Plus, I can keep responding to critic 2, but I'd merely be repeating that the case for the 60% is solid.

The market equilibrium is in margin revenue = marginal costs. You can approach this from two perspectives. The first one is that each unit of mining effort includes a sunk cost in the form of hardware costs, which are irrelevant to the decision to mine. Hence marginal costs can be 100% electricity.

When prices are increasing, you could also consider the cost of acquiring new hardware. The S9 retailed as low as $1200, and that's still including a profit margin. Song estimated the actual production cost was around $500. Given a lifetime of two years, add $1200 in electricity costs (at 5 cents per KWh). That's 70% going to electricity for the full machine over its lifetime.

You can keep pointing at the selling price as much as you like, but that doesn't change the equilibrium for that case. Eg if Bitmain asks $2300, and miners expect an electricity bill of $1200, they won't buy the machine if they expect to gain $3500 or less. Bitmain then has a few options 1) stop producing, 2) produce and mine themselves, as they can still make a profit of $1800 per machine (it costs them $1700 and the revenue is $3500), 3) lower the retail price and sell at maybe $2000 - then miners will buy again. And so on...
08 Mar 2018 15:20 UTC

Digiconomist wrote: It would be pretty insane for Bitmain to refuse to lower prices and stop producing while their production cost is only $500... They might be big, but they're not a monopolist. 08 Mar 2018 15:25 UTC

mrb wrote: «that doesn't change the equilibrium for that case»

On the contrary, the S9 price changes everything. This is the core flaw in your model. Guess why Bitmain is not lowering it to $500? Because *currently* miners expect to easily recoup its inflated price of $2000+. This means your assumption of 60% (or 28%) is not *currently* representative of reality. It is that simple. Your assumptions are broken, therefore your model is broken.

Do not weasel your way out of explaining this self-contradiction. You cannot in the same sentence claim the S9 selling price does not matter, when the S9 selling price (ie. cost to miners who buy it) is literally the one and only data point you chose to justify your 60% (or 28%) parameter.
12 Mar 2018 16:27 UTC

Digiconomist wrote: Do you understand the concept of an equilibrium? We expect a covergence to this point. No we're not there now. Guess where the moving average is for? This is to point to where we stand today.

I'm not sure where you get from that I'm using the retail price to set the 60%. It wouldn't make any sense at all. I've used the production cost and an expected lifetime of less than two years as explained here
12 Mar 2018 21:59 UTC

Digiconomist wrote: Realistically the only value that could be changed is the time period under consideration. But it's so ridiculously long already (just checked the live number is 439 days) that real production numbers will most likely exceed it. 600k new machines per month (at least per the start of the year) + the fact that Bitmain is not the only producer (although the biggest one) should outpace my index easily - especially now the whole mining market is in going into production overdrive. 12 Mar 2018 22:11 UTC

Digiconomist wrote: By the way, I came across your name again the other, and I was wondering: what ARE you investing in? It said "tech investor", your profile here says "investor in the booming area of cryptocurrencies". Does this include mining operations or PoW cryptos by any chance? I've never seen any relevant disclosure. 13 Mar 2018 10:23 UTC

mrb wrote: I updated all my CSV files at showing that the lifetime average percentages of all mining rigs (even those at end-of-life) released between December 2014 and March 2018 are between 6.3% and 38.6%.

If you stick with 60%, then you are openly acknowledging that your model is not based on real-world data/percentages but merely on your personal future expectations... Not very solid.

Your 439-day moving average is insufficient. You would need a 1000-day average to produce accurate results. You may want to read critic #3.1 again (I rewrote it from scratch today.) In a nutshell: you have given no rationale behind why 439 days (why not 200? why not 800?), your formula is ill-specified, unreproducible, unverifiable. You essentially picked an arbitrary averaging period that gives you the liberty to change your electricity consumption estimate twofold by tweaking a parameter that is neither disclosed, nor documented, nor justified.
22 Mar 2018 00:58 UTC

Digiconomist wrote: Looks like we can finally put this discussion to rest. The core methodology of the Bitcoin Energy Consumption Index has been anchored in peer-reviewed academic literature. I'd recommend reading the story since it might clear up some of these longstanding misconceptions.
17 May 2018 01:07 UTC

mrb wrote: You are deceiving people: your Joule article fails to support your Bitcoin Energy Consumption Index. They directly contradict each other:

Joule article: "The Bitcoin network can be estimated to consume at least 2.55 gigawatts of electricity currently, and potentially 7.67 gigawatts IN THE FUTURE"

Whereas, as of 16 May 2018 (article publication date), your index estimated Bitcoin CURRENTLY consumed 7.66 gigawatts ("67.138 TWh/year").

Your article claims the methodology estimates FUTURE consumption, while the index claims the same methodology estimates CURRENT consumption. These are two different claims. One is true, the other is a falsehood.

Now, I think you know Joule reviewers did not let you claim Bitcoin was already at 7.66 GW as the time. So you were told to weaken your claim in the article to present the methodology as a tool that can estimate, at best, where FUTURE consumption is headed. That's alright and mostly correct. I've always seen your methodology as just that: a limited tool to estimate future consumption (see my last sentence of critic #2: "[his] model is not based on current real-world data/percentages but on his personal FUTURE EXPECTATIONS.") That's why Joule reviewers allowed you publish this revised, softened-down claim. But don't deceive people now by claiming your article supports your flawed index. It's not true.

It's been a year now, the time has come to reveal it: I was the academic peer that Joule selected to review your article. I was the one who asked you to revise your paper to clarify the methodology can only estimate future consumption, not present. The only "longstanding misconceptions" that exist are those that you spread through your non-peer-reviewed "Broken Energy Consumption Index."
21 May 2019 17:28 UTC

Digiconomist wrote: I suppose we can finally wrap this discussion thanks to the University of Cambridge; or as I wrote: it's now easy to show that these "serious faults" are a great exaggeration 10 Jul 2019 18:33 UTC

mrb wrote: The University of Cambridge's new bitcoin energy consumption index (CBECI) is based on my methodology. They consulted with me to design it. It shows that you overestimate energy consumption by 51% in year 2018, when compared to CBECI's best guess figures:

It is my personal opinion that CBECI, although far more sophisticated than BECI, currently also moderately overestimates consumption. I hope to continue working with them to further refine CBECI.

Meanwhile it has been almost 3 years and you have still failed to address the majority of BECI's flaws documented here.

Even worse: you never replied, acknowledged, denied, counterargued, or wrote or said anything about my latest points (#4, #5, #6, #7, #8). Complete radio silence. I suppose avoiding your critics is the only option when you have nothing to say in your defense.
19 Dec 2019 01:46 UTC