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Rosalie Checca
I subscribe to Netflix for almost 10 years now. Of course, I have my favorite series and movies that I repeatedly watch, especially before bedtime. With this, I came to memorize many scenes, especially from Friends, Brooklyn 99, and Modern Family. I always wondered how many times I watched a show, but Netflix never gave me that answer. Until now.
A few days ago, I found out it was possible to download all my data from Netflix, which included my viewing history. As the name suggests, the viewing history has the logs for all the things you watch on Netflix, with time, duration of the session, content title, and more. So, it was just a matter of manipulating the data little, and I would finally have the answer to a question I have for a while.
My first approach was pretty straightforward, using all my knowledge from Excel, creating some formulas to filter and sum the hours. A few minutes later I got my answer, and to my surprise, I found out I had watched Friends almost 20 times!
From checking the data, I could see that I would be able to also break down the titles to recover in case of a TV show, what the show was, the season, and the episode. Here are a few examples of the titles of a few entries:
As you can see, although there is not much consistency between the naming of the seasons, with one regular expression, I could capture the series title, the season, and also the episode name. This might fail for some languages/shows, but it worked pretty well in my data as a heuristic. I had to add some translations because my data had entries both in English and in Brazilian Portuguese. The regexp looks like:
To make my life easier, I decided to convert all the durations from the format HH:MM:SS , to a single integer seconds. This allows me to sum all the durations without caring for the format. Later in the flow, I could just convert back from seconds to some more user-friendly value.
To use jinja you basically create a template, which in my case is a HTML file, and, on the places you want to include specific information, you call render, passing the data to be replaced/added. Something like:
Jinja accepts an array and is capable of doing the iteration on the fields. All I needed to do is create an object in the format expected (with profile, title, type, total_seconds, and total_time), and boom! The table was being built as expected.
I know many people have done much more in-depth and much more complicated analysis on the Netflix data and made that available. Still, considering my personal goal of writing some python lines and work with some different technologies, this project was a great success. I had a lot of fun with the data, thinking about what I wanted to display, and especially how.
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Even before millions were confined to their homes by a global pandemic, improvements in internet connections and service offerings had led to an exponential increase in the use of streaming video around the world. With few options left for entertainment, streaming services are taking off. In this commentary, we examine the carbon footprint of these services.
Streaming services are associated with energy use and carbon emissions from devices, network infrastructure and data centres. Yet, contrary to a slew of recent misleading media coverage, the climate impacts of streaming video remain relatively modest, particularly compared to other activities and sectors.
Drawing on our analysis and other credible sources, we expose the flawed assumptions in one widely reported estimate of the emissions from watching 30 minutes of Netflix. These exaggerate the actual climate impact by up 90 times.
The relatively low climate impact of streaming video today is thanks to rapid improvements in the energy efficiency of data centres, networks and devices. But slowing efficiency gains, rebound effects and new demands from emerging technologies, including artificial intelligence (AI) and blockchain, raise increasing concerns about the overall environmental impacts of the sector over the coming decades.
Update 11/12/2020: The energy intensity figures for data centres and data transmission networks were updated to reflect more recent data and research. As a result, the central IEA estimate for one hour of streaming video in 2019 is now 36gCO2, down from 82gCO2 in the original analysis published in February 2020. The updated charts and comparisons also include the corrected values published by The Shift Project in June 2020, as well as other recent estimates quoted by the media.
Looking at electricity consumption alone, the original Shift Project figures imply that one hour of Netflix consumes 6.1 kilowatt hours (kWh) of electricity. This is enough to drive a Tesla Model S more than 30km, power an LED lightbulb constantly for a month, or boil a kettle once a day for nearly three months. The corrected figures imply that one hour of Netflix consumes 0.8 kWh.
With 167 million Netflix subscribers watching an average of two hours per day, the corrected Shift Project figures imply that Netflix streaming consumes around 94 terawatt hours (TWh) per year, which is 200 times larger than figures reported by Netflix (0.45TWh in 2019).
The assumptions behind the Shift Project analysis (largely based on a 2015 paper, whose assumptions have been significantly revised in 2019 and 2020) contain a series of flaws, which, taken together, seriously exaggerate the electricity consumed by streaming video.
This difference stemmed from a stated assumption of 3Mbps apparently being converted in error to 3 megabytes per second, MBps, with each byte equivalent to eight bits. The Shift Project corrected this error in their June 2020 update, but did not revise any of their other assumptions, discussed below.
The Shift Project analysis overestimates the energy intensity of data centres and content delivery networks (CDNs) that serve streaming video to consumers by around 35-fold, relative to figures derived from 2019 Netflix electricity consumption data and subscriber usage data.
My original February 2020 analysis showed that the Shift Project assumptions for data transmission energy intensity (0.15-0.88 kWh/GB) were much higher than more recent estimates (0.025-0.23kWh/GB). However, the latest research shows that these data-based intensity values (kWh/GB) are not appropriate for estimating the network energy use of high bitrate applications such as streaming video. Instead, experts advise using time-based energy intensity values (kWh per viewing hour). Therefore, my assumptions for data transmission energy use have been updated with time-based energy intensity values.
Taken together, my updated analysis suggests that streaming a Netflix video in 2019 typically consumed around 0.077 kWh of electricity per hour, some 80-times less than the original estimate by the Shift Project (6.1 kWh) and 10-times less than the corrected estimated (0.78 kWh), as shown in the chart, below left. The results are highly sensitive to the choice of viewing device, type of network connection and resolution, as shown in the chart, below right.
The IEA estimate is also substantially lower than other estimates quoted in the media, including 22-times lower than the Despacito claim (cited on Channel 4, the BBC, Fortune, and Al Jazeera, assuming a global average grid mix) and 11-times lower than the claim by Save On Energy that 80 million views of Birdbox emitted 66ktCO2 (cited in the New Yorker, Euronews, Forbes, Die Welt, and the Daily Mail). My estimate of 36gCO2 per hour is over 2100-times lower than Marks et al. (2020) who estimated that 35 hours of HD video emits 2.68tCO2, or 77kgCO2 per hour.
But as the chart above shows, this figure depends heavily on the generation mix of the country in question. In France, where around 90% of electricity comes from low-carbon sources, the emissions would be around 2gCO2e, equivalent to 10 metres of driving.
Using country average emission factors may still overestimate emissions, particularly from data centres. Technology firms operating large data centres are leaders in corporate procurement of clean energy, accounting for about half of renewable power purchase agreements in recent years.
The electricity mix is also rapidly decarbonising in many parts of the world. For instance, the emissions intensity of electricity in the UK fell by nearly 60% between 2008 and 2018. Compared to 2019 levels, global emissions intensity of electricity falls by around a quarter by 2030 in the IEA Stated Policies Scenario and by half in the Sustainable Development Scenario.
As well as changes that are invisible to the consumer, there are also obvious trends in the technology seen everyday. Devices are also becoming smaller and more efficient, for example, in shifts from CRT to LCD screens, and from personal computers to tablets and smartphones.
Many new video streaming and cloud gaming services have also launched in recent months. Particularly noteworthy is the rapid growth in video traffic over mobile networks, which is growing at 55% per year. Phones and tablets already account for more than 70% of the billion hours of YouTube streamed every day.
The ease of accessing streaming media is leading to a large rebound effect, with overall streaming video consumption rising rapidly. But the complexity of direct and indirect effects of digital services, such as streaming video, e-books, and online shopping, make it immensely challenging to quantify the net environmental impacts, relative to alternative forms of consumption.
Moreover, emerging digital technologies, such as machine learning, blockchain, 5G, and virtual reality, are likely to further accelerate demand for data centre and network services. Researchers have started to study the potential energy and emissions impacts of these technologies, including blockchain and machine learning.
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