Publications and Working Papers
Legacy Lending Relationships and Credit Rationing: Evidence from the Paycheck Protection Program
Working Paper
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" (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.
Structure, Risk, and Access to Credit: Reassessment of the Paycheck Protection Program Effectiveness
Working Paper
Using PSM-DID on a balanced panel from March to September 2020 and exploit variation in loan holding duration, I found 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 (significant but limited), respectively, with early loan recipiency strengthening all three margins. Heterogeneity analysis shows that small–medium firms and borrowers with intermediate pre-crisis risk experience the largest gains, while micro, very large, 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.
Modular Landfill Remediation for AI Grid Resilience
with Qi He, Working Paper
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.
Waste-to-Energy-Coupled AI Data Centers: Cooling Efficiency and Grid Resilience
with Qi He, Working Paper
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.
