柔軟な計算負荷の炭素効果：ディスパッチ・投資・最適契約の分布非依存理論

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:柔軟負荷の炭素影響を分布非依存で定式化した理論的貢献。

🏢実務担当者:再エネ余剰を活用する契約設計により、計算負荷の炭素削減と収益性を両立できる可能性。

🏛政策担当者:柔軟負荷の市場参加ルールや契約条件が炭素排出に与える影響を考慮した政策設計の必要性。

Bitcoin mining and AI training are among the fastest-growing electricity demands, and both are flexible loads, ramping up when power is plentiful and off when scarce. Whether that flexibility warms the grid or helps clean it is still unsettled, since careful studies reach opposite conclusions for the same technology in different regions. The catch is that a flexible load pulls carbon two ways at once. It keeps fossil plants running in idle hours, which raises emissions, and it funds new renewable capacity, which lowers them. This study solves dispatch and long-run investment together to identify when such loads cut carbon and the contract that does so. We find the equilibrium cutoffs are distribution-free, pure ratios of prices and costs for every demand shape. The load enters down to (phi)₁ = Cₘ/(R−p), and the net carbon change collapses to a single closed-form functional, (delta) = Phi(F). Under an unconstrained run-whenever-profitable rule, a load large enough to matter always raises emissions. Under a surplus-only contract that runs computing only on otherwise-wasted renewable power, dispatch becomes carbon-optimal. The same cutoffs carry over to a competitive market of third-party miners, where fossil planning separates entirely from the load. Across eight demand distributions the load raises profit and cuts blackout risk in sixteen of sixteen cases, and carbon falls only in a bounded cheap-renewable regime. These findings establish when, and in which market, flexible computing can decarbonize the power grid, in a form robust to the regional heterogeneity that causes previous estimates to disagree.

• openalex https://journals.gmu.edu/jssr/article/view/5665first seen 2026-10-12 04:40:45