arXiv subjects

Chunyu Qu

Publications and source records attributed to Chunyu Qu.

5 recordsLinked to original sources

arXiv2026

Planning Waste-to-Energy-Coupled AI Data Centers Through Grade-Matched Cooling and Corridor Screening

AI data-center growth is increasingly constrained by limited deliverable electricity, interconnection capacity, and cooling demand. This study develops a boundary-consistent screening framework for waste-to-energy (WtE)-coupled AI data-center cooling. It treats cooling as an energy service that can be supplied through grade matching rather than only through electricity-driven mechanical chilling. The framework translates plant-side exportable heat into corridor-level planning metrics by accounting for thermal attenuation, absorption conversion, and parasitic electricity for delivery and auxiliaries. In a reference case, a regulated WtE plant processing 1500 t/day of municipal solid waste at 10 MJ/kg provides about 78.1 MWth of exportable heat. At a 20 km corridor, this yields about 53.0 MW of delivered cooling and 8.0 MWe of net avoided cooling electricity after parasitic loads. The coupled system is governed by operating regimes rather than a single efficiency score. Under baseline assumptions, full thermal coverage extends to about 20.9 km, the quality-adjusted criterion remains positive to about 22.9 km, and net electricity relief remains positive to about 44.7 km. For a 1 GW IT campus at 70 percent utilization and a 5 km corridor, net grid relief ranges from about 116.9 to 264.4 MW across scenarios. The required WtE footprint ranges from about 3 to 148 representative plants, or 0.6 to 40 full-load-equivalent plants at a 25 percent displacement target. The framework identifies when WtE-coupled cooling is corridor-feasible, when hybrid operation is required, and when infrastructure scale becomes the binding constraint. It is intended for screening and comparison, not project-specific hydraulic or plant-cycle design.

eess.SY↗

arXiv2025

Waste-to-Energy-Coupled AI Data Centers: Cooling Efficiency and Grid Resilience

AI data-center expansion is increasingly constrained by the coupled availability of deliverable electricity and heat-rejection (cooling) capacity. We propose and evaluate an integrated Waste-to-Energy-AI Data Center configuration that treats cooling as a first-class energy service rather than an unavoidable electricity burden. The coupled system is modeled as an input-output 'black box' with transparent boundaries and a standalone benchmark in which mechanical chilling is powered by grid electricity. The central mechanism is energy-grade matching: low-grade WtE thermal output drives absorption cooling to deliver chilled service, thereby displacing baseline cooling electricity. We show that thermoeconomic superiority is governed by three first-order determinants, (i) cooling coverage of IT heat load, (ii) parasitic electricity for transport and auxiliaries, and (iii) distance-driven delivery decay, yielding a break-even corridor beyond which net benefits vanish. Comparative statics characterize sensitivity to IT utilization, feedstock quality (waste LHV and throughput), climate parameterization, and corridor distance. We translate these accounting gains into decision language through a computable prototype for Levelized Cost of Computing (LCOC) and an ESG valuation channel grounded in measurable mechanisms, without re-deriving full lifecycle inventories. The framework provides siting-ready feasibility conditions for WtE-AIDC coupling in urban AI corridors under grid stress.

eess.SY↗

arXiv2025

Structure, Risk, and Access to Credit: Reassessment of the Paycheck Protection Program Effectiveness

The Paycheck Protection Program (PPP) was the largest targeted business support program in the United States, yet its firm-level effects remain contested. I link administrative PPP and SBA 7(a) records to a near-universe panel of U.S. employer firms from Dun and Bradstreet, covering roughly 30 million establishments, and evaluate short-run impacts on employment, financial stress, and commercial credit risk. To address non-random take-up, I combine propensity score matching with difference-in-differences on a balanced panel from March to September 2020 and exploit variation in loan holding duration. PPP receipt raises employment by about 0.07 percent on average but improves failure-risk and delinquency-risk percentile rankings by roughly 1.2 and 3.2 points, respectively, with longer loan duration strengthening all three margins. Heterogeneity analysis shows that small-to-medium firms and borrowers with intermediate pre-crisis risk experience the largest gains, while micro firms, very large firms, and highly stressed firms benefit less. Firms without prior 7(a) borrowing relationships realize particularly large credit-score gains. Overall, the evidence indicates that PPP functioned more as a balance-sheet and credit-risk backstop than as a powerful jobs program for the average treated firm. The results highlight how firm structure, pre-crisis financial health, and access to government-backed credit shape the effectiveness of large-scale emergency support.

econ.GN↗

arXiv2025

Legacy Lending Relationships and Credit Rationing: Evidence from the Paycheck Protection Program

This article examines how legacy lending relationships shape the allocation of emergency credit under severe information frictions. Using a novel dataset linking Small Business Administration (SBA) loan records with Dun and Bradstreet microdata for over 26 million U.S. firms, I investigate whether prior participation in the SBA 7(a) program acted as a gateway to the Paycheck Protection Program (PPP). Employing entropy balancing to construct a strictly comparable counterfactual group, I document a distinct dynamic evolution in credit rationing. In the program's initial "panic phase" in April 2020, banks relied heavily on legacy ties as a screening technology: firms with prior 7(a) relationships were approximately 29 percentage points more likely to receive funding than observationally identical non-7(a) firms. By June 2021, however, this insider advantage had largely vanished, suggesting that policy adjustments and extended timelines eventually mitigated the initial intermediation frictions. These findings highlight a fundamental trade-off between speed and equity in crisis response. While leveraging existing credit rails accelerates deployment, it systematically excludes informationally opaque borrowers. I discuss policy implications for designing future digital infrastructure to decouple verification from historical lending relationships.

econ.GN↗

arXiv2025

Modular Landfill Remediation for AI Grid Resilience

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.

eess.SY↗