Beyond Carbon Capture: Systemic Energy, Governance, and Justice Challenges of AI Data Centers
Original framing: “Carbon storage could curb more than 90% of AI data center emissions, study finds” — Phys.org
Scientific assessments show that CCS can capture up to 90 % of CO₂ at point sources, yet life‑cycle analyses reveal significant upstream emissions from energy use, transport, and storage infrastructure. Moreover, the scalability required for global AI demand would demand storage capacities comparable to entire national oil fields, raising feasibility concerns. Rigorous modelling must therefore incorporate grid decarbonization trajectories and alternative cooling technologies.
The promise of carbon capture for AI data centers masks a deeper reliance on fossil‑fuel infrastructure, echoing historical patterns where technological fixes postpone systemic change.
Indigenous stewardship, historical lessons, and cross‑cultural renewable models reveal that equitable siting, community ownership, and circular design are essential to break the lock‑in of carbon‑intensive compute. Scientific life‑cycle analyses, future scenario modelling, and artistic‑spiritual reflections converge on the insight that demand‑side efficiency and renewable grid integration must precede any reliance on CCS. By aligning policy, corporate responsibility, and marginalized voices, a resilient, just, and low‑carbon AI ecosystem can emerge, turning the trickster’s inversion into a transformative re‑imagining of digital infrastructure.