Search arXiv⌕ Search

arXiv subjects

Jiawei Qu

Publications and source records attributed to Jiawei Qu.

2 recordsLinked to original sources
arXiv2026

Moving bits instead of watts: Accelerating global AI data-centre deployment at the energy-digital-water-land nexus

Artificial intelligence is driving an unprecedented expansion of data centres. However, this growth is increasingly stalled by grid transmission constraints that trap new data centres in years-long connection queues. Unlike electricity, computation can be moved across the globe through fibre-optic networks, but whether this spatial transfer can bypass constrained grids and accelerate global AI data-centre deployment remains unknown. Here we develop a global framework that places future data-centre capacity in energy-abundant regions and routes computational workloads through fibre-optic networks rather than transmitting electricity. We evaluate the framework through more than 11 million hourly operation simulations at subnational resolution, integrating constraints on energy systems, water, digital connectivity, land and financing risk. Results show that across multiple data-centre development and climate-policy scenarios, leveraging global data transfer allows new data centres to be sited where these conditions are favourable, lowering construction costs by 44-47% and carbon emissions by 38%. By treating the global fibre-optic network as an energy-system asset, it avoids 34-67 GW of high-voltage transmission capacity, comparable in magnitude to Italy's national peak electricity demand. Crucially, this methodology could bring more than 95% of connection-delayed data centres online in 2030, with Canada, the Nordic countries and the Arabian Peninsula emerging as preferred locations for data-centre development. Our findings establish computational mobility as an alternative to energy-system expansion, informing the coordinated global development of energy and artificial-intelligence infrastructure.

eess.SY↗
arXiv2026

Right Model, Right Task: Capability-Constrained Carbon Labeling to Decarbonize Generative AI

The carbon footprint of generative-AI inference depends not only on hardware efficiency and electricity supply, but also on whether the selected model is appropriate for the task. A frontier model may expend unnecessary computation on a routine request, whereas an under-capable model may increase energy use through failure, retries, extended reasoning and escalation. However, a standardized evaluation framework that accounts for task-specific capability requirements and the carbon emissions associated with successful task remains lacking. In this Perspective, we argue for a capability-constrained carbon labeling framework that shifts environmental assessment from carbon intensity per inference towards the expected carbon emissions per successfully completed task at the required capability level. Under this framework, models would first be assessed against task-specific capability requirements, and only models capable of meeting those requirements would be compared on their expected energy use and operational carbon emissions. By linking service quality to carbon cost, this approach could enable more informed matching of the right model to the right task, offering a demand-side pathway to reducing emissions associated with generative-AI token use. Realizing this vision will require standardized capability-based service assessments, comprehensive energy accounting and transparent carbon disclosures.

eess.SY↗