Data Center Sustainability Metrics: Hidden Emissions

In 2024, Google claimed that their data centers are 1.5x more energy efficient than industry average. In 2025, Microsoft committed billions to nuclear power for AI workloads. The data center industry tracks power usage effectiveness to three decimal places and optimizes water usage intensity with machine precision. We report direct emissions and energy emissions with religious fervor.

These are laudable advances, but these metrics account for only 30 percent of total emissions from the IT sector. The majority of the emissions are not directly from data centers or the energy they use, but from the end-user devices that actually access the data centers, emissions due to manufacturing the hardware, and software inefficiencies. We are frantically optimizing less than a third of the IT sector’s environmental impact, while the bulk of the problem goes unmeasured.

Incomplete regulatory frameworks are part of the problem. In Europe, the Corporate Sustainability Reporting Directive (CSRD) now requires 11,700 companies to report emissions using these incomplete frameworks. The next phase of the directive, covering 40,000+ additional companies, was originally scheduled for 2026 (but is likely delayed to 2028). In the United States, the standards body responsible for IT sustainability metrics (ISO/IEC JTC 1/SC 39) is conducting active revision of its standards through 2026, with a key plenary meeting in May 2026.

The time to act is now. If we don’t fix the measurement frameworks, we risk locking in incomplete data collection and optimizing a fraction of what matters for the next 5 to 10 years, before the next major standards revision.

The limited metrics

Walk into any modern data center and you’ll see sustainability instrumentation everywhere. Power usage efficiency (PUE) monitors track every watt. Water usage efficiency (WUE) systems measure water consumption down to the gallon. Sophisticated monitoring captures everything from server utilization to cooling efficiency to renewable energy percentages.

But here’s what those measurements miss: End-user devices globally emit 1.5 to 2 times more carbon than all data centers combined, according to McKinsey’s 2022 report. The smartphones, laptops, and tablets we use to access those ultra-efficient data centers are the bigger problem.

Data center operations, as measured by power usage efficiency, account for only 24 percent of the total emissions.

On the conservative end of the range from McKinsey’s report, devices emit 1.5 times as much as data centers. That means that data centers make up 40 percent of total IT emissions, while devices make up 60 percent.

On top of that, approximately 75 percent of device emissions occur not during use, but during manufacturing—this is so-called embodied carbon. For data centers, only 40 percent is embodied carbon, and 60 percent comes from operations (as measured by PUE).

Putting this together, data center operations, as measured by PUE, account for only 24 percent of the total emissions. Data center embodied carbon is 16 percent, device embodied carbon is 45 percent, and device operation is 15 percent.

Under the EU’s current CSRD framework, companies must report their emissions in three categories: direct emissions from owned sources, indirect emissions from purchased energy, and a third category for everything else.

This “everything else” category does include device emissions and embodied carbon. However, those emissions are reported as aggregate totals broken down by accounting category—Capital Goods, Purchased Goods and Services, Use of Sold Products—but not by product type. How much comes from end-user devices versus datacenter infrastructure, or employee laptops versus network equipment, remains murky, and therefore, unoptimized.

Embodied carbon and hardware reuse

Manufacturing a single smartphone generates approximately 50 kg CO₂ equivalent (CO₂e). For a laptop, it’s 200 kg CO₂e. With 1 billion smartphones replaced annually, that’s 50 million tonnes of CO₂e per year just from smartphone manufacturing, before anyone even turns them on. On average, smartphones are replaced every 2 years, laptops every 3 to 4 years, and printers every 5 years. Data center servers are replaced approximately every 5 years.

Extending smartphone lifecycles to 3 years instead of 2 would reduce annual manufacturing emissions by 33 percent. At scale, this dwarfs data center optimization gains.

There are programs geared towards reusing old components that are still functional and integrating them into new servers. GreenSKUs and similar initiatives show 8 percent reductions in embodied carbon are achievable. But these remain pilot programs, not systematic approaches. And critically, they’re measured only in data center context, not across the entire IT stack.

Imagine applying the same circular economy principles to devices. With over 2 billion laptops in existence globally and 2-3-year replacement cycles, even modest lifespan extensions create massive emission reductions. Extending smartphone lifecycles to 3 years instead of 2 would reduce annual manufacturing emissions by 33 percent. At scale, this dwarfs data center optimization gains.

Yet data center reuse gets measured, reported, and optimized. Device reuse doesn’t, because the frameworks don’t require it.

The invisible role of software

Leading load balancer infrastructure across IBM Cloud, I see how software architecture decisions ripple through energy consumption. Inefficient code doesn’t just slow things down—it drives up both data center power consumption and device battery drain.

For example, University of Waterloo researchers showed that they can reduce 30 percent of energy use in data centers by changing just 30 lines of code. From my perspective, this result is not an anomaly—it’s typical. Bad software architecture forces unnecessary data transfers, redundant computations, and excessive resource use. But unlike data center efficiency, there’s no commonly accepted metric for software efficiency.

This matters more now than ever. With AI workloads driving massive data center expansion—projected to consume 6.7-12 percent of total U.S. electricity by 2028, according to Lawrence Berkeley National Laboratory—software efficiency becomes critical.

What needs to change

The solution isn’t to stop measuring data center efficiency. It’s to measure device sustainability with the same rigor. Specifically, standards bodies (particularly ISO/IEC JTC 1/SC 39 WG4: Holistic Sustainability Metrics) should extend frameworks to include device lifecycle tracking, software efficiency metrics, and hardware reuse standards.

To track device lifecycles, we need standardized reporting of device embodied carbon, broken out separately by device. One aggregate number in an “everything else” category is insufficient. We need specific device categories with manufacturing emissions and replacement cycles visible.

To include software efficiency, I advocate developing a PUE-equivalent for software, such as energy per transaction, per API call, or per user session. This needs to be a reportable metric under sustainability frameworks so companies can demonstrate software optimization gains.

To encourage hardware reuse, we need to systematize reuse metrics across the full IT stack—servers and devices. This includes tracking repair rates, developing large-scale refurbishment programs, and tracking component reuse with the same detail currently applied to data center hardware.

To put it all together, we need a unified IT emission-tracking dashboard. CSRD reporting should show device embodied carbon alongside data center operational emissions, making the full IT sustainability picture visible at a glance.

These aren’t radical changes—they’re extensions of measurement principles already proven in data center context. The first step is acknowledging what we’re not measuring. The second is building the frameworks to measure it. And the third is demanding that companies report the complete picture—data centers and devices, servers and smartphones, infrastructure and software.

Because you can’t fix what you can’t see. And right now, we’re not seeing 70 percent of the problem.