Fewer CO2 emissions through optimised payment routes

L3S Best Publication of the Quarter (1/2024)
Category: Green AI and Computing  

Reducing CO₂ emissions in a peer-to-peer distributed payment network: Does geography matter in the lightning network?  

Authors: Danila Valko, Daniel Kudenko 
Published in the Q1 journal “Computer Networks” 

The paper in a nutshell: 

Our paper investigates the sustainability of blockchain-related payment networks with an example on the Lightning Network (LN). We propose a solution to reduce carbon dioxide emissions by optimizing payment routes based on geographical distribution and electricity carbon intensity, while maintaining network efficiency. 

The Lightning Network (LN) is a secondary P2P network layer that operates on top of existing blockchain infrastructure. It is aimed to enable fast and anonymous multi-hop payments on a global scale. For an introduction to the basics of LN and specific preliminaries, we would refer the reader to http://dx.doi.org/10.1016/j.pmcj.2022.101584

What is new about the research? 

Our findings offer a novel approach to sustainability in blockchain-related networks by integrating geographical data and electricity carbon intensity into payment pathfinding algorithms. This research raises important questions on network design and contributes to the growing field of sustainable technology development and green computing, offering an effective solution to address environmental concerns. 

See more details in https://doi.org/10.1016/j.comnet.2024.110297