Waste-to-Energy Coupled AI Data Centers Improve Cooling Efficiency

Qi He and Chunyu Qu have submitted a research paper proposing an integrated Waste-to-Energy-AI Data Center configuration that uses WtE heat to provide absorption cooling and improve grid resilience (submitted 31 Dec 2025).

• Main announcement: The paper proposes an integrated Waste-to-Energy-AI Data Center configuration that treats cooling as a first-class energy service, using energy-grade matching where low-grade WtE thermal output drives absorption cooling to displace baseline cooling electricity; the authors provide a computable prototype for Levelized Cost of Computing (LCOC) and an ESG valuation channel.
• Key details & methodology: The thermoeconomic outcome is governed by three determinants: (i) cooling coverage of IT heat load, (ii) parasitic electricity for transport and auxiliaries, and (iii) distance-driven delivery decay producing a break-even corridor; comparative statics examine IT utilization, feedstock quality (waste LHV and throughput), climate parameterization, and corridor distance; paper includes PDF and arXiv metadata (PDF: https://arxiv.org/pdf/2512.24683, arXiv:2512.24683).