Modular Landfill Remediation for AI Grid Resilience

TL;DR

This paper introduces a modular landfill remediation framework that transforms legacy sites into resilience assets, reducing methane emissions and providing microgrid power to enhance AI infrastructure stability within U.S. regulatory constraints.

Contribution

It proposes a novel modular approach to landfill remediation that integrates environmental and energy resilience, addressing U.S. institutional barriers to de-landfilling.

Findings

Mitigates methane emissions by 60-70% at sites

Reclaims urban land for alternative uses

Provides 20 MW of microgrid power for AI infrastructure

Abstract

Rising AI electricity demand and persistent landfill methane emissions constitute coupled constraints on U.S. digital infrastructure and decarbonization. While China has achieved a rapid 'de-landfilling' transition through centralized coordination, the U.S. remains structurally 'locked in' to landfilling due to fragmented governance and carbon accounting incentives. This paper proposes a modular legacy landfill remediation framework to address these dual challenges within U.S. institutional constraints. By treating legacy sites as stock resources, the proposed system integrates excavation, screening, and behind-the-meter combined heat and power (CHP) to transform environmental liabilities into resilience assets. A system analysis of a representative AI corridor demonstrates that such modules can mitigate site-level methane by 60-70% and recover urban land, while supplying approximately 20 MW of firm, islandable power. Although contributing only approximately 5% of a hyperscale data center's bulk load, it provides critical microgrid resilience and black-start capability. We conclude that remediation-oriented waste-to-energy should be valued not as a substitute for bulk renewables, but as a strategic control volume for buffering critical loads against grid volatility while resolving long-term environmental liabilities.