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Approaches to calculating website energy and carbon

It’s common to see carbon calculations for websites and other network services using data transfer as the unit of work. This is multiplied by an energy intensity figure to get the energy consumed.

For 2020 calculations, a commonly used energy intensity figure is 0.0065 kWh/GB taken from Aslan et al (or 0.06 kWh/GB if you don’t read the paper and fail to realize the 2015 figure in the abstract is projected to fall by half every 2 years). Assuming a website total download size of 1MB (0.001GB), this would be 0.001 * 0.0065 = 0.0000065 kWh.

Carbon emissions are then calculated using a carbon factor, usually one from the local government wherever the data center is located.

However, this method has a lot of assumptions:

  • Is all of that data downloaded on every page load? How much is cached by the browser?

  • Are there any CDNs involved? Are they moving data from a centralized origin and then caching it?

  • Is any processing happening on the server side? What about in the browser, such as executing JS or decoding video?

  • The intensity figure is an average – it doesn’t capture differences between high and low data volume applications such as web browsing vs video streaming.

  • What about on mobile devices? The figures from Aslan et al only refer to fixed-line networks in countries with modern, advanced infrastructure. They’re not valid on a global basis (another common error).

So you may end up with a figure, but is it useful for taking action in any way that actually makes a difference?

Allocational vs power model approach #

The model described above is commonly used to allocate fixed resources to a user, service, or unit of work (GB transferred in this case). It’s useful if you already know the total energy consumption and need to allocate it for reporting because the averages can be used to estimate the past.

However, that’s not how networks actually work. The infrastructure in use when you visit a website has already been deployed, the network equipment is already running, and the capacity has already been fixed based on expected peak load. The network is always-on and is always drawing power. Unlike servers, which over time have been improving their dynamic range – meaning their power consumption is more proportional to load – network energy consumption has a baseline and varies by only small amounts based on traffic volume.

There is a baseline level of power consumption and this varies by small amounts depending on the activity e.g. video streaming for an hour or two, but the overall energy consumption does not change that much. This is known as the power model approach, described in a 2020 conference paper by Malmodin and explained in detail in a 2021 Carbon Trust report on video streaming.

Graph of power and data over time
Power/data and power/time over 24hrs for fixed-line broadband. This hows how power consumption increases by only 0.2W during a 1 hour Netflix session. Source: Malmodin, J. (2020).

The result is that short-term network energy consumption is not directly proportional to data transferred. We can see this from network operator annual reports which show large increases in data traffic, but falling energy intensity figures.

Over longer time periods, increases in traffic do result in an increase in total energy consumption because network capacity is expanded. Networks need to cope with peak load and include redundancy. But that may be offset by newer, more efficient networking equipment, and the intensity figures fall because the increase in traffic outweighs the increase in total energy.

Both approaches have their uses. The allocational approach is much more well known and understood, but is probably only useful for backwards looking reporting. It can’t reflect changes in page size, or video quality settings, for example. The power model approach is much more reflective of how networks actually work, but it’s quite new and the research to-date only discusses specific networks with particular characteristics which are difficult to generalize.

Graph of energy use and network data flows
Annual energy use and network data flows for two large network providers, expressed as an index relative to 2016 = 1.0. Source: Koomey & Masanet, 2021.

How accurate are website carbon calculators? #

Website carbon calculators are not very accurate, especially if they only use data transfer as the metric. Average values can’t represent the variety of applications which means they can’t be used for comparisons.

Many calculators make basic errors, taking figures from old academic papers or reports based on papers which are outdated or have suspicious methodologies. Always check how the calculations are performed and the sources for the energy intensity figure – has it been correctly adjusted for time? Do they caveat the calculations with anything mentioned above?

The calculators from the cloud providers are more useful because they work off real data…I assume. They should, because Amazon, Google and Microsoft all know how much energy their infrastructure consumes so they can allocate it across their customers and services. This is useful for reporting, but their methodologies are less than transparent so it’s hard to be sure.

However, I’d bet they are much more accurate than anything else. Trying to calculate the carbon footprint of a website based on data transfer without knowing the actual energy consumption of the underlying resources is not going to produce useful results. Focusing on page performance using something like Google’s Lighthouse (which includes reducing download sizes) is a better use of time.