arXiv:2606. 09617v1 Announce Type: cross Abstract: The rapid expansion of AI globally has led to the proliferation of energy-intensive hyperscale data centres (DCs), making them as a structurally challenging component in power system planning and operation.
arXiv:2607. 17391v1 Announce Type: cross Abstract: As hyperscale and colocation AI data centers continue to expand, the electric grid is increasingly required to support large, concentrated loads, with individual facilities ranging from 500 MW to 2 GW.
arXiv:2603. 26678v2 Announce Type: replace-cross Abstract: AI and renewable energy are increasingly framed as a "power couple," on the premise that surging AI demand will accelerate clean-energy investment, yet concerns persist that AI will entrench fossil-fuel carbon lock-in.
arXiv:2606. 10660v1 Announce Type: cross Abstract: AI inference services -- API subscriptions, enterprise chat tools, and SaaS products with embedded AI features -- fall unambiguously within Scope 3 Category 1 under the Corporate Sustainability Reporting Directive (CSRD), which requires disclosure for fiscal years starting January 2024.
arXiv:2606. 14707v1 Announce Type: cross Abstract: AI training and deployment consume substantial electricity, but carbon outcomes remain weakly integrated into routine model development decisions.
arXiv:2606. 00811v1 Announce Type: cross Abstract: Data centers now account for 4.
The study uses deep learning on high‑resolution socioeconomic and sectoral data to project EU27 CO₂ emissions through 2023 and beyond. It finds that, under current trends, EU emissions will exceed the 2030 target by 35 % (620 Mt CO₂ shortfall), with only a few countries on track. While the power sector is on target thanks to renewables, mobility contributes over a third of emissions and shows little progress, indicating structural inertia across member states.
The paper uses deep learning on high‑resolution socioeconomic and sectoral data to project EU27 CO₂ emissions through 2030. It finds that emissions will exceed the EU’s 55% reduction target by 35% (620 Mt CO₂ shortfall), with only a few countries on track. While the power sector is on target thanks to renewables, mobility remains a major source of emissions, indicating structural inertia across member states.
arXiv:2608. 12915v1 Announce Type: cross Abstract: The rapid growth of LLM inference is shifting sustainability concerns from one-time training to continuous serving, where infrastructure decisions shape energy use, carbon emissions, water consumption, and service quality.
The paper reviews 194 papers on using artificial intelligence to optimize data center energy use, coding 63 of them. It finds that most control studies validate only in simulation, none consider water withdrawal or embodied carbon, and savings estimates overlap across methods, preventing ranking. The authors propose CLEAR‑DC, a framework that links control and workload demand through elasticity, reports net benefits, and records energy, carbon, water, embodied share, and validation venue.
arXiv:2609.00847v1 Announce Type: cross Abstract: As machine learning and artificial intelligence find their way into nearly every aspect of climate, weather, and Earth system modeling, it is worth p...
The paper presents a framework that links data‑center electricity demand growth, available generation capacity, and market‑clearing prices to explain rising electricity costs. It first uses a deterministic model to show how varying demand and supply growth estimates influence prices, then extends to stochastic processes that generate probabilistic distributions for supply, demand, and prices. Finally, it formulates generation expansion as a stochastic control problem, illustrating how uncertainties in load forecasts, development risks, and potential overbuilding can dampen investment incentives needed to stabilize prices.
arXiv:2607. 01258v1 Announce Type: cross Abstract: The rapid advancement of Artificial Intelligence (AI) has been accompanied by significant increases in computational and environmental costs, driven by large-scale investments in AI infrastructure, hardware, and software.