# CarbonX: An Open-Source Tool for Computational Decarbonization Using Time Series Foundation Models

arXiv: https://arxiv.org/abs/2510.01521 · HTML: https://mlsanity.com/paper/2510.01521/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 46 references that are in the map.
- First posted on arXiv 1 Oct 2025.

## Builds on (most cited first, 13 of 13)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Chronos: Learning the Language of Time Series](https://mlsanity.com/paper/2403.07815/) (2024)
- [MOMENT: A Family of Open Time-series Foundation Models](https://mlsanity.com/paper/2402.03885/) (2024)
- [Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts](https://mlsanity.com/paper/2409.16040/) (2024)
- [Sundial: A Family of Highly Capable Time Series Foundation Models](https://mlsanity.com/paper/2502.00816/) (2025)
- [Sequential Predictive Conformal Inference for Time Series](https://mlsanity.com/paper/2212.03463/) (2022)
- [Carbon Explorer: A Holistic Approach for Designing Carbon Aware Datacenters](https://mlsanity.com/paper/2201.10036/) (2022)
- [Let's Wait Awhile: How Temporal Workload Shifting Can Reduce Carbon Emissions in the Cloud](https://mlsanity.com/paper/2110.13234/) (2021)
- [EnsembleCI: Ensemble Learning for Carbon Intensity Forecasting](https://mlsanity.com/paper/2505.01959/) (2025)
- [Uncertainty-Aware Decarbonization for Datacenters](https://mlsanity.com/paper/2407.02390/) (2024)
- [AI Greenferencing: Routing AI Inferencing to Green Modular Data Centers with Heron](https://mlsanity.com/paper/2505.09989/) (2025)
- [Short-Term Forecasting of CO2 Emission Intensity in Power Grids by Machine Learning](https://mlsanity.com/paper/2003.05740/) (2020)
- [Carbon Containers: A System-level Facility for Managing Application-level Carbon Emissions](https://mlsanity.com/paper/2309.14477/) (2023)

## Built on by (most cited first, 3 of 3)

- [FM-CAC: Carbon-Aware Control for Battery-Buffered Edge AI via Time-Series Foundation Models](https://mlsanity.com/paper/2604.16448/) (2026)
- [TriHead-GAN: A Generative Adversarial Network with Triple-Head Discriminator for Carbon Emission Time Series Generation](https://mlsanity.com/paper/2606.07569/) (2026)
- [FMplex: Model Virtualization for Serving Extensible Foundation Models](https://mlsanity.com/paper/2606.09643/) (2026)

## Abstract (arXiv)

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. In this paper, we present CarbonX, an open-source tool that leverages Time Series Foundation Models (TSFMs) for a range of decarbonization tasks. CarbonX utilizes the versatility of TSFMs to provide strong performance across multiple tasks, such as carbon intensity forecasting and imputation, and across diverse grids. Using only historical carbon intensity data and a single general model, our tool achieves a zero-shot forecasting Mean Absolute Percentage Error (MAPE) of 15.82% across 214 grids worldwide. Across 13 benchmark grids, CarbonX performance is comparable with the current state-of-the-art, with an average MAPE of 9.59% and tail forecasting MAPE of 16.54%, while also providing prediction intervals with 95% coverage. CarbonX can provide forecasts for up to 21 days with minimal accuracy degradation. Further, when fully fine-tuned, CarbonX outperforms the statistical baselines by 1.2--3.9X on the imputation task. Overall, these results demonstrate that CarbonX can be used easily on any grid with limited data and still deliver strong performance, making it a practical tool for global-scale decarbonization.