FROM COST EFFICIENCY TO CARBON EFFICIENCY: THE NEW METRIC DRIVING TECH DECISIONS

Ali Muzaffar, Assistant Editor at School of Mathematical and Computer Sciences, Heriot-Watt University Dubai

In boardrooms across the globe, something big is happening, quietly but decisively. Sustainability has evolved far beyond being a “nice-to-have” addition to an ESG report. It’s now front and centre in business strategy, especially in tech. From green computing and circular data centers to AI that optimizes energy use, companies are reshaping their technology roadmaps with sustainability as a core driver and not as an afterthought.

Not long ago, tech strategy was all about speed, uptime, and keeping costs per computation low. That mindset has evolved. Today, leaders are also asking tougher questions: How carbon-intensive is this system? How energy-efficient is it over time? What’s its full lifecycle impact? With climate pressure mounting and energy prices climbing, organisations that tie digital transformation to their institutional sustainability goals.

At its heart, green computing seeks to maximise computing performance while minimising environmental impact. This includes optimising hardware efficiency, reducing waste, and using smarter algorithms that require less energy.

A wave of recent research shows just how impactful this can be. Studies indicate that emerging green computing technologies can reduce energy consumption by 40–60% compared to traditional approaches. That’s not a marginal improvement, that’s transformational. It means smaller operating costs, longer hardware life, and a lower carbon footprint without sacrificing performance.

Part of this comes from smarter software. Techniques like green coding, which optimise algorithms to minimise redundant operations, have been shown to cut energy use by up to 20% in data processing tasks.

Organisations that adopt green computing strategies aren’t just doing good; they’re driving tangible results. Informed by sustainability principles, energy-efficient hardware and

optimisation frameworks can reduce energy bills and maintenance costs at the same time, often with payback periods of three to five years.

Data centres are the backbone of the digital economy. They power software, store vast troves of data, and support the artificial intelligence systems driving innovation. But this backbone comes with a heavy environmental load. Collectively, global data centres consume hundreds of terawatt-hours of electricity each year, which is about 2% of total global electricity.

As AI workloads surge and data storage demand explodes, energy consumption is rising sharply. Looking ahead to 2030, the numbers are hard to ignore. Global data

centre electricity demand is expected to almost double, reaching levels you’d normally associate with an entire industrialised country. That kind of energy appetite isn’t just a technical issue, it’s a strategic wake-up call for the entire industry.

This surge has forced a fundamental rethink of how data centres are built and run. Enter the idea of the circular data centre. It’s not just about better cooling or switching to renewables. Instead, it looks at the full lifecycle of infrastructure, from construction and daily operations to decommissioning, recycling, and reuse, so waste and inefficiency are designed out from the start.

The most forward-thinking operators are already implementing this approach. Advanced cooling methods, such as liquid cooling and AI-driven thermal management, are revolutionising the industry, reducing cooling energy consumption by up to 40% compared to traditional air-based systems. That’s a big win not only for energy bills, but also for long- term sustainability.

Beyond cooling, operators are turning heat waste into a resource. In Scandinavia, data centres are already repurposing excess thermal output to heat residential buildings, a real- world example of how technology can feed back into the community in a circular way. These strategies are already showing results, with approximately 60% of data centre energy now coming from renewable sources, and many operators are targeting 100% clean power by 2030.

Circular thinking extends to hardware too. Companies are designing servers and components for easier recycling, refurbishing retired equipment, and integrating modularity so that parts can be upgraded without replacing entire systems.

For businesses, circular data centres represent more than environmental responsibility. They can significantly lower capital expenditures over time and reduce regulatory risk as governments tighten emissions requirements. While AI itself has been criticised for energy use, the technology also offers some of the most effective tools for reducing overall consumption across tech infrastructure.

AI algorithms excel at predictive optimisation, they can analyse real-time sensor data to adjust cooling systems, balance computing loads, and shut down idle resources. Across case studies, such systems have reliably achieved 15–30% energy savings in energy management tasks in cloud environments, dynamic server allocation and AI-assisted workload management have contributed to energy savings of around 25% when compared with conventional operations.