There are currently only two credible estimates for global data center energy consumption. These range from 196 TWh (Masanet et al., 2020) to 400 TWh (Hintemann, 2020) for 2020. Both papers are updated estimates from many years of related publications (from the same authors) which primarily use bottom-up calculations based on low-level hardware energy consumption and shipment statistics to provide estimated models. The difference mainly comes down to the system boundaries as to whether cryptocurrency mining is included, and the extent of workload migration from old data centers to the cloud.
These figures offer a rough figure of 1-2% of total global electricity demand coming from data centers. Although we can make some hand-waving estimates about what that means for carbon footprint, to get anywhere near an acceptable level of accuracy would require at least regional- and ideally country-level energy stats and carbon emissions factors. Even countries as close together as the UK and France have very different grid mixes which would affect the total carbon footprint of data centers operating in those countries.
Over the last decade, IT has seen massive growth in usage. More people are using more IT services. IP traffic has grown almost x20 over that period. But technology has also been improving. Computers, servers and network equipment are more efficient, so the growth in usage has decoupled from the energy consumption.
That isn’t reflected in the common narrative around data center energy because there are several different methodologies for assessing IT energy, and most of them require nuance to understand. The mainstream media is not good at nuance, so the easiest is to take values for a given year and apply historical growth rates, maybe with an adjustment for “efficiency”. This extrapolation-based approach works fine in some situations, but those need to be situations with very few variables that affect the growth. IT is not one of them.
Energy efficiency improvements are just one variable. Processors are more powerful. Infrastructure is moving to the cloud. Hyperscale data centers have become very efficient. More renewable energy is being deployed for data centers. All these impact energy estimates, and are very difficult to accurately estimate when using extrapolation based methodologies.
Extrapolation methodologies have their place – they can be useful for creating simple scenarios to help make broad decisions – but they are more often used to back up a predetermined narrative. The situation is actually more nuanced than meets the eye.
This is a problem for the mainstream narrative that wants to make a simple case that data centers are using loads of energy, emitting lots of carbon, and are bad for the planet.
You will often see discussions of the vast amount of energy consumed by data centers emitting lots of carbon and damaging the planet. These tend to be used by the mainstream media, lobby groups urging “digital sobriety” or case studies showing how much a particular technology or product has “saved” the planet. Most of these stem from extrapolation based models, the most commonly cited of which is Andrae, A.S.G. & Edler, T. (2015).
Whenever you see crazy data center and/or IT energy estimates, you can bet the numbers are based on Andrae and Edler (2015), or one of the followup papers. These use simplistic extrapolation methodologies to arrive at absurd numbers, not least because they attempt to estimate the very broad category of “IT” (which also includes data centers). But as it reaches 600 citations, the bad estimates continue to be referenced. It’s a great example of the idea that you can find academic papers to back up any point you want.
To illustrate, the historical estimates of past papers can be plotted against the actual values produced using retrospective modelling once the real data becomes available:
That’s not to say that the 196-400 TWh range is infallible. Indeed, a survey methodology by the European Union estimated 104 TWh of data center energy consumption just within EU countries in 2020 (Avgerinou, Bertoldi & Castellazzi L, 2017). That puts a global figure of 196 on the low end. The point is that finding a number from a scientific article to support your conclusions is not the end of the story.
Two of the leading researchers in this field recently published a good (and short) guide to how to avoid bad estimates of IT energy consumption:
- Check the methodology of the paper you are citing. This can be difficult without a detailed understanding of the area, but common red-flags include the lack of a detailed, transparent method with original data and calculations. Even simpler than that – how old is the data? Estimates must specify the year they apply to because important factors like overall energy efficiency and emissions factors can change quite quickly. Historical estimates say something about the past, not necessarily the future.
- Short term changes do not necessarily result in long term impacts. The increase in internet traffic due to working from home last year in the COVID lockdowns did not result in a proportional increase in energy consumption, as reported by several major telecoms companies. Energy proportionality is important to understand in the context of a specific timeframe. Systems can be designed with spare capacity for short term changes, but how do they adapt in the long run?
- Projections more than a few years out are probably highly inaccurate. As discussed above, there are too many factors that mean future IT energy estimates are too difficult to predict more than a couple of years into the future. Moore’s Law operated on a ~2 year timeline, but we’re now unsure how that might change.
- IT systems are complex and the system must be considered as a whole, or the system you are comparing must match. Traditional data centers are very inefficient but hyperscale cloud data centers are very efficient. IT usage might have increased, but where are those workloads placed? Substitution effects can be unintuitive.
Of course it’s easy to blame the media for using figures to fit a narrative, but the industry needs to do better as well. The common theme I’ve found throughout my research in this area is the lack of transparency. Estimates are inaccurate because of insufficient data. If the industry wants to avoid the bad PR of bad calculations, it needs to release more granular data.