Over recent years, rapid advancements in AI have transformed data center discourse from an industry conversation around capacity, uptime, and latency to an intense public debate about power constraints on the grid, carbon emissions, water usage, and public consent.

This increased level of scrutiny is critical; with global data center electricity consumption projected to double by 2030, our current infrastructure simply isn’t built for this level of demand. Grid connections are already delayed in many markets, and developers are increasingly looking at behind-the-meter power and on-site generation as ways to bring projects forward.

But bringing power to site faster will only solve part of the problem. Another key part of the debate must be how responsibly electricity and water are used by data centers. The regulatory landscape is beginning to reflect this, with operators facing growing expectations to report more clearly on facility energy use, water consumption and environmental impact.

AI infrastructure is now being treated by many governments as strategically important, given its role in economic growth, research, public services and, potentially, decarbonization in other sectors. But strategic importance brings higher scrutiny, and if data centers are to compete for scarce grid capacity, land, water and planning consent, the industry needs to ensure and evidence that facilities are being designed and operated as efficiently as possible.

Power Usage Effectiveness (PUE) remains the industry’s most familiar efficiency metric, measuring total facility energy use against the energy used by IT equipment. While this is useful, it is insufficient on its own. A facility can report a strong PUE while still drawing substantial power at peak times, relying on high-carbon energy sources or placing pressure on local water supplies.

Carbon Usage Effectiveness (CUE), Water Usage Effectiveness (WUE), peak demand, cooling efficiency, back-up power strategy, demand-flexibility potential and the carbon intensity of power consumed all need to be considered alongside PUE. These metrics should be forecast before a project begins and measured on an ongoing basis once a facility is operational. Data center decarbonization must be measured, modeled, and continuously verified - not simply promised.

The technical challenge with this is that data centers are not static assets - they must navigate fluctuating loads, changing weather patterns, cooling systems that behave differently at part load, and increasing rack densities. The interaction between servers, cooling equipment, electrical systems, local climate, and the grid is dynamic by nature.

That makes static calculations a weak basis for long-term decisions. Peak-load assessments and spreadsheet calculations may help with early sizing, but they cannot properly capture annual performance, seasonal variation, part-load behavior, or climate-specific risk. Computational Fluid Dynamics (CFD) is useful for analyzing airflow and hotspots in data halls, but it does not, by itself, show whole-facility energy, water, and carbon performance across a full year.

For high-density AI workloads, which generate significant heat and often require more advanced cooling strategies, this is a big problem. The sector is already moving beyond conventional air cooling towards more advanced liquid and hybrid cooling technologies, including direct-to-chip, free/evaporative/immersion cooling, rear-door heat exchangers, and aisle containment strategies. These technologies can offer major gains, but their performance depends on climate, site conditions, operational profile, and integration with the wider facility.

The solution, as explored in our recent whitepaper, De-risking High-Performance Data Centres with Dynamic Simulation, is whole-facility, climate-specific dynamic simulation that tests how IT loads, cooling systems, controls, local weather, and building fabric interact over time. Dynamic simulation technology can create a more reliable evidence base for decisions that affect power demand, water use, carbon emissions, and resilience.

Better evidence can also help viable projects move faster. If developers can show how a proposed facility is expected to perform under real climate conditions, how it will manage both peak and part-load demands, and how emissions will be reduced over time, assumptions can be tested, trade-offs made visible, and weak points addressed earlier.

This is especially important for existing facilities. Uptime Institute’s Global Data Center Survey 2024 found that nearly half of respondents work primarily with a facility more than 11 years old. Many were not designed for today’s AI-driven rack densities or liquid cooling requirements, meaning that retrofitting those assets will be central to decarbonization - not least because existing sites often already have the grid access that new projects struggle to secure.

A more transparent performance culture may also help to address skepticism around corporate climate claims. Recent reporting on proposed UK data centers has highlighted how easily carbon and energy assumptions can be misinterpreted or inconsistently presented during planning. If operators want to make carbon-related claims, those claims need to be grounded in consistent reporting and operational evidence.

The fact of the matter is that data centers will continue to grow because demand for digital services will continue to grow. If capacity is delivered through inefficient designs, weak assumptions, and limited operational transparency, the sector risks eroding public trust and placing greater strain on already stretched energy systems. If growth is backed by rigorous modeling, measured performance, and verified improvements, project teams can make a stronger case for the infrastructure they need.

AI may be accelerating the demand curve, but it does not remove the sector’s responsibility to use energy and water wisely. The next phase of data center decarbonization must be judged by performance in use. That means moving beyond promises and proving, facility by facility, that digital infrastructure is being built and operated with the efficiency the moment demands.