Information and Methodology
This carbon calculator for linear TV distribution was developed as
part of my master's thesis "The Carbon Footprint of Linear Television
compared to Video Streaming" (original title: "Die Treibhausgasemissionen
von linearem Fernsehen im Vergleich zu Videostreaming").
My thesis is currently only available in German, an English translation is planned.
The abstract on the right provides a short summary of its contents.
This page provides some basic information on the methodology used for
the development of this tool. The complete set of methods including
all the assumptions, formulas, and parameters used in my model can be
found in my thesis. I will provide copies upon request.
How it works
My carbon calculator tool lets you calculate energy consumption
and greenhouse gas emissions of a specific TV viewing scenario,
e. g. that of your personal setup at home. Please choose the type of
your transmission and then answer all of the following questions. The options
available for selection may differ depending on your previous choices.
Some topics allow for manual input of values via the "..." button.
Please bear in mind, that standby energy consumption is not calculated
for manually entered values. Input fields will show average or typical
values by default. Please refer to the tooltips for further information
on specific parameters. Year of reference: 2022.
DTT transmission energy is calculated as total energy consumption of
transmission infrastructure (as provided by Media Broadcast,
the largest provider of such services in Germany) divided by total
annual device viewing hours for DTT (calculation of which based on
Carnstone (2022)).
Cable transmission energy values have been calculated based on
cable network provider reporting and research publications. Overlap of
cable TV and cable internet users as well as allocation of bandwidth usage
has been accounted for.
Satellite transmission energy is calculated from assumptions
on annual uplink provider energy consumption (here: SES Astra) for the
transmission of German TV channels divided by total annual device
viewing hours for satellite TV (calculation of which based on
Carnstone (2022)).
IPTV energy consumption is based on Malmodin's "power model"
(Malmodin, 2020)
and its implementation in
Carbon Trust (2021).
Some parameters have been adjusted to account for the situation in Germany.
For the calculation of TV set energy consumption a dataset of 60 different
TV sets has been created. Using information from their technical data sheets
the model calculates energy consumption as a function of screen size
(with different functions for each combination of SDR/HDR and LED/QLED/OLED).
The results table breaks down energy consumption and greenhouse gas
emissions into four categories (tranmission, CPE, peripherals, viewing device).
The results are also displayed in a stacked bar chart below.
Abstract
The increasing popularity of video streaming led to an intense debate about the associated greenhouse gas emissions in recent years. This thesis compares distribution and viewing of linear broadcasting and video streaming in terms of energy consumption and greenhouse gas emissions in Germany. Based on own modeling and previous studies, a product carbon footprint for the functional unit of one device-hour of TV viewing or video streaming is calculated. The system boundary includes transmission, any CPE and peripherals, and viewing devices for terrestrial, cable, and satellite television as well as IPTV. Depending on the platform, energy consumption is found to be between 0.15 and 10 Wh/h for transmission, and also in a singledigit range for CPE and peripheral devices. With an average of around 100 Wh/h, TV sets have the largest share of total energy consumption in every case examined. The entire value chain emits 40 to 45 g of CO2e per device-hour, depending on the mode of transmission. OTT energy consumption and emissions depend heavily on the viewing device. When streaming on a TV, OTT hardly differs from linear broadcasting, whereas on mobile devices emissions are typically below 5 g per hour. Based on the PCF, a carbon calculator capable of interactively displaying different scenarios has been developed. Optimization potentials derived from its results can primarily be found in CPE, TVs, and changes in platform penetration and are being discussed in this thesis in the light of changing user behaviour.