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As Red And Blue Debate, Green Printer And Orange Coin Always Win

The nation and world are on the brink, awaiting official Electoral College votes to be cast on December 14, despite convincing conjectural consensus on the champion. But either way, 2020’s highly-contested U.S. presidential election reminds us that much of the country is either rabidly red or batty blue. This has resulted in a magnitude of red vs. blue conflict the likes of which we have rarely seen before.

The purported stark contrasts between both sides, however, ring empty in regards to the monetary policies these two parties employ. Especially so over the previous five decades, while they swapped control over the executive branch of the United States. These staunchly disputed differences of position appear to be shallow and essentially meaningless  —  with both sides hardly commentating, let alone differentiating, on monetary policy.

The Federal Reserve supposedly maintains its political independence, however the data suggests that the private legalized monetary cartel has in fact been influenced by the Oval Office and U.S. Treasury, depending on the general direction of the controlling regime. As such, decisions from the Executive Office of the United States of America have major implications on the integrity of the United States dollar. 

We can possibly glean some insight from historical presidential precedence by composing volumetric evidence of what these two political parties have done historically at the helm of the monetary printing presses. This political printing analysis is important and applicable today, given the sheer volume of monetary expansion that has been and is still occurring. It is also especially important in regard to Bitcoin because either Donald J. Trump or Joseph R. Biden have been in the office of president or vice president since January 20, 2009, just days after the Bitcoin network Genesis Block on January 3, 2009.

M1 And M2 

Money may be the root of all evil, but what is it made of? The composition of money in the United States is a complex, obfuscated tale of deposits, notes, checks, credit, bills and various other IOUs, both physical and digital. The physical composition of the paper money of the United States, however, is a simpler tale: 75 percent cotton and 25 percent linen

Common measures of the U.S. monetary supply used by the United States Federal Reserve are M1 and M2. Straight from the horse’s mouth, here is the definition for the M1 data set:

M1:

M1 includes funds that are readily accessible for spending. M1 consists of: (1) currency outside the U.S. Treasury, Federal Reserve Banks, and the vaults of depository institutions; (2) traveler’s checks of nonbank issuers; (3) demand deposits; and (4) other checkable deposits (OCDs), which consist primarily of negotiable order of withdrawal (NOW) accounts at depository institutions and credit union share draft accounts. Seasonally adjusted M1 is calculated by summing currency, traveler’s checks, demand deposits, and OCDs, each seasonally adjusted separately. 

–Board of Governors of the Federal Reserve System (US), M1 Money Stock [M1], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/M1, November 10, 2020.

In 2020 alone, Team Red, the Fed and the Trump administration oversaw the growth of M1 money supply by $1.628 trillion, eclipsing total M1 in circulation in 2009 ($1.612 trillion) when President Obama first took office. In 2020 M1 increased by 40.96 percent to $5.6 trillion.

M2:

M2 includes M1 and some other monetary instruments. According to the Fed, the M2 data set is:

M2 includes a broader set of financial assets held principally by households. M2 consists of M1 plus: (1) savings deposits (which include money market deposit accounts, or MMDAs); (2) small-denomination time deposits (time deposits in amounts of less than $100,000); and (3) balances in retail money market mutual funds (MMMFs). Seasonally adjusted M2 is computed by summing savings deposits, small-denomination time deposits, and retail MMMFs, each seasonally adjusted separately, and adding this result to seasonally adjusted M1. 

–Board of Governors of the Federal Reserve System (US), M2 Money Stock [M2], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/M2, November 10, 2020.

Similar to the massive increases in M1, 2020 saw the growth of M2 Money Stock by $3.320 trillion, as of October 26. Over the last four years of the Trump administration, M2 increased by 40.30 percent to $18.839 trillion

For further analysis, M1 and M2 will be the metrics we use to attempt to benchmark previous presidential administrations in regards to monetary policy going back to 1981 (M2 data) and 1974 (M1 data).

Does Blue Or Red Printer BRRR More Green?

So, in the great struggle between red and blue, it is important to understand which party has historically been more friendly to the activity of the monetary printing presses. Let us start with M1.

M1:

The trend is stark: the M1 monetary supply of the United States is growing at an alarmingly accelerating rate. Each American administration since Richard Nixon’s has overseen the expansion of the money supply, some more aggressively than others. 

For example, by 2015 the Obama administration had overseen the expansion of M1 by an additional $1.729 trillion, more than was originally in circulation when he took office in 2009. A clean doubling of the M1 money supply, in just over six years.

Likewise, in 2020 alone, the Fed and the Trump administration grew the USD M1 monetary supply by $1.628 trillion, eclipsing total 2009 M1 in circulation ($1.612 trillion) when President Obama first took office. However, in terms of percentage, these M1 growth numbers look slightly different. The Obama and Reagan administrations hold the respective Blue and Red Team MVP awards in percentage increases, surprisingly. 

In 2020, M1 increased 40.96 percent year-over-year to $5.6 trillion. You read that correctly: In a single year, Trump grew the M1 money supply an equivalent amount that took the historically pro-printer, banker-friendly Obama administration more than six years! Likewise, Obama in a single year (201 1,  $310 billion) increased M1 an amount that took George W. Bush four years to accomplish. 

Interestingly enough, despite this, Team Red as a whole has issued more USD M1 than the typically more fiscally liberally Team Blue over the previous 46 years. This may be due to the fact that Team Red held the presidency for six more years than Team Blue, so let’s try and normalize it.

Team Blue grew M1 by roughly $2 trillion during three separate administrations over 20 presidential years since 1974 , an average rate of $100 billion per year.

Team Red expanded M1 by $3.3 trillion during five different administrations over 26 presidential years since 1974 ,  an average rate of $128 billion per year. Team Red prints volumetrically more and at a 29 percent quicker rate per year.

Interestingly enough, if the Trump administration’s four years of excessive printing were removed from the above analysis, Team Blue would have out-printed Team Red at about twice the rate. However, the historical evidence is clear, neither party or team is a stranger to the monetary printing presses. It would appear that regardless of which team currently holds the presidency of the United States, the money printers have roared.

M2:

We will do this analysis again, except we will study the amount of M2 growth per year while visualizing each year by team of the administration that oversaw that expansion. The chart below also attributes the total amount of M2 circulating at the end of each presidency. 

The trend is unfortunately again clear, the monetary policies of each successive president sees the expansion of the M2 money supply more than the last. There is one exception though: it would appear that George H.W. Bush printed the least amount of money and bucked the trend. 

A shocking stat from the by-presidency analysis of M2 expansion: The Trump administration oversaw 13-times the growth of M2 as the first Bush administration. However, in relative values, these M2 growth values compare differently. Bush, in terms of percentage actually grew M2 the most.

Again, Team Red oversaw more growth of USD M2 than the stereotypically more fiscally liberal Team Blue over the previous 40 years. However, this is not as surprising, because a major component of M2 is M1.

The Democrats and Team Blue grew M2 by roughly $6 trillion during two separate administrations over 16 presidential years since 1980 , an average rate of $376 billion per year.

The Republicans and Team Red expanded M2 by $10 trillion during four different administrations over 24 presidential years since 1980,   an average rate of $420 billion per year. Team Red prints more M2 and at about a 12 percent quicker rate per year. 

Both M2 and the portion of M1 embedded in the money supply increases by presidency is displayed in the chart below: 

The data from M1 and M2 expansion is clear, while each side of this current political conflict prints more here or there depending on how it is measured, the general tendency is painfully obvious: the monetary differences of each major American political party have been negligible over the past few generations. The American public at large appears to be caught up in a Fiscal Illusion

Implications For Bitcoin

As the M1 and M2 money supply measures continue their pace upwards, the “printing presses” will continue to stream dollars from the Fed into every nook and cranny of the economy and world, all in the name of maximum employment and stable prices. These newly-created dollars are seeking refuge wherever they can retain value. Asset inflation and real inflation are now widely recognized as completely decoupled from typical goods and services inflation, which is often represented as the heavily manipulated CPI (consumer price index). 

Simple supply-and-demand theory explains why the market price of dollars is dropping against scarcer goods such as gold and bitcoin: because the money supply (M1 and M2) is aggressively increasing.

These steep downward dollar price trends against gold (over 50-plus years) and bitcoin (over 10-plus years) align nicely with supply increases of the USD seen in the M1 and M2 data sets. This value and supply alignment also corresponds well with the Quantity Theory of Money, originally put forward by Nicolaus Copernicus in 1517. His “Quantity Theory of Money” states that the general price level of goods and services is directly proportional to the amount of money in circulation, or money supply. Other monetary theories to explore and study on this topic include:

The data and trends are as clear as can be: Irrespective of who comes out ahead in the vote tally, the results of the Electoral College or even who holds the high office of the presidency, the money supply in the United States will likely continue to increase. The Red vs. Blue Team hysteria that we are seeing, with regards to differentiating monetary policy in America, is baseless. 

The dynamics of how easy money interacting with hard money will continue to play out over this presidential term, just as they have in all of the previous terms since President Nixon and his “Nixon Shock.” Projected Winners: Green Printer, Orange Coin. 

This is a guest post by Tyler Bain. Opinions expressed are entirely his own and do not necessarily reflect those of BTC Inc or Bitcoin Magazine.

The post As Red And Blue Debate, Green Printer And Orange Coin Always Win appeared first on Bitcoin Magazine.

Source: Bitcoin magazine

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How Do Seasonal Fluctuations Really Affect Bitcoin Mining?

There has been much ado made recently over supposed seasonal mining fluctuations on the Bitcoin network. 

The narrative goes that, as the rainy season in China comes to a halt around the August to October timeframe each year, cheap and abundant hydroelectricity dries up. This forces many inefficient late-model miners to shut off or move elsewhere to find more affordable, accessible energy — creating migratory or nomadic miners, if you will. 

The narrative also claims that the network sees a significant drop in hash rate and difficulty at yearly intervals roughly matching this seasonal decline in Chinese hydroelectric generation. This certainly appears to be the case now, in fall 2020, as many speculate the recent loss of about 48 exahashes per second (Eh/s) (30 percent of the network’s total hash rate) is due to just this phenomenon. But does the data support this for other years? 

And what about the recent Bitcoin difficulty adjustment at block height 655,200, one of the largest drops in Bitcoin’s history? Clark Moody’s dashboard shows the block difficulty experienced a 16 percent drop based on the aforementioned loss of network hash rate.

Bitcoin Block Production Rate, Difficulty Adjustment And Hash Rate 

The Bitcoin protocol is finely tuned and optimized for certain predictable outcomes. The way the network arrives at these desired results is through a series of carefully designed system rules and guidelines that were crafted into the free and open-source software upon its creation. 

The Bitcoin timechain is a series of blocks that verify, group and order transactions based on a preset series of rules. One such rule is the fact that blocks are added to the chain at a programmatic rate of approximately once every 10 minutes, six blocks per hour and about 144 per day. 

Block difficulty is generally proportional to the amount of computational work miners need to generate to produce a block. The Bitcoin Genesis block had a difficulty of 1. Yesterday, the block difficulty was 19,997,335,994,446. And, at the time of this writing, the block difficulty is 16,787,779,609,932. This means that today, it is about 16.7 trillion times harder to discover a block compared to the first block. Block difficulty is a unitless Bitcoin network metric. 

In order to maintain 10-minute block production rates with an ever-changing amount of miners and hash rate being produced on the network, the software programmatically adjusts block difficulty  every 2,016 blocks, or roughly once every two weeks, commonly referred to as a “Bitcoin block difficulty epoch.” This difficulty adjustment algorithm elegantly maintains an average block production rate, even with wildly fluctuating network hash rates. Over time, as more miners have tried their luck on the network, block difficulty has automatically adjusted upwards to compensate and stabilize block production rates.  

Bitcoin network hash rate and difficulty history, linearly

On the chart above, difficulty is seen as declining every so often after a reduction in hash rate, and increasing as hash rate goes up. If blocks are minted at a rate faster (or slower) than once every 10 minutes, on average, that would mean more (or less) computing power is being pointed toward Bitcoin than the difficulty threshold can accommodate. As more or fewer miners work toward the chain of blocks, the block difficulty target number will be changed to compensate, ensuring blocks are created at a rate of about one every ten minutes.

While we can clearly see the difficulty decreases on the linear chart, the above logarithmic chart makes the hash rate and difficulty drawdowns less perceivable. Historically on the Bitcoin network, block difficulty has trended upward and block difficulty reductions are rare. This is in part due to increasing mining equipment efficiency and effectiveness

There have only been a handful of months over the past decade in which the block difficulty ended at a value lower than when it started. The relentless growth is even more apparent in charts that illustrate an average Bitcoin network hash rate by month and year. There has not been a month, year-over-year, in which the Bitcoin network hash rate went down.

Accounting For Seasonal Fluctuations 

So, now that we have established that the Bitcoin hash rate, over long enough timeframes, is aggressively NgU (Number Go Up), is there validity to the theory that seasonal fluctuations cause significant changes in network hash rate? 

Per the chart above, it appears that the years 2020, 2019 and 2018 all saw average network hash rates trend lower toward the end of the year than they were in late summer and early fall. And what about other years?

Fall 2013

For 2013, the network doesn’t appear to have had a downward difficulty adjustment. This aggressive upward movement may be due to the revolution in ASIC effectiveness that was occurring during this timeframe. 

Fall 2014

For 2014, there were some difficulty adjustments downward during the late November timeframe. However, difficulty appears to be trending upward during most of the season. 

Fall 2015

2015 is a similar story to 2013: the network doesn’t appear to have had a downward difficulty adjustment. More and better ASICs were being rapidly developed at this time.

Fall 2016

So, 2016 sees a small difficulty adjustment downward around the October timeframe. Also, it appears that the growth of the network hash rate and difficulty slows down during the same timeframe, however, NgU.

Fall 2017

2017 tells a similar story to 2016: network hash rate growth stalls and difficulty actually adjusts downwards a few different times. This is especially noteworthy, as price was increasing aggressively along these same timelines. However, these fluctuations may not be due to migratory miners. Along these same seasonal time frames in 2017, some major miners were forking off the network and manipulating hash rate to pursue other avenues. 

Fall 2018

The fall of 2018 may show the most obvious seasonal trend of difficulty and hash rate declining rapidly across the network. It’s important to note that during these timeframes, the price was also falling from all-time highs. A significant percentage of the network, almost half, went offline seasonally. Yet, due to the elegance of the difficulty adjustment algorithm, the peer-to-peer network continued churning along. 

Fall 2019

The fall of 2019 doesn’t show as significant of a decline as the 2018 season does, but it does show a few significant difficulty adjustments downward and hash rate reductions. It also shows a similar stunting of network hash power growth during the same time frames. 

Fall 2020

So, this brings us to today. Mining centralization is a common criticism of Bitcoin and the narrative that many Chinese ASICs shut off seasonally appears to be valid for the fall of 2020. Where else would folks have about 48 Eh/s (30 percent of the network, as noted above) sitting idly by waiting for abundant and affordable energy? This works out to be about 3 million Antminer S9 ASIC mining units. 

So, where does this dropoff rank among previous difficulty drops in the October and November time frames? 

With chain data we can actually see that 2011 had the largest downward difficulty adjustment and an even larger month-over-month drop, occurring over a few different difficulty adjustments. This November 2020 difficulty drop is the largest we have seen in recent years caused by apparent seasonal fluctuations from enterprising Chinese hydroelectric miners. 2012 to 2015 did not see any difficulty drops between these months. 

How do these seasonal difficulty and hash rate fluctuations stack up to the entirety of the block history on the Bitcoin network? The histograms for both the difficulty adjustments and hash rate changes offer some insight: 

Is This A Bitcoin Mining Death Spiral?

When the network hash rate or price of bitcoin goes down, there is always much speculation on the possibility of what is colloquially referred to as the “Mining Death Spiral.” The claim goes that if the price drops low enough, miners decide to shut down and the network loses a sizable percentage of its hash rate. This would force some miners to liquidate their earnings, pushing market prices lower and furthering this vicious feedback loop until a death spiral ensues. For a network like Bitcoin, this would result in miners shutting down, blocks ceasing to be mined and the timechain ceasing to propagate  —  Bitcoin would fail. 

However, as an example, Dogecoin ($DOGE) is still minting blocks, so this doom and gloom theory may not hold true for these types of distributed systems. There are many enthusiasts, fanatics and “miners of last resort” that will maintain some of these systems simply for the sake of maintaining them, and the faith of belief. 

So, let us imagine if 50 percent of the miners stopped mining right at the difficulty adjustment block and shut down; what would happen? 

It would take the remaining half of the network about twice as long to find the 2,016 blocks. This would mean four weeks, or about one month, to get to the next difficulty adjustment point. What would the ramifications be for the network? 

The mempool would begin to build up, transactions would become delayed and fees would increase as folks bid up new, even more scarce block space. This is actually exactly what we have seen for a few days after the hash rate recently dropped off. 

However, the Bitcoin network eventually adjusted difficulty, as it has each time that 2,016th block has come up in the past. This difficulty adjustment algorithm is a very elegant solution to a few different challenges facing the Bitcoin network, and because of these types of solutions, the self-regulating system continues to propagate forward.

The post How Do Seasonal Fluctuations Really Affect Bitcoin Mining? appeared first on Bitcoin Magazine.

Source: Bitcoin magazine

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Introducing CBPI: A New Way To Measure Bitcoin Network Electrical Consumption

There have been many claims in recent years that bitcoin and the miners securing the network via SHA-256 proof of work use an unconscionable amount of energy. But what data are these claims based on, are the source calculations using flawed or sound approaches and assumptions? How much electrical power does the network draw and how much electrical energy has the Bitcoin network used historically?

Methodologies And Misconceptions

Due to the vast, globally distributed topology of the Bitcoin network, the amount of electrical power and energy that miners consume isn’t exactly verifiable, instead it must be estimated. Among the energy consumption hysteria over the previous few years, a surprisingly large number of reputable sources have weighed in and attempted to estimate Bitcoin’s network energy consumption in more level-headed and data-derived ways:

Estimation methodologies seem to fall into two major categories: economics-based approaches rooted in financial assumptions, as well as physics-based approaches planted in engineering principles. These two estimation approaches were thoroughly compared and contrasted at BTC2019.

It’s important to understand when digesting all of these yearly usage estimations that electrical consumption is typically measured in two ways: instantaneously (power, watts, kilowatts, etc.) and that same instantaneous power measurement integrated over time (energy, joules, kilowatt-hours (kWh), etc.)

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Small Bitcoin miners draw about 1,300 watts of power and use about 31,200 watt hours of energy over a 24-hour period.

The Problems With Economics-Based Network Energy Estimations

Economicsbased approaches that estimate the Bitcoin network energy consumption generally assume perfectly rational market behavior, and can easily be manipulated with a few input variable misassumptions.

In theory, the Bitcoin mining industry is rational, profit maximizing and perfectly competitive: mining marginal revenue should tend to equal marginal cost (MR = MC). Meaning, on long enough time horizons, the market should find an equilibrium, where the cost of energy consumed in a unit of bitcoin’s production should be roughly equivalent to the unit’s market value at the time of minting. This calculation methodology can be distilled as, “How much can Bitcoin network miners afford to spend on electricity?” 

Typically, these types of estimations are too dependent on a single volatile variable: the market exchange price of bitcoin. Below is a quick, simplified example of this type of estimation:

[MR] = [MC]

[(Blocks/Day)*(Reward/Block)*(BTC Price) ]=[(kWh/Day)*($/kWh)]

[(Blocks/Day)*(BTC/Block)*($/BTC)]/($/kWh) = (kWh/Day) = Energy/Day

Let’s try this estimation. Bitcoin blocks are generated roughly every 10 minutes — a rate of 6 per hour, or 144 every day. Currently, a single bitcoin block contains 6.25 BTC of coinbase block subsidy; that’s 37.5 BTC per hour, or 900 newly-minted bitcoin rewarded to miners daily. With bitcoin’s current market exchange price of about $10,750 at the time of this writing, that is roughly $9,675,000 earned per day that bitcoin miners have available to spend on electricity.

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Average U.S. electrical costs in cents per kWh by state, per EIA data

[(144)*(6.25/Block) * $10,750] / ($0.10/kWh) = (96.75 GWh/Day)

This amount of daily energy equates to roughly 35.3 TWh of yearly usage that the bitcoin miners could afford to consume, if we take a snapshot today and assume constant bitcoin price for a year and U.S. average electrical costs.

While this method is overly reliant on bitcoin price, it is also heavily dependent on the assumed electrical energy cost for miners. The calculations and conclusions of this kind of estimate can be drastically different or even manipulated depending on the assumptions used as inputs: energy costs ($/kWh) and the price of bitcoin ($/BTC).

Here we used the average U.S. electrical cost of $0.10/kWh. However, in the U.S., electrical costs actually vary seasonally, from state to state, city to city and, in some cases, neighborhood to neighborhood. Global electrical costs have the same incongruence. This isn’t even including wide-ranging industrial, commercial or residential electrical energy rates, adding even more sources of error to these economics-based estimation techniques. And, in fact, this calculation’s heavy energy price dependence has yet another flaw: some miners’ high in ingenuity have near-zero fuel cost as they harvest excess, otherwise wasted, inaccessible or curtailed energy sources.

This quick exercise highlights, in my opinion, why this type of economics-based estimation approach is a gross oversimplification fraught with the following issues:

  • Bitcoin mining, hash rate and, therefore, network energy consumption isn’t as responsive to sudden price movements as these economics-based estimation methods are.
  • The economics-based model claims energy usage is cut in half along with network miner rewards after each bitcoin block reward halving cycle, which is every 210,000 blocks or about four years, while difficulty and proof-of-work-based data disproves this.
  • This type of model assumes a single average global energy cost ($/kWh); electrical energy costs vary widely by region, seasonally and even by energy source.
  • This is likely to be an upper-bound estimation.

The Benefits Of Physics-Based Network Energy Estimations

Physicsbased network energy estimation approaches, on the other hand, tend to be a very rigorous type of “running of the numbers” that the Bitcoin community is accustomed to.

These methods use independently verifiable on-chain difficulty, proof-of-work data and original equipment manufacturer (OEM) -published heat rate specifications to more accurately estimate historical energy inputs into the bitcoin mining system. The physics estimation attempt may best be described as a “bitcoin stoichiometric ratio unit analysis calculation:”

Bitcoin Difficulty (Unitless) → Bitcoin Hash Rate (Daily Average TH/s)

Daily Average Hash Rate (TH/s) → Yearly Hashes (TH/Year)

Yearly Hashes (TH/Year) * Yearly Hash Heat Rate (Joules/TH) = (J /Year)

Energy Per Year (Joules/Year) → (kWh/Year) → (TWh/Year) → (ktoe/Year)

So, let’s try out this style of estimation using bitcoin proof-of-work difficulty data and OEM-published data. Bitcoin network difficulty self-adjusts once every 2,016 blocks, or roughly once every two-week period. This difficulty adjustment is to compensate for block production speed discrepancies and, thus, network hash rate fluctuations.

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Bitcoin network hash rate (Th/s) compared to difficulty, April 2015 to April 2020.

This difficulty and proof-of-work relationship allows us to derive an estimate for network hash rate based on the block production rate and the associated difficulty level. From the amount of work done at the various difficulty levels over the previous decade, we can roughly estimate the amount of SHA-256 hashes computed per year on the Bitcoin network, shown below in terahashes per year (Th/year) or a trillion hashes per year. We can also do this same type of exercise with daily data to produce more granular calculations (spoiler: keep reading).

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Estimated total Bitcoin network terahashes by year.

Bitcoin is on pace to have roughly 3,934 yotahashes computed on the network in 2020 or  about 3,934 septillion hashes (“yota” and “septillion” are the largest of the Scientific International (SI) prefixes to date, (10²⁴)),

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Scientific International (SI) unit prefixes, based on NIST data here.

Now that we have an estimation for the amount of hashes per year, next we must compile mining rig efficiency data over the past 11 years to understand how much energy would have been required to produce that amount of work.

Here it is important to understand the different types of mining equipment that have provided work toward the Bitcoin blockchain over the years. Each era and year has distinctly different proof-of-work efficiency characteristics, which change the network’s energy consumption values over time. From the humble beginnings of the Bitcoin genesis block being built by work derived from CPUs (central processing units), to blocks eventually being constructed with GPUs (graphics processing units), then on to FPGAs (field programmable gate arrays), and finally ASICs (application-specific integrated circuits) the Bitcoin network has evolved at a stunning pace.

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Bitcoin network hash rate colored by general device type eras, in terahashes per second.

Important note: efficiency is defined as useful work performed over energy expended to complete that work (terahash/joules — Th/J). However, ASIC original equipment manufacturers typically cite a type of heat rate specification, or the inverse of efficiency, showing energy expended over useful work (joules per terahash — J/Th).

As you can see in the log scale chart below, over the past eight years, bitcoin mining ASICs’ heat rates have been steadily marching lower every year, meaning network mining efficiency has been increasing.

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Manufacturer published SHA-256 ASIC energy per hash heat rate in joules/terahash by bitcoin mining rig.

Translating this data into an average yearly heat rate (chart below) shows a similar steep decline during the entire history of bitcoin mining. CPU, GPU and FPGA benchmarks along with published OEM power usage data was used to estimate 2009 to 2012 network average heat rate. ASIC miners announced in 2020 were visualized above and below to show the continued decrease in hash heat rate, but they were discarded from the energy estimations as they are not yet publicly available.

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Bitcoin mining equipment average yearly heat rate, the inverse of efficiency (j/Th)

So, now that we have compiled all of the necessary data (yearly hashes and yearly hash heat rate), let’s combine them via an engineer’s attempt at bitcoin mining energy stoichiometry:

Yearly Hashes (TH/Year) * Yearly Hash Heat Rate (Joules/TH) = (J /Year)

Energy Per Year (Joules/Year) → (kWh/Year) → (TWh/Year)

Simply multiply the yearly work completed (terahash/year) by the yearly estimated heat rate (in joules/terahash) for miners on the system and you arrive at a joules/year estimation. We will convert from joules/year to kWh/year (a kWh is equal to 3.6 megajoules) and below those yearly energy estimates are charted.

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Yearly estimates For Bitcoin network energy consumption, in kWh

However, this physics-based estimation method also has some issues:

  • The quantity of active miners by level of efficiency isn’t known, and this physics-based model assumes equal participation from all miner models available on the market by year released.
  • This model also uses a step function for yearly heat rate data as in input. That yearly data abruptly changes at the first of each year, a gradual heat rate decline would be more realistic as older miners steadily retire and new ones fire up.
  • It assumes old miners retire after a year, which is also unlikely as equipment life cycles are now ranging for two or more years.
  • This is likely to be a lower-bound type of estimation.

Comparing Different Network Energy Estimations

Where do these yearly energy consumption estimates fall among the previously-cited calculation attempts? Interestingly enough, both of our calculations, even using drastically different methodologies and with all of the shortcomings discussed above — the economic-based estimation (35.3 TWh) and the physics-based estimation (40.17 TWh) — are very similar in value. They also fall within the range of a variety of other popular estimations from noteworthy individuals, entities and institutions shown in the chart below. That all of these estimations are fairly similar in magnitude lends credibility to the various different estimators as well as the wide variety of methodologies and different assumptions used.

Noteworthy below: it appears that the Bitcoin network hash rate (EH/s) is beginning to decouple from the general yearly energy (TWh/year) estimation trend. This may be due to the decreasing heat rate of SHA-256 ASIC mining equipment if the estimate is physics based, or due to the halving and price stagnation if the estimate is economics based.

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Bitcoin network hash rate (EH/s) and yearly electrical energy (TWh/year) estimates from various sources

This chart above shows yearly energy estimation snapshots at time of publishing in TWh/year, but a few of these sources (University of Cambridge [C-BECI] and Alex de Vries [D-BECI]) actually publish these yearly estimates on a daily graph going back a few years. This gets back to the previous energy vs. power discussion: logic should prevent plotting yearly energy estimations on a daily axis. 

Regardless, I thought it would be worth comparing these published estimates with our above calculations using more continuous time series data going back to late 2017 (the previous market all-time high). The economic and physics calculations, the Cambridge estimates, as well as Digiconomist‘s results are all fairly similar in magnitude over time, again adding some peer review and validity to these different estimation techniques.

A yearly energy estimation comparison (TWh/year)

Our above estimation methodologies appear to align nicely with the other various daily interval yearly energy estimates, so they were averaged together to create a sort of Composite Bitcoin Energy Index (CBEI) as shown below in TWh/year. Each of these estimations have different assumptions, varying levels and sources of inaccuracy, and thus their composite may be more accurate. This composite of estimations (CBEI) has just recently retested the 60 TWh threshold for total yearly Bitcoin network energy consumption.

A yearly energy estimation average, Composite Bitcoin Energy Index (CBEI)

How does this composite energy index compare to Bitcoin network hash rate over time? The CBEI shows a similar decoupling as hash rate and energy around early 2019 with hash rate continuing to rise and energy consumption staying relatively steady as ASIC heat rates and bitcoin mining incentives have shrunk.

Interestingly, snapshot bitcoin consumption estimations are commonly extrapolated for an entire year, expressed as an energy value in TWh/Year without supporting time data or evidence. Daily network power estimations would be much preferred to all of these yearly energy consumption estimates plotted on a daily chart. The chart crime in this case is the egregious graphical error that makes folks massively misinterpret the data: yearly energy estimates graphed on a daily axis. So, I took the liberty of converting these daily interval estimates into a daily power estimation chart to correct for these above chart errors that force data misinterpretations.

I present the Composite Bitcoin Power Index (CBPI) compiled from the D-BECI and Minimum, the C-BECI Maximum, Minimum and Estimated, as well as our above economics- and physics-based estimates

This CBPI composite estimates for Bitcoin’s instantaneous electrical usage as expressed in watts, the unit of electrical power. The CBPI peaked recently at nearly 7.58 GW, or about 6 DeLorean time machines at 1.21 Gigawatts (or should I say jigawatts?).

CBPI In Context

Energy values that large are difficult to digest, especially in a yearly context, so let’s put these estimations in perspective with some quick comparisons:

  • 650 TWh/year consumed by the banking system
  • 200 TWh/year used in gold mining
  • 75 TWh/year used on PC and console gaming
  • 60 TWh/year on bitcoin mining (CBEI)
  • 11 TWh/year used on paper currency and coin minting
  • 7 TWh/year used on Christmas lights in the U.S.
A comparison of our index to other popular energy usage estimates.

Based on our estimations above, the Bitcoin network consumes roughly 40 to 60 TWh/Year or around 0.15 percent of global yearly electricity generation (26,700 TWh) and only about 0.024 percent of global total energy production (14,421,151 ktoe). (A ktoe is also a unit of energy: a kiloton of oil equivalent, 11.36 MWh.)

So, Bitcoin energy consumption today is only a very tiny portion of what many consider to be a significant civilization-level problem: ever-increasing human energy consumption. Check out interesting solutions to this problem outlined a century ago by Nikola Tesla. As recently as September 2020, a study claimed that nearly 76 percent of the Bitcoin network is powered by clean energy sources. Also, remember that once Einstein discovered mass-energy equivalence and humanity harnessed the energy embedded in the atom, energy for the advancement of mankind has become materially abundant.

This is a guest post by Tyler Bain. Opinions expressed are entirely his own and do not necessarily reflect those of BTC Inc or Bitcoin Magazine.

The post Introducing CBPI: A New Way To Measure Bitcoin Network Electrical Consumption appeared first on Bitcoin Magazine.

Source: Bitcoin magazine