On-chain Peak Shaving

Paper: arXiv 2604.19956

Authors: Irene Aldridge, Gavhar Annaeva, Leyla Beriker, Zhiheng Cai, Samyak Choudhary, Camila Godoy, Kaicheng Gong, Zitao Huang, Jonah Ji, Hetvi Kharvasiya, Heng Li, Yuxuan Li, Tianchi Ma, Qingcheng Meng, Ruiyang Shi, Ananya Shrivastava, Jiaqi Wang, Yifan Wang, Zihua Wu, Jiayang Xu, Yuheng Yan, Zijun Zeng, Bowen Zhang, Francesco Zhang

Abstract

Blockchain technology is widely expected to reduce transaction costs by automating contract enforcement and eliminating intermediaries; yet, the execution costs imposed by network congestion have received little attention in the operations management literature. We study on-chain peak shaving, the systematic scheduling of Ethereum transactions toward low-congestion windows to reduce gas fee exposure. We use transaction-level data from seven firms across seven industries (N = 62,142 transactions, January-March 2026). Gas fees vary significantly throughout the day: the peak-hour premium at 10 AM Eastern Time reaches USD 0.220 per transaction above the overnight baseline, driven primarily by speculative-arbitrage demand rather than operational activity. Firm-level scheduling responses are heterogeneous and not uniformly disciplined. Only three of seven firms transact disproportionately during off-peak hours; four transact counter-cyclically, concentrated in peak windows due to external deadlines or governance cycles. This heterogeneity is explained by two moderators: transaction deferrability and gas intensity. We formalize these into an On-Chain Scheduling Matrix that maps firms to four regimes: 1) full peak shaving, 2) selective peak shaving, 3) cost provisioning, and 4) accept-market-rate, with regime membership predicting both fee savings and residual cost floors (40-92 percent of actual expenditure). Theoretically, we extend Transaction Cost Economics to account for time-varying execution costs imposed by congestion externalities. In addition to extending Williamson’s original cost taxonomy, we introduce a dual classification of gas fees as execution costs in timing but maladaptation costs in origin. The findings reposition on-chain gas-fee management alongside energy procurement and foreign exchange hedging as a domain requiring systematic operational planning.

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 6.0/10 (capped from 7.5: transaction costs not modelled; no out-of-sample test; see penalty flags)
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper relies on descriptive statistics, transaction-level data analysis, and firm-level clustering rather than advanced stochastic calculus or complex formal proofs. Its strength lies in its grounded empirical approach to operational efficiency and the systematic analysis of real-world blockchain transaction data.

Research Flowchart

  flowchart TD
    A[<b>Research Goal:</b> On-chain peak shaving to reduce gas fees] --> B[<b>Input:</b> 62,142 Ethereum transactions across 7 industries]
    B --> C[<b>Process:</b> Analyze transaction timing vs. congestion/fee cycles]
    C --> D[<b>Methodology:</b> Develop + Apply 'On-Chain Scheduling Matrix']
    D --> E[<b>Analysis:</b> Evaluate firm-level heterogeneity in deferrability & intensity]
    E --> F[<b>Outcomes:</b> Categorize into 4 regimes + Extend Transaction Cost Economics theory]

Cited References (213)

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5. Do Formal Contracts and Relational Governance Function as Substitutes or Complements? (2002)
6. Distributed Ledgers and Operations: What Operations Management Researchers Should Know about Blockchain Technology (2020)
7. The Supply Chain Has No Clothes: Technology Adoption of Blockchain for Supply Chain Transparency (2018)
8. A Supply Chain Traceability System for Food Safety Based on HACCP , Blockchain and Internet of Things (2017)

0 of 213 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.