CarbonX: An Open-Source Tool for Computational Decarbonization Using Time Series Foundation Models
Year: 2026
Type: conference-paper
Abstract: Computational decarbonization aims to reduce carbon emissions in computing and societal systems such as data centers, transportation, and built environments. This requires accurate, fine-grained carbon intensity forecasts, yet existing tools have several key limitations: (i) they require grid-specific electricity mix data, restricting use where such information is unavailable; (ii) they depend on separate grid-specific models that make it challenging to provide global coverage; and (iii) they provide forecasts without uncertainty estimates, limiting reliability for downstream carbon-aware applications.
Language: English
Topic: Green IT and Sustainability
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
Awards: Collaborative Research: CNS Core: Medium: IoCT: System Mechanisms for Enabling an Internet of Collaborative Things, Collaborative Research: NGSDI: CarbonFirst: A Sustainable and Reliable Carbon-Centric Cloud-Edge Software Infrastructure, G3575947144, CCRI: New: A Community Testbed for Designing Carbon-Efficient Cloud Applications, CNS Core: Medium:Model-driven Resource Management for Avoiding Performance Pitfalls in Edge Computing, G6503531606, National Science Foundation Expeditions in Computing for Computational Decarbonization of Societal Infrastructures at Mesoscales
OpenAlex ID: W4414817294