# Use or Produce - Carbon Impact of a Video Streaming Device

Pierre Le Gargasson*, Olivier Weppe*, Thibaut Marty*, Maxime Pelcat*, Daniel Ménard*, Christian Herglotz†

*Univ. Rennes, INSA Rennes, CNRS, IETR – UMR 6164, Rennes, France

†Computer Engineering, Brandenburgische-Technisch Universität Cottbus-Senftenberg, Cottbus, Germany

pierre.le-gargasson@insa-rennes.fr olivier.weppe@insa-rennes.fr thibaut.marty@insa-rennes.fr

maxime.pelcat@insa-rennes.fr daniel.menard@insa-rennes.fr christian.herglotz@b-tu.de

**Abstract**—This paper presents a detailed carbon-centered Life Cycle Analysis (LCA) of a video streaming device designed for long-term operation. We chose a development board with a screen to have full control on its operation, for which we have detailed information on its components allowing us to accurately model emissions from the production to the use of the device. Concerning its use, we perform a dedicated measurement series to determine the actual power consumption in different video playback scenarios and develop a linear power estimation model, which we use to evaluate different usage scenarios. The resulting LCA indicates that potential savings are highest when exploiting low-power sleep modes or switching off the device during its lifetime. When operating the device in a country with low carbon intensity, production and usage show similar emissions, while in countries with medium to high carbon emissions, the usage of the device causes significantly higher emissions.

**Index Terms**—video streaming, codec, LCA, energy, sustainability

## I. INTRODUCTION

Video technology has become ubiquitous in the daily lives of human beings worldwide. Video systems are used in various scenarios in private (social networks, video streaming) as well as in professional life (teleconferencing, education). As a consequence, it is estimated that around 1% of global greenhouse gas (GHG) emissions are caused by online video technology [1], indicating that research targeting a sustainable use of video consumption is essential to limit our impact on global warming.

In the last decades, substantial researches have been performed with respect to the two main components of video technology causing emissions and resource consumption: the production and the use of video hardware. Concerning the use, a multitude of aspects was considered such as the power consumption of end-user devices [2], [3], the power consumption during decoding [4]–[6] and encoding [7], [8], the display [9], the exploitation of standardized metadata [10], [11], and the overall energy consumption of online video systems [12]. Although being highly valuable in understanding the energy consumption during the use of hardware, these studies all neglected the impact of the production of the related devices.

Concerning the production, detailed LCAs have been performed for different devices capable of video delivery such as

smartphones [13], televisions [14], and laptops [15]. While the production process is investigated in detail, the actual use of the devices was modeled with highly simplified assumptions on the power consumption of the hardware.

In this paper, our goal is to combine these two complementary views to obtain an understanding on the joint carbon impact of production and use of video technology. Therefore, we focus on an end-user device and combine a detailed LCA on the production with an in-depth investigation of energy consumption usage. For the latter, a sophisticated power measurement setup has been created. It allows to study different usage factors in detail, e.g., the impact of video parameters such as the resolution and the bitrate. Results indicate that for the target device, a simple Printed Circuit Board (PCB) with a microprocessor and a display, the use phase causes the highest share of emissions for countries with medium to high electricity production carbon intensities. In this phase, configuring the device adequately during its lifetime can save significant resources.

This paper is organized as follows. First, Section II provides an overview on current LCA trends and research targeting an energy-efficient use of video technology. Afterwards, Section III explains the LCA of the target device for its production and Section IV introduces the power measurement setup and the subsequent modeling approach to get detailed information on the power consumption during the use of the device. Afterwards, Section V introduces several usage scenarios and analyzes the sensitivity of greenhouse gas emissions with respect to the hardware components, the usage scenarios, and the country. Finally, Section VI concludes this paper.

## II. STATE-OF-THE-ART

LCA is an ISO standardised method for evaluating environmental costs of any product [16]. LCA is organized in four phases: 1) goal, functional unit and scope definition, 2) inventory, 3) impact assessment and 4) interpretation. Functional unit refers to a unit of service offered by the product, such as “the possibility to watch a high-definition streaming video, one hour per day, for 10 years, in a mobility context”. Inventory consists of listing all the activities having conducted to provide the functional unit to the user. LCA of electronics devices is still a young discipline, and requires a strong knowledge, as the supply chain of PCBs and Integrated Circuits (ICs) is

<footnote>This study has been partially funded by the France2030 ESOS Project (Electronics: Sustainable, Open and Sovereign), operated by French ANR (Agence Nationale de la Recherche).</footnote>
long and versatile, and as the number of employed materials is
very large, leading to imprecise LCAs [17]. However, results
of LCAs on smartphones, tablets and personal computers
concur in showing that manufacturing costs, and in particular
ICs manufacturing costs dominate carbon impacts [18]. The
scientific community of LCA has converged to a set of 16
environmental impacts, denoted as *midpoint impacts*, to be
analyzed in the LCA impact assessment and interpretation
phases [19]. This set of midpoint impacts contains the Global
Warming Power (GWP) index, measured in kg of CO₂ equiv-
alent (kg CO₂e). GWP covers all global warming effects of
Greenhouse Gases (GHG), and converts the effects of other
gases to a mass of CO₂ having the same global warming
potential over a period of 100 years.

Although studies showed that carbon impact in LCA is
dominated by manufacturing of devices, a multitude of studies
investigated the energy consumption during the use of video
streaming hardware. Examples are the in-depth assessment of
a smartphone's energy consumption [2], [3] or a laptop's and
PC's energy consumption during video streaming tasks [20].
Further studies investigated subcomponents of video streaming
systems such as the display [9], [10], the hardware and the
software video decoder [4], [21], or the video encoder [7], [8].
Finally, many studies aimed to decrease the energy consump-
tion of video systems to improve sustainability. Examples are
the efficient construction of bitrate ladders for adaptive stream-
ing purposes [22], the use of dynamic voltage and frequency
scaling [23], the encoding of videos for efficient decoding
[5], [24], or the development of energy efficient hardware
implementations for the encoder and the decoder [25].

In this study, the energy-efficiency assessment approach
is extended to focus on the usage phase of video systems
towards the manufacturing of the corresponding devices, as is
performed in LCA. Still, this study diverges from classic LCA
by concentrating on the unique metric of GWP. While LCA
studies attributing costs to smartphones and tablets have been
published [26], this paper is, to the extent of our knowledge,
the first to attribute the end-user device's carbon impact of
video streaming, including precise evaluations of both the
manufacturing and use phases.

### III. PRODUCTION PARAMETERS

The proposed study is based on the STM32MP157F-DK2
kit. This specific device was selected due to its widespread use
in various embedded systems applications, which makes it a
representative model for evaluating environmental impacts in
similar contexts. Moreover, the STM32MP157F-DK2 includes
a Linux capable dual-core Arm Cortex-A processor, making
it well fit for multimedia and power-efficient devices, such as
a reprogrammable video capable devices. Finally, the device
hardware is thoroughly documented, including access to the
layout and component list.

The production phase encompasses emissions from cradle-
to-gate. This phase, combined with the usage analysis dis-
cussed in Section IV, covers the cradle-to-use scope of the
product's life cycle, i.e. from the extraction of resources to
the end of product fabrication. However, we have opted not

to include the end-of-life and transport phases in our analysis.
This decision is based on the uncertainty surrounding disposal
and transportation methods, which can vary widely depending
on geographic location, user behavior, and local infrastructure.
Additionally, the impact of the device case is not included in
this study, as it can vary significantly between scenarios, and
we are only considering components directly necessary for
video streaming.

#### A. Printed Circuit Board Assembly (PCBA)

To inventory the Printed Circuit Board Assembly
(PCBA), the Bill of Materials (BOM) associated with
the STM32MP157F-DK2, which is publicly available, has
been used. Each individual component listed in the BOM
was mapped to a corresponding input in the Ecoinvent 3.9.1
Environmental Footprint v3.1 (EF v3.1) database [27],
providing accurate representation of its environmental impact.

#### B. Display Production

For the display production, the data available in the Ecoin-
vent database are outdated, as it pertained to monitors from
the late 1990s. In order to provide a more accurate repre-
sentation, we followed the methodology used in the Fair-
phone 4 LCA [13], which involves extracting more up-to-date
data from the AUO Corporation sustainability report [28]. In
their 2022 report, AUO reported total Scope 1 and Scope 2
emissions amounting to 242.79 × 10⁷ kg CO₂e for their screen
production activity. Scope 3 emissions were excluded because
these involve the use phase of the product, which would
result in double-counting within our study. AUO also reported
the purchase of 55,106,302 m² of glass substrate during the
same year. Assuming no material was discarded during pro-
duction, we computed that the production carbon intensity is
44.06 kg CO₂e/m², which is used in our analysis of display
manufacturing impacts.

### IV. USAGE PARAMETERS

For the modeling of the resource consumption during the
use phase of the device, we focus on the total energy con-
sumption throughout product lifetime. For this, the modeling
approach presented in [5] has been used. The power consump-
tion of a smartphone during video streaming was assessed
in detail. The model relied on various parameters of the
video (e.g., bitrate, resolution, codec) and device as well as
playback settings (e.g., display brightness, online streaming).
To adapt the model to the use case in this paper, new power
measurement setup using the target device-under-test (DUT),
the STM32MP157F-DK2, has been developed. The power
supply has been evaluated with an *Odroid SmartPower 3* power
meter.

To assess the power consumption during playback of videos,
9 low-resolution sequences from the JVET common test con-
ditions [29] have been tested. FFmpeg [30] with target codecs
MPEG-2 and H.264 [31] has been used for the encoding.
Note that more recent codecs could not be executed due to
the restricted computing power of the device. The bit streams
were packed into .mpg and mp4-containers, respectively, and stored on the DUT. For MPEG-2 videos, we chose quantization factors (q scales) of 2, 3, 5, and 8; for H.264 we used the constant rate factors (crfs) 18, 23, 28, and 33. Due to the resulting high bitrates, from the maximum total of 72 videos, only 46 videos could be displayed without time stalls.

The necessary precautions have been taken to ensure a reliable measurement by repeating the measurements several times and averaging the results to meet a statistical confidence interval test as described in [21]. The test ensures that with a probability of 99%, the true mean energy consumption does not deviate more than 1% from the measured mean energy consumption. In the same way, adequate processing of the start and the end of the video was conducted to limit the effect of initialisation and ending processes. Furthermore, power measurements in idle mode (no user tasks waiting for processing) and stand-by mode of the DUT were performed. Based on the dataset and the tested conditions, a model has been developed to describe the video playback power consumption as follows

$$ \begin{aligned} \hat{P} &= P_0 + P_{\text{res}} + P_{\text{fps}} + P_{\text{kbps}} + P_{\text{codec}} \quad (1) \\ &= P_0 + p_{\text{res}} \cdot R + p_{\text{fps}} \cdot f + p_{\text{kbps}} \cdot b + p_{\text{codec}} \cdot F_{\text{H.264}} \end{aligned} $$

In this, the estimated mean power consumption $\hat{P}$ in Watts is calculated using a constant offset power $P_0$ and the powers $p$ related to the resolution (res), theipter (fps), the bitrate (kbps), and the codec. For the latter ones, specific power coefficients are multiplied with the actual values of the resolution $R$, theipter $f$, the bitrate $b$, and a binary indicator of the codec $F_{\text{H.264}}$ which is ‘one’ if the codec is H.264 and ‘zero’ if it is MPEG-2.

Training this equation with the measured power values using a least-squares curve fitting approach results in a cross-validated mean absolute percentage error (MAPE) of

$$ \varepsilon = \frac{1}{N} \sum_{i=1}^{N} \frac{|\hat{P}_{i} - P_{i}|}{P_{i}} = 2.81\%, \quad (2) $$

where $i$ is the index of the measurement corresponding to one of the $N = 46$ encoded videos, $\hat{P}_i$ is the estimated power consumption of the $i$-th video from Eq. (1), and $P_i$ the measured power consumption of the $i$-th video. Cross-validation was performed with $k = 10$ randomly selected folds.

The resulting coefficients in the model, which describe the dependency of the total power consumption on video properties, are listed in Table I. The coefficient $p_{\text{kbps}}$ tends to zero with a negative value smaller than 1% of the total energy, which is below the accuracy of the measurement. In addition, the maximum value of the power components when the corresponding parameters reach the highest values is reported in the second row. The values indicate that the baseline power consumption in idle mode $p_0$ represents the largest portion of all components. From the other components, theipter shows the highest additional impact with an additional power consumption of maximum 0.12 W. The maximum power consumption in our scenarios, which was also observed in the measurements, reaches $P_{\text{max}} \approx 1.83$ W,

<caption>TABLE I</caption>

POWER COEFFICIENT VALUES FROM THE TRAINED POWER MODEL IN Eq. (1). THE SECOND ROW PROVIDES THE MAXIMUM CONTRIBUTION OF EACH POWER COMPONENT AT THE MAXIMUM VALUE OF THE CORRESPONDING INPUT PARAMETER.

<table><thead><tr><th></th><th>P<sub>0</sub></th><th>P<sub>res</sub></th><th>P<sub>fps</sub></th><th>P<sub>kbps</sub></th><th>P<sub>H.264</sub></th></tr></thead><tbody><tr><th>Value</th><td>1.58</td><td>9.90e<sup>-8</sup></td><td>1.93e<sup>-3</sup></td><td>-6.47e<sup>-7</sup></td><td>2.65e<sup>-2</sup></td></tr><tr><th></th><th>P<sub>0</sub></th><th>P<sub>res</sub></th><th>P<sub>fps</sub></th><th>P<sub>kbps</sub></th><th>P<sub>H.264</sub></th></tr><tr><th>Max</th><td>1.58 W</td><td>0.09 W</td><td>0.12 W</td><td>-0.01 W</td><td>0.03 W</td></tr></tbody></table>

such that roughly $\frac{1.83-1.58}{1.83} \approx 14\%$ of the maximum power consumption actually depend on the video streaming settings.

## V. SENSITIVITY ANALYSIS

### A. Playback Scenarios

In the sensitivity analysis, the full LCA for the production plus the usage of the DUT targeting the greenhouse gas emissions in the unit gCO₂e is computed. To allow a better understanding of the impact during the use of the device, a set of usage scenarios of the DUT in a certain timeframe of A years with different video playback parameters has been defined. The scenarios incorporate different video resolutions, frame rates, bitrates, codecs, and playback duration in minutes $t_{\text{daily}}$ per day. In practice, different playback duration could occur because the device either plays videos throughout the whole day (constant monitoring) or only on request. The energy consumption in kWh throughout the entire lifetime is then calculated by:

$$ E_{\text{use}} = \frac{365.25 \cdot A \cdot \left( \hat{P} \cdot t_{\text{daily}} + P_{\text{idle}} \cdot (60 \cdot 24 - t_{\text{daily}}) \right)}{60 \cdot 1000}, \quad (3) $$

where $\hat{P}$ is the estimated power consumption derived by Eq. (1) and $P_{\text{idle}}$ either the offset power, the standby power, or zero if we assume that the device is switched off when not used.

Multiple usage scenarios has been defined for the sensitivity analysis. First, the baseline scenario corresponding to the highest resolution, frame rate, and bitrate with the more recent codec H.264 is defined. Furthermore, playback duration of $t_{\text{daily}} = 5$ min per day and $A = 20$ years of operation is assumed. In addition, a lowPower scenario where the video parameters are set to smaller values is defined. Finally, the impacts of changing the daily playback duration, the operating time, and the idle power consumption are tested. All scenarios, including total energy consumption $E_{\text{use}}$, are summarized in Table II.

### B. Analysis

1) Production impact: The production impact has been assessed using an inventory derived from the BOM of the device and computing the LCA impacts using Ecoinvent [27]. In addition, the display production impact is computed using the impact defined in Section III for a surface of 47 cm². The total emissions for the production of the device yield 24.3 kg CO₂e.
<table><caption>TABLE II
USAGE SCENARIOS OF THE VIDEO PLAYBACK DEVICE AND TOTAL ENERGY CONSUMPTION $E_{\text{use}}$.</caption><thead><tr><th>Scenario</th><th>Resolution</th><th>Frame rate</th><th>Bitrate</th><th>Codec</th><th>P<sub>idle</sub></th><th>t<sub>daily</sub></th><th>A</th><th>E<sub>use</sub></th></tr></thead><tbody><tr><td>baseline</td><td>1280 × 720</td><td>60 fps</td><td>10 Mbps</td><td>H.264</td><td>P<sub>0</sub></td><td>5 min</td><td>20 years</td><td>267.67 kWh</td></tr><tr><td>lowPower</td><td>416 × 240</td><td>30 fps</td><td>1 Mbps</td><td>MPEG-2</td><td>P<sub>0</sub></td><td>5 min</td><td>20 years</td><td>267.58 kWh</td></tr><tr><td>fullDay</td><td>1280 × 720</td><td>60 fps</td><td>10 Mbps</td><td>H.264</td><td>P<sub>0</sub></td><td>1440 min</td><td>20 years</td><td>316.50 kWh</td></tr><tr><td>fullDaylowPower</td><td>416 × 240</td><td>30 fps</td><td>1 Mbps</td><td>MPEG-2</td><td>P<sub>0</sub></td><td>1440 min</td><td>20 years</td><td>288.62 kWh</td></tr><tr><td>10years</td><td>1280 × 720</td><td>60 fps</td><td>10 Mbps</td><td>H.264</td><td>P<sub>0</sub></td><td>5 min</td><td>10 years</td><td>133.84 kWh</td></tr><tr><td>30years</td><td>1280 × 720</td><td>60 fps</td><td>10 Mbps</td><td>H.264</td><td>P<sub>0</sub></td><td>5 min</td><td>30 years</td><td>401.51 kWh</td></tr><tr><td>switchOff</td><td>1280 × 720</td><td>60 fps</td><td>10 Mbps</td><td>H.264</td><td>0</td><td>5 min</td><td>20 years</td><td>1.10 kWh</td></tr><tr><td>standby</td><td>1280 × 720</td><td>60 fps</td><td>10 Mbps</td><td>H.264</td><td>0.4 W</td><td>5 min</td><td>20 years</td><td>70.98 kWh</td></tr></tbody></table>

<table><caption>TABLE III
CARBON IMPACT OF USAGE FOR ALL SCENARIOS IN THREE COUNTRIES. ALL IMPACTS ARE EXPRESSED IN kg CO₂e.</caption><thead><tr><th>Scenario</th><th>Usage impact in FR</th><th>Usage impact in GE</th><th>Usage impact in PL</th></tr></thead><tbody><tr><td>baseline</td><td>23.9</td><td>51.0</td><td>264.2</td></tr><tr><td>lowPower</td><td>23.9</td><td>51.0</td><td>264.1</td></tr><tr><td>fullDay</td><td>28.3</td><td>60.3</td><td>312.3</td></tr><tr><td>fullDaylowPower</td><td>25.8</td><td>55.0</td><td>284.8</td></tr><tr><td>10years</td><td>12.0</td><td>25.5</td><td>132.1</td></tr><tr><td>30years</td><td>35.9</td><td>76.5</td><td>396.2</td></tr><tr><td>switchOff</td><td>0.1</td><td>0.2</td><td>1.1</td></tr><tr><td>standby</td><td>6.3</td><td>13.5</td><td>70.0</td></tr></tbody></table>

<figure><img src="image_1.png" /><figcaption>Fig. 1. Carbon impact of production for ICs, peripheral components, PCB, passive components and display. For Global location.</figcaption></figure>

A distribution of the emissions per component category is illustrated in Fig. 1. The highest impact is due to the integrated circuits (ICs) with a share of almost 84% of the total production emissions. The other components (peripheral components, printed circuit board (PCB), passive components, and the display) show a minor impact.

2) Usage impacts: The usage impacts are computed from the total energy consumption defined in Table II and converted to kg CO₂e using Ecoinvent as well. The impacts are presented in Table III for three countries. The countries are chosen to show the carbon impact using various power grid carbon intensities: low-carbon electricity (France), moderate carbon intensity (Germany) and high carbon intensity (Poland).

Firstly, the main factor influencing the usage impacts is the power grid carbon intensity of the country where the device is used. For each scenario, using the device in Germany or Poland has respectively 2.13 and 11.04 more impact than in France.

Secondly, in a country with a low carbon intensity, the usage emissions are similar to the production emissions (23.9 kg CO₂e vs. 24.3 kg CO₂e, respectively) for the baseline scenario. Third, significant emissions can be reduced when exploiting low-power standby modes of the DUT. If the device is completely switched off, more than 99.5% of emissions

caused by the usage can be saved. Making use of effective standby modes, reductions still yield more than 73%. Hence, due to the long operation time of the device, using ultra-low-power states while idling is the most effective way to reduce the overall usage emissions.

Finally, for this device, the impact of video parameters is rather small, especially in the case that the device is idling most of the day. If the device is running all day, switching from high quality videos (fullDay) to low quality videos (fullDayLowPower) reduces the emissions by 8.8%.

3) Hardware acceleration: The figures presented in this study focus exclusively on software decoding, as the selected SoC lacks a hardware decoder. However, the power consumption results could differ in the case of SoCs equipped with hardware decoding capabilities. As noted in [4], hardware decoding offers a substantial advantage over software decoding, with a power consumption for decoding HEVC content which is at least four times lower when hardware decoding is utilized.

This improvement in power efficiency comes with a trade-off in environmental impact during fabrication due to the additional silicon required for implementing the hardware decoding functions. Using an open-source decoder core as a reference [32], with a reported silicon area of 14.44 mm² in 180 nm technology, the corresponding silicon area in 28 nm technology can be estimated. After scaling to account for the higher transistor density [33], the area is reduced to approximately 0.53 mm². According to the IMEC Netzero model [34], this corresponds to an additional estimated carbon footprint of 4 g CO₂e, which is negligible with respect to all the other components.

## VI. CONCLUSION

In this work, we performed a detailed LCA on a video displaying device over its lifetime, splitting up contributions from the production phase and the usage phase, with a focus on global warming potential. We find that in countries with low carbon intensities, the usage and the production show similar effects, while in countries with high carbon intensities, the usage phase dominates significantly. Furthermore, the use of power saving modes (e.g., sleep or switch-off of a device) have a substantial effect and can reduce overall usage emissions by more than 99%.

In future work, we will verify these results for other devices with different screen sizes (TV sets, smartphones, laptops), different ambient temperatures and extend our considerations towards server production and use. Eventually, our goal is to perform a detailed and exhaustive LCA of an entire online video system.
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