In 2024, Google claimed that their data centers are 1.5x extra power environment friendly than business common. In 2025, Microsoft committed billions to nuclear power for AI workloads. The information middle business tracks energy utilization effectiveness to a few decimal locations and optimizes water utilization depth with machine precision. We report direct emissions and power emissions with non secular fervor.
These are laudable advances, however these metrics account for less than 30 p.c of complete emissions from the IT sector. Nearly all of the emissions should not instantly from knowledge facilities or the power they use, however from the end-user gadgets that really entry the info facilities, emissions attributable to manufacturing the {hardware}, and software inefficiencies. We’re frantically optimizing lower than a 3rd of the IT sector’s environmental impression, whereas the majority of the issue goes unmeasured.
Incomplete regulatory frameworks are a part of the issue. In Europe, the Company Sustainability Reporting Directive (CSRD) now requires 11,700 firms to report emissions utilizing these incomplete frameworks. The following part of the directive, masking 40,000+ extra firms, was initially scheduled for 2026 (however is probably going delayed to 2028). Within the United States, the requirements physique liable for IT sustainability metrics (ISO/IEC JTC 1/SC 39) is conducting energetic revision of its requirements by 2026, with a key plenary assembly in Might 2026.
The time to behave is now. If we don’t repair the measurement frameworks, we danger locking in incomplete data collection and optimizing a fraction of what issues for the following 5 to 10 years, earlier than the following main requirements revision.
The restricted metrics
Stroll into any fashionable knowledge middle and also you’ll see sustainability instrumentation in every single place. Energy utilization effectivity (PUE) screens observe each watt. Water utilization effectivity (WUE) programs measure water consumption right down to the gallon. Subtle monitoring captures every part from server utilization to cooling effectivity to renewable energy percentages.
However right here’s what these measurements miss: Finish-user gadgets globally emit 1.5 to 2 instances extra carbon than all knowledge facilities mixed, in accordance with McKinsey’s 2022 report. The smartphones, laptops, and tablets we use to entry these ultra-efficient knowledge facilities are the larger drawback.
Information middle operations, as measured by energy utilization effectivity, account for less than 24 p.c of the entire emissions.
On the conservative finish of the vary from McKinsey’s report, gadgets emit 1.5 instances as a lot as knowledge facilities. That implies that knowledge facilities make up 40 p.c of complete IT emissions, whereas gadgets make up 60 p.c.
On high of that, roughly 75 percent of gadget emissions happen not throughout use, however throughout manufacturing—that is so-called embodied carbon. For knowledge facilities, solely 40 p.c is embodied carbon, and 60 percent comes from operations (as measured by PUE).
Placing this collectively, knowledge middle operations, as measured by PUE, account for less than 24 p.c of the entire emissions. Information middle embodied carbon is 16 p.c, gadget embodied carbon is 45 p.c, and gadget operation is 15 p.c.
Underneath the EU’s present CSRD framework, firms should report their emissions in three classes: direct emissions from owned sources, oblique emissions from bought power, and a 3rd class for every part else.
This “every part else” class does embrace gadget emissions and embodied carbon. Nonetheless, these emissions are reported as mixture totals damaged down by accounting class—Capital Items, Bought Items and Companies, Use of Bought Merchandise—however not by product kind. How a lot comes from end-user gadgets versus datacenter infrastructure, or worker laptops versus community tools, stays murky, and due to this fact, unoptimized.
Embodied carbon and {hardware} reuse
Manufacturing a single smartphone generates roughly 50 kg CO2 equal (CO2e). For a laptop computer, it’s 200 kg CO2e. With 1 billion smartphones changed yearly, that’s 50 million tonnes of CO2e per yr simply from smartphone manufacturing, earlier than anybody even turns them on. On common, smartphones are changed each 2 years, laptops each 3 to 4 years, and printers each 5 years. Information middle servers are changed roughly each 5 years.
Extending smartphone lifecycles to three years as a substitute of two would cut back annual manufacturing emissions by 33 p.c. At scale, this dwarfs knowledge middle optimization positive aspects.
There are packages geared in the direction of reusing outdated parts which might be nonetheless useful and integrating them into new servers. GreenSKUs and related initiatives present 8 p.c reductions in embodied carbon are achievable. However these stay pilot packages, not systematic approaches. And critically, they’re measured solely in knowledge middle context, not throughout the whole IT stack.
Think about applying the identical round economic system rules to gadgets. With over 2 billion laptops in existence globally and 2-3-year alternative cycles, even modest lifespan extensions create large emission reductions. Extending smartphone lifecycles to three years as a substitute of two would cut back annual manufacturing emissions by 33 p.c. At scale, this dwarfs knowledge middle optimization positive aspects.
But knowledge middle reuse will get measured, reported, and optimized. Machine reuse doesn’t, as a result of the frameworks don’t require it.
The invisible position of software program
Main load balancer infrastructure throughout IBM Cloud, I see how software architecture choices ripple by power consumption. Inefficient code doesn’t simply sluggish issues down—it drives up each knowledge middle energy consumption and gadget battery drain.
For instance, College of Waterloo researchers showed that they will scale back 30 p.c of power use in knowledge facilities by altering simply 30 strains of code. From my perspective, this outcome isn’t an anomaly—it’s typical. Dangerous software program structure forces pointless knowledge transfers, redundant computations, and extreme useful resource use. However in contrast to knowledge middle effectivity, there’s no generally accepted metric for software program effectivity.
This issues extra now than ever. With AI workloads driving large knowledge middle enlargement—projected to eat 6.7-12 p.c of complete U.S. electrical energy by 2028, according to Lawrence Berkeley Nationwide Laboratory—software program effectivity turns into vital.
What wants to vary
The answer isn’t to cease measuring knowledge middle effectivity. It’s to measure gadget sustainability with the identical rigor. Particularly, requirements our bodies (notably ISO/IEC JTC 1/SC 39 WG4: Holistic Sustainability Metrics) ought to lengthen frameworks to incorporate gadget lifecycle monitoring, software program effectivity metrics, and {hardware} reuse requirements.
To trace gadget lifecycles, we want standardized reporting of gadget embodied carbon, damaged out individually by gadget. One mixture quantity in an “every part else” class is inadequate. We’d like particular gadget classes with manufacturing emissions and alternative cycles seen.
To incorporate software program effectivity, I advocate creating a PUE-equivalent for software program, corresponding to power per transaction, per API name, or per consumer session. This must be a reportable metric below sustainability frameworks so firms can reveal software program optimization positive aspects.
To encourage {hardware} reuse, we have to systematize reuse metrics throughout the total IT stack—servers and gadgets. This contains monitoring restore charges, creating large-scale refurbishment packages, and monitoring element reuse with the identical element at present utilized to knowledge middle {hardware}.
To place all of it collectively, we want a unified IT emission-tracking dashboard. CSRD reporting ought to present gadget embodied carbon alongside knowledge middle operational emissions, making the total IT sustainability image seen at a look.
These aren’t radical modifications—they’re extensions of measurement rules already confirmed in knowledge middle context. Step one is acknowledging what we’re not measuring. The second is constructing the frameworks to measure it. And the third is demanding that firms report the whole image—knowledge facilities and gadgets, servers and smartphones, infrastructure and software program.
As a result of you may’t repair what you may’t see. And proper now, we’re not seeing 70 p.c of the issue.
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