Data Centers Need a Different LCA Playbook

As the digital infrastructure sector races to meet AI-driven demand, one question is becoming increasingly important: Are we measuring the environmental impact of data centers correctly?

The answer, in many cases, is no.

For years, Whole Life Cycle Assessments (WLCAs) have been developed around conventional commercial buildings, where embodied carbon is largely concentrated in the structure, façade, and interiors. Data centers are fundamentally different. They are infrastructure assets as much as they are buildings.

In a modern data center, a significant proportion of embodied carbon lies outside the traditional building envelope—in electrical distribution networks, UPS systems, backup generators, batteries, cooling infrastructure, telecommunications, fire protection systems, substations, utility corridors, and extensive site-wide infrastructure. Unlike conventional buildings, data center campuses extend beyond the building envelope, with site-wide infrastructure and construction activities representing a significant share of embodied carbon.

This creates a critical challenge for the industry. If system boundaries differ from one assessment to another, benchmarks become unreliable, hotspots remain hidden, and investment decisions are made on incomplete data.

Limitations of doing Datacentre WLCA currently?

• Limited availability of emissions data for MEP systems: Mechanical, electrical, and plumbing (MEP) equipment accounts for a significant share of a data center's embodied carbon, yet reliable product-specific emission factors remain scarce. As a result, practitioners often rely on proxy datasets and industry averages, introducing uncertainty into whole-building LCA results.

• Data collection is resource-intensive and difficult to scale: Gathering accurate material and equipment data across complex data center supply chains is a time-consuming process. While essential for improving assessment accuracy, this manual effort increases project costs and limits the scalability of whole-building LCAs across large portfolios.

• Manual assessments are slow and susceptible to human error: Traditional wbLCA methodologies rely heavily on manual data extraction, mapping, and calculations. This not only extends assessment timelines but also increases the risk of inconsistencies and human error, making it challenging to deliver repeatable, standardized assessments at the speed required by today's rapidly expanding data center industry.

  
  

The next generation of data center LCAs must move beyond buildings and assess the entire digital infrastructure ecosystem. Only then can developers, operators, investors, and supply chains benchmark consistently, identify genuine carbon reduction opportunities, and accelerate the transition to low-carbon digital infrastructure.

© 2026 MoruBld AI. All rights reserved.

MoruBld AI Pte. Ltd.

60 Paya Lebar Road
#06-28 Paya Lebar Square
Singapore 409051