On the Impact of the Lightning Network on Bitcoin Transaction Fees and Network Value
Bibliographic record
Abstract
This paper examines the influence of second-layer networks, particularly the Lightning Network (LN), on Bitcoin mining fees and network valuation through Metcalfe's law. Analyzing Bitcoin transaction data from January 2014 to November 2023, we observe that transactions with a value below USD 1000 constitute more than 27 % of total mining fees, underscoring their economic importance. The LN's facilitation of faster, cheaper offchain transactions, particularly transactions of small value, could possibly decrease miners' fee revenues, especially from smaller transactions, which are a significant revenue source. To examine the impact of LN on the Bitcoin network, we employ Metcalfe's law to model Bitcoln's value over the next six years, correlating it with the number of daily active users to gauge network growth. Incorporating LN adoption rates into our model suggests that while LN may reduce fee-based income for miners in terms of the number of Bitcoins they receive, it is poised to enhance the overall network value by expanding the user base and transaction volume. This growth, driven by LN's improved utility and scalability, suggests a longterm net benefit despite short-term fee revenue losses. A part of our analysis is focused on LN's capacity to integrate millions of new users through its adoption by Superhubs. Superhubs could significantly boost the value of the Bitcoin network, counterbalancing the potential dip in mining fees. We explore LN's broader adoption implications, envisioning its role in expanding the Bitcoin value proposition from a Store of Value (SoV) to a Medium of Exchange (MoE). By applying Metcalfe's law, we offer insights into the economic effects of LN's widespread use, contributing to discussions on Bitcoin's scalability, mining sustainability, and digital currency valuation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".