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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsThere is no universal answer to whether a digital service is better for the climate than the offline activity it might replace. Compare the same useful outcome, define a clear unit and lifecycle boundary, and check whether the offline activity actually changes. A service’s operating footprint alone does not include its full lifecycle or prove that it avoids emissions elsewhere.
Start with the outcome, not the technology
First define what a person gets from each option. Examples include one hour of entertainment, one meeting for a specified number of participants, or one completed shopping trip. Then compare ways of delivering that same outcome.
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A service-hour and a car trip are not equivalent units by themselves. If streaming is being compared with going to the cinema, define the comparison as, for example, one person watching a film at home versus one person travelling to and watching the same film at a cinema. State any important differences, such as whether the cinema trip is shared with other activities.
For a service, say what is included: the platform, type of content, number of participants, or other features that affect the result. Also say what is excluded. The Carbon Trust’s 2023 guidance on product carbon footprints recommends a specific functional unit and clear communication of what a service footprint covers.
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Set the boundary and context
A lifecycle boundary determines which emissions are counted. For a digital service, relevant stages can include device manufacture and end of life, device electricity use, network equipment and operation, and data centres. For the offline alternative, consider relevant travel, premises, and product delivery. Include equivalent stages where possible; if they cannot be included, make the exclusions visible rather than implying the totals cover the same things.
For example, the Carbon Trust’s conventional video-streaming approach focused on the use phase: electricity consumed by data centres, networks, and viewing devices. It excluded device manufacture and end of life. That can help describe streaming’s operational footprint, but it is not a complete lifecycle comparison with an alternative whose production or travel emissions are counted.
Record the geography and year as well as the boundary. Electricity’s emissions intensity varies by place and time, and technologies and energy use change. Electricity consumption in kWh is not itself a greenhouse-gas footprint: translating it into emissions depends on the electricity-supply assumptions.
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Read streaming estimates as dated examples, not universal answers
Published estimates can show the scale of a particular analysis, but their different units, locations, years, and methods mean they should not be treated as a direct head-to-head comparison.
| Estimate | What it describes | Source and context |
|---|---|---|
| About 0.077 kWh per hour | Electricity used for streaming video | IEA analysis published in 2020, based largely on 2019 data; an estimate affected by device, network, and resolution. |
| About 18 gCO2e per half-hour show | Emissions for a half-hour Netflix show using a global-average electricity mix | IEA, 2020; based on 2019 conditions. |
| About 55 gCO2e per hour | Average emissions for video-on-demand streaming in Europe | Carbon Trust estimate published in 2021 for 2020; the viewing device was typically the largest contributor in its analysis. |
The IEA’s electricity estimate is not an emissions figure; the two IEA values use different units and answer different questions. The Carbon Trust’s European estimate has a different geography and method, so it should not be used to claim that emissions rose or fell relative to the IEA figure.
The IEA’s 2020 analysis also warned that some widely repeated streaming claims relied on erroneous or outdated assumptions. Check the source year and method before reusing a number as a current benchmark.
Include hardware when the question is about the full lifecycle
Manufacturing can be a substantial part of a device’s lifecycle footprint. In its 2020 analysis, the IEA estimated that production accounted for around 80% of mobile-device lifecycle carbon emissions and about one-third of television lifecycle emissions. Those shares concern the devices’ lifecycle emissions, not the emissions of every individual streaming session.
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For a comparison, decide how to allocate the device’s production emissions across its useful life. A longer service life can spread those emissions over more use, but it does not make manufacture disappear. State the assumed lifetime and usage if the estimate depends on them.
Ask what activity the service actually displaces
A digital service does not automatically avoid the emissions of an offline alternative. The relevant question is what would otherwise have happened, and what changes as a result. A video call may replace travel, or it may simply add a meeting that would not have occurred. Remote work may reduce commuting while increasing energy use at home or changing other travel behavior.
The IEA has noted that household energy and behavior can reduce the net climate benefit of working from home. Avoided travel should therefore be treated as a possible consequence, not a guaranteed credit. George Kamiya, then an IEA Energy Policy Analyst, described the broader difficulty: “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.”
Keep a service’s own footprint separate from estimates of emissions it might avoid. Carbon Trust guidance recommends distinguishing the service transaction from enablement, downstream consequences, and avoided emissions. If you make a net-impact claim, state the counterfactual and assumptions behind it.
Check the factors most likely to change the result
Before drawing a conclusion, identify which assumptions could materially affect either option:
- Use and scale: duration, number of participants, viewing resolution, and how often the service is used.
- Devices: device type and screen size, electricity use, manufacturing footprint, and assumed hardware lifetime.
- Digital infrastructure: network connection, data-centre and network assumptions, and electricity carbon intensity.
- Offline alternative: travel mode and distance, premises energy, and any material product delivery.
- Substitution: whether the digital option replaces an offline activity, changes it, or is additional.
Streaming analyses from the IEA and Carbon Trust identify device, connection, resolution, and electricity assumptions as relevant variables. For travel or remote work, the actual trip or commute displaced—and any household energy or behavior changes—can be decisive.
Use the same comparison axes for each option
- Write the functional unit. State the outcome, quantity, and options being compared.
- Fix the context. Name the location and period; note the electricity-supply assumptions.
- Choose the lifecycle boundary. List the stages included for both options and call out exclusions.
- Record the main assumptions. Include devices, lifetime, usage, network, transport, premises, and delivery where relevant.
- Describe the counterfactual. Say what the person would otherwise do and whether that activity is actually avoided.
- Report uncertainty with the result. Identify the assumptions that matter most instead of presenting a single estimate as universally applicable.
If paired estimates do not use compatible units, boundaries, locations, and periods, say that the evidence does not establish which option has the lower footprint. That is more informative than subtracting unlike figures.
Handle emerging services with extra care
For AI video generation and other newer digital services, provider disclosures and methods are not consistent enough to assume that one provider’s figure is directly comparable with another’s. The Carbon Trust’s 2026 AI video research says early evidence indicates AI video generation can be significantly more energy-intensive than other forms of generative AI, with emissions scaling across the many generations that may be needed for professional-quality content. The report recommends more consistent lifecycle-assessment methods; treat its finding as an emerging signal, not a universal per-video footprint.
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