Mainland China<\/p>\n

44.45<\/p>\n

32.89<\/p>\n

77.34<\/p>\n

73.48<\/p>\n

5.25<\/p>\n

United States<\/p>\n

4.65<\/p>\n

25.20<\/p>\n

29.85<\/p>\n

32.89<\/p>\n

-9.24<\/p>\n

Kazakhstan<\/p>\n

3.18<\/p>\n

12.06<\/p>\n

15.24<\/p>\n

15.94<\/p>\n

-4.39<\/p>\n

Russia<\/p>\n

4.71<\/p>\n

7.59<\/p>\n

12.29<\/p>\n

12.28<\/p>\n

0.081<\/p>\n

Malaysia<\/p>\n

3.31<\/p>\n

4.13<\/p>\n

7.44<\/p>\n

7.29<\/p>\n

2.06<\/p>\n

Canada<\/p>\n

0.80<\/p>\n

5.25<\/p>\n

6.05<\/p>\n

6.62<\/p>\n

-8.61<\/p>\n

Iran<\/p>\n

2.33<\/p>\n

3.06<\/p>\n

5.39<\/p>\n

5.13<\/p>\n

4.82<\/p>\n

Germany<\/p>\n

0.67<\/p>\n

3.31<\/p>\n

3.98<\/p>\n

4.18<\/p>\n

-4.78<\/p>\n

Ireland<\/p>\n

0.62<\/p>\n

2.69<\/p>\n

3.31<\/p>\n

3.43<\/p>\n

-3.50<\/p>\n

Singapore<\/p>\n

0.31<\/p>\n

1.13<\/p>\n

1.43<\/p>\n

1.56<\/p>\n

-0.083<\/p>\n

Other (Excluding Singapore)<\/p>\n

3.69<\/p>\n

6.73<\/p>\n

10.42<\/p>\n

10.63<\/p>\n

-1.98<\/p>\n

Total<\/p>\n

68.72<\/p>\n

104.04<\/p>\n

172.76<\/p>\n

173.42<\/p>\n

-0.38<\/p>\n<\/div>\n

Another tricky thing about this study is that they combined the energy use for both 2020 and 2021 into one number. This was really tricky because if you look at their figures, you\u2019ll notice that the biggest text states, \u201cTotal: 173.42 TWh\u201d. It\u2019s also slightly confusing because the figure caption states, \u201c2020-2021\u201d, which for many people would be interpreted as a period of 12 months, not 24 months. Well, whatever. I broke them up into their individual years so everyone could see the steps that were taken to get to these numbers.<\/p>\n

Look at the far right column with the header, \u201cPercent Change Between 2020 + 2021 Calculations (%)\u201d. I calculated the percent change between my calculations and Chamanara et al.\u2019s. This is rather curious, isn\u2019t it? Based on my conversations with the researchers at CCAF, the numbers should be identical. Maybe the changelog doesn\u2019t reflect a smaller change somewhere, but our numbers are slightly different nonetheless. China has a greater share and the United States has a smaller share in the data that CCAF shared with me compared to the UN study. Despite this, the totals are fairly close. So, let\u2019s give the authors the benefit of the doubt and say that they did a reasonable job calculating the energy share, given the limitations of the CBECI model. Please bear in mind that noting that their calculation was reasonable doesn\u2019t mean that it\u2019s reasonable to use these historical estimates to make claims about the present and future and direct policy. It isn\u2019t.<\/p>\n

One evening while working by candlelight, I glanced to my left and saw Frank\u2019s stabbing, black pupils (the Donnie Darko character I mentioned earlier) staring at me like two pieces of Stronghold waste coal, fixed in a quiet bed of pearly sand. He was reminding me that this report was still not finished and something about time travel. I grabbed my extra-soft curls (I switched to bar shampoo, it\u2019s a godsend for frizz) and yanked as hard as I could. Willie Nelson\u2019s 1974 Austin City Limits pilot episode blasting on my cheap-ass Chinese knock-off monitor\u2019s mono speakers was moving through my ears like heroin through Lou Reed\u2019s 4-lanes wide network of veins. Begrudgingly, I accepted my fate. I needed to go deeper down this rabbit hole. I needed to do a deeper analysis of the 2020 and 2021 CBECI data to show how important it is to do an annual analysis and not blur the years into one calculation. Realizing I was out of my hard liquor of choice, a splash of sherry in a Shirley Temple (shaken, not stirred), I grabbed a bottle of bootleg antiseptic that I got during the pandemic lockdown and chugged.<\/p>\n

I flipped through my notes. I have lots of notes because I\u2019m a serious person. What about the mining map issues? Can we do this through an analysis of the two separate years? What was happening for each of the ten countries? Does that tell us anything about where hashrate went after the China ban? What about the Kazakhstan crackdown? That\u2019s post-2021, but the UN study acts like it never happened when they\u2019re talking about the current mining distribution\u2026<\/p>\n

Not to the authors\u2019 credit, they failed to mention to the peer-reviewers and to their readers that the mining map data only goes through January 2022. So, even though they talk about bitcoin mining\u2019s energy mix as if it represents the present, they are completely wrong. Their analysis only captures historical trends, not the present and definitely not the future.<\/p>\n

See this multi-colored plot of CBECI’s estimated daily energy use (TWh) from January 2020 through August 31, 2023? At this macro scale, we see plenty of variability. But also it\u2019s apparent just from inspection that each year is different from the next in terms of variability and energy use. There are a number of possible reasons for the cause of variability at this scale. Some possible influences on energy use could be bitcoin price, difficulty adjustment, and machine efficiency. More macroscale influences could be as a result of regulation, such as the Chinese bitcoin mining ban that occurred in 2021. Many of the Chinese miners fled the country for other parts of the world, Kazakhstan and the United States are two countries where hashrate found refuge. In fact, the power of the Texas mining scene really came to be at this unprecedented moment in hashrate history.<\/p>\n

Look at the histograms for 2020 (top left), 2021 (top right), 2022 (bottom left), and 2023 (bottom right). It\u2019s obvious that for each year, the estimated annualized energy consumption data shows different distributions. Even though we do see some possible distribution patterns, we have to be careful not to take this as a pattern that happens every four-year cycle. We need more data to be sure. For now, what we can say is that some years in our analysis show a bimodal distribution while other years show a kind of skewed distribution. The main point here is to show that the statistics for energy use for each of these four years are different, and distinctly so for the two years that were used in Chamanara et al.\u2019s analysis.<\/p>\n

In the UN study, the authors wrote that bitcoin mining exceeded 100 TWh per year in 2021 and 2022. However, if we look at the histograms of the daily estimated annualized energy consumption, we can see that daily estimates vary quite a bit, and even in 2022 there were many days where the estimated energy consumption was below 100 TWh. We\u2019re not denying that the final estimates were over 100 TWh in the older estimated data for these years. Instead, we\u2019re showing that because bitcoin mining\u2019s energy use is not constant from day to day or even minute-to-minute, it\u2019s worth doing a deeper analysis to understand the origin of this variability and how it might affect energy use over time. Lastly, it\u2019s worth noting that the updated data now estimates the annual energy use to be 89 TWh for 2021 and 95.53 TWh for 2022.<\/p>\n

One last comment, Miller et al. 2022 showed that operations (specifically buildings) with high variability in energy use over time are generally not suitable for emission studies that use averaged annual emission factors. Yet, that\u2019s what Chamanara et al. chose to do, and what so many of these bullshit models tend to do. A good portion of bitcoin mining doesn\u2019t operate like a constant load, Bitcoin mining can be highly flexible in response to many factors from grid stability to price to regulation. It\u2019s about time that researchers started thinking about bitcoin mining from this understanding. Had the authors spent even a modest amount of time reading previously published literature, rather than operating in a silo like Sai and Vranken noted in their review paper, they might have at least addressed this limitation in their study.<\/p>\n

\u2014<\/p>\n

So, I\u2019ve never been to a honky tonk joint before. At least not until I found myself in a taxi cab with several other conferencegoers at the North American Blockchain Summit. Fort Worth, Texas, is exactly what you\u2019d imagine. Cowboy boots, gallon-sized cowboy hats, Wrangler blue jeans, and cowboys, cowboys, cowboys everywhere you looked through the main drag. On a brisk Friday night, Fort Worth seemed frozen in time, people actually walked around at night. The stores looked like the kind of mom-and-pop shops you\u2019d see on an episode of The Twilight Zone. I felt completely disoriented.<\/p>\n

My companions convinced me that I should learn how to two-step. Me, your standard California girl, whose physics advisor once told her that while you can take the girl out of California, you can\u2019t take California out of the girl, should two-step?! I didn\u2019t know a two-step from an electric slide and the only country I remember experiencing was a Garth Brooks commercial I saw once on television when I was a child. He was really popular in the nineties. That\u2019s about as much country as this bitcoin mining researcher gets. The place was filled with kitschy gift shops and bright lights everywhere radiating from neon signs. At the center of the main room, a bartender wearing a black diamond studded belt with a white leather gun holster and lined with evenly spaced silver bullets. Who the hell knows what kind of gun he was packing, but it did remind me of the guns in the 1986 film, Three Amigos.<\/p>\n

It was here, against the backdrop of what sounded like a country band that wasn\u2019t entirely sure that it was country, that I watched the Texas Blockchain Council\u2019s Lee Bratcher address a ball with the kind of trigonometric grace that you could only find at the end of a cue and land that billiard in a tattered, leather pocket for what seemed like the hundredth time that night. The smooth clank of billiard against billiard awoke something inside me. I realized that I was not yet out of the rabbit hole that Frank sent me down. I remembered somewhere scribbled in my notes that I had not plotted the hashrate share over time for the countries mentioned in the UN study. So, at half past three in the morning, I threw my head back to take a swig of some club soda and bumped it against the wall of the photo booth where nuclear families could pose with a mechanical bull, and fell unconscious.<\/p>\n

Three hours later, I was back in my hotel room. Thankfully, someone placed some worthless fiat in my hand, loaded me into a cab, and had the driver take me back to the non-smoking room I checked into at the very center of the decay of twenty-first-century business travel, the Marriott Hotel. Fuzzy-brained and bleary-eyed, I let the blinding, dangerously blue light from my computer screen wash over my tired face and increase my chances of developing macular degeneration. I continued my analysis.<\/p>\n

What follows are a series of plots of CBECI mining map data from January 2020 through January 2022. Unsurprisingly, Chamanara et al. focus attention on China\u2019s contribution to energy use, and subsequently to its associated environmental footprint. China\u2019s monthly hashrate peaked at over 70 percent of the network\u2019s total hashrate in 2020. In July 2021, that hashrate share crashed to zero until it recovered to about 20 percent of the share at the end of 2021. We don\u2019t know where it stands today, but industry insiders tell me it\u2019s likely still hovering around this number, which means that in absolute terms, the hashrate is still growing there despite the ban.<\/p>\n

Russia, also unsurprisingly, gets discussed as well. Yet, based on the CBECI mining map data from January 2020 through January 2022, it\u2019s hard to argue that Russia was an immediate off-taker of exiled hashrate. There\u2019s certainly an immediate spike, but is this real or just miners using VPN to hide their mining operation? By the end of 2021, the Russian hashrate declined to below 5 percent of the hashrate and in absolute terms, declined from a brief peak of over 13 EH\/s to a bit over 8 EH\/s. When looking at the total year\u2019s worth of CBECI estimated energy use for Russia, we do see that Russia did hold a significant portion of hashrate, it\u2019s just not clear that when working with such a limited set of data, we can make any reasonable claims about the present contribution to hashrate and environment footprint for the network.<\/p>\n

The most controversial discussion in Chamanara et al. deals with Kazakhstan\u2019s share of energy use and environmental footprint. Obviously, the CBECI mining map data shows that there was a significant increase in hashrate share both in relative and absolute terms. It also appears that this trend started before the China ban was implemented, but certainly appears to rapidly increase just before and after the ban was implemented. However, we do see a sharp decline from December 2021 to January 2022. Was this an early signal that the government crackdown was coming in Kazakhstan?<\/p>\n

In their analysis, Chamanara et al. ignored the recent Kazakhstan crackdown, where the government imposed an energy tax and mining licenses on the industry, effectively pushing hashrate out of the country. The authors overemphasized Kazakhstan as a current major contributor to bitcoin\u2019s energy use and thus environmental footprint. If the authors had stayed within the limits of their methods and results, then noting the contribution of Kazakhstan\u2019s hashrate share to the environmental footprint for the combined years of 2020 and 2021 would have been reasonable. Instead, not only do they ignore the government crackdown in 2022, but they also claim that Kazakhstan’s hashrate share increased by 34% based on 2023 CBECI numbers. CBECI\u2019s data has not been updated since January 2022 and CCAF researchers are currently waiting for data from the mining pools that will allow them to update the mining map.<\/p>\n

I know I\u2019ve shown you, my faithful reader, a lot of data, but go ahead and have another shot of the hardest liquor you have in your cabinet, and let\u2019s take a look at one more figure. This one represents the United States hashrate share in the older CBECI mining map data. The trend we see for the United States is also similar for Canada, Singapore, and what CBECI Calls \u201cOther countries\u201d, which represent the countries that did not make the top ten list for hashrate share. There\u2019s a clear signal that reflects what we know to be true. The United States took a significant portion of Chinese hashrate and this hashrate share grew rapidly in 2021. While we know that the CBECI mining map data is limited to less than a majority of the network hashrate, I do think that their share is at least somewhat representative of the network\u2019s geographic distribution. Hashrate geographic distribution seems to be heavily shaped by macro trends. While electricity prices matter, government stability and friendly laws play an important role. Chamanara et al. should have done this kind of analysis to help inform their discussion. If they had, they might have realized that the network is responding to external pressures at varying times and geographic scales. We need more data before we can make strong policy recommendations when it comes to the effects of bitcoin\u2019s energy use.<\/p>\n

\u2014<\/p>\n

At this point, I was no longer sure if I was a bitcoin researcher or an NPC, lost in a game where the only points tallied were for the intensity of self-loathing I was feeling for agreeing to this undertaking. At the same time, I could smell the end of this analysis was near and that, with enough somatic therapy and EMDR, I might actually remember who I used to be before I got dragged into this mess. Just two days prior, Frank and I had a falling out over whether Courier New was still the best font for displaying mathematical equations. I was alone in this rabbit hole now. I dug my fingers into the dirt walls surrounding me and slowly clawed my way back to sanity.<\/p>\n

Upon exiting the hole, I grabbed my laptop and decided it was time to address the study\u2019s environmental footprint methodology, wrap up this puppy, and put a bow on it. Chamanara et al. claimed that they followed the methods used by Ristic et al. (2019) and Obringer et al. (2020). There are a few reasons why their environmental footprint approach is flawed. First, the footprint factors are typically used for assessing the environmental footprint of energy generation. In Ristic et al., the authors developed a metric called the Relative Aggregated Factor that incorporated these factors. This metric allowed them to evaluate the placement of new electricity generators like nuclear or offshore wind. The idea behind this approach was to be mindful that while carbon dioxide emissions from fossil fuels were the main driver for developing energy transition goals, we should also avoid replacing fossil fuel generation with generation that could create environmental problems in different ways.<\/p>\n

Second, Obringer et al. used many of the factors listed in Ristic et al. and combined them with network transmission factors from Aslan et al. (2018). This was a bad move because Koomey is a co-author on this paper, so it shouldn\u2019t be surprising that in 2021, Koomey co-authored a commentary alongside Masanet where they called out Obringer et al. In Koomey and Masanet, 2021, the authors chided the assumption that short-term changes in demand would lead to immediate and proportional changes in electricity use. This critique could also be applied to Chamanara et al., which looked at a period when bitcoin was experiencing a run-up to an all-time high in price during a unique economic environment (low interest rates, COVID stimulus checks, and lockdowns). Koomey and Masanet made it clear in their commentary that ignoring the non-proportionality between energy and data flows in network equipment can yield inflated environmental-impact results.<\/p>\n

More importantly, we have yet to characterize what this relationship looks like for bitcoin mining. Demand for traditional data centers is defined by the number of compute instances needed. What is the equivalent for bitcoin mining when we know that the block size is unchanging and the block pace is adjusted every two weeks to keep an average 10-minute spacing between each block? This deserves more attention.<\/p>\n

Either way, Chamanara et al. did not seem to be aware of the criticisms of Obringer et al.\u2019s approach. This is really problematic because as mentioned at the start of this screed, Koomey and Masanet laid the groundwork for data center energy research. They should have known not to apply these methods to bitcoin mining because while the industry has differences from a traditional data center, it\u2019s still a type of data center. There\u2019s a lot that bitcoin mining researchers can take from the torrent of data center literature. It\u2019s disappointing and exhausting to see papers published that ignore this reality.<\/p>\n

What more can I say other than this shit has to stop. Brandolini\u2019s Law is real. The bullshit asymmetry is real. I really want this new halving cycle to be the one where I no longer have to address bad research. While I was writing this report, Alex de Vries published a new bullshit paper on bitcoin mining\u2019s \u201cwater footprint\u201d. I haven\u2019t read it yet. I\u2019m not sure that I will. But if I do, I promise that I will not write over 10,000 words on it. I\u2019ve stated my case and made my peace with this genre of academic publishing. It was a fun ride, but I think it\u2019s time to practice some self-care, treat myself to several evenings of healthy binge-watching, and dream of the ineffable.<\/p>\n

\u2014<\/p>\n