# Carbon Explorer: A Holistic Approach for Designing Carbon Aware Datacenters

arXiv: https://arxiv.org/abs/2201.10036 · HTML: https://mlsanity.com/paper/2201.10036/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2022.

## Built on by (most cited first, 30 of 32)

- [LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models](https://mlsanity.com/paper/2309.14393/) (2023)
- [Characterization of Large Language Model Development in the Datacenter](https://mlsanity.com/paper/2403.07648/) (2024)
- [Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models](https://mlsanity.com/paper/2304.03271/) (2023)
- [MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AI](https://mlsanity.com/paper/2410.12032/) (2024)
- [Toward Sustainable GenAI using Generation Directives for Carbon-Friendly Large Language Model Inference](https://mlsanity.com/paper/2403.12900/) (2024)
- [Towards Environmentally Equitable AI via Geographical Load Balancing](https://mlsanity.com/paper/2307.05494/) (2023)
- [Online Conversion with Switching Costs: Robust and Learning-Augmented Algorithms](https://mlsanity.com/paper/2310.20598/) (2023)
- [Beyond Efficiency: Scaling AI Sustainably](https://mlsanity.com/paper/2406.05303/) (2024)
- [SustainDC: Benchmarking for Sustainable Data Center Control](https://mlsanity.com/paper/2408.07841/) (2024)
- [CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centers](https://mlsanity.com/paper/2311.03615/) (2023)
- [EnsembleCI: Ensemble Learning for Carbon Intensity Forecasting](https://mlsanity.com/paper/2505.01959/) (2025)
- [Chasing Convex Functions with Long-term Constraints](https://mlsanity.com/paper/2402.14012/) (2024)
- [Carbon Footprint Reduction for Sustainable Data Centers in Real-Time](https://mlsanity.com/paper/2403.14092/) (2024)
- [Uncertainty-Aware Decarbonization for Datacenters](https://mlsanity.com/paper/2407.02390/) (2024)
- [CarbonX: An Open-Source Tool for Computational Decarbonization Using Time Series Foundation Models](https://mlsanity.com/paper/2510.01521/) (2025)
- [Green Federated Learning via Carbon-Aware Client and Time Slot Scheduling](https://mlsanity.com/paper/2509.08980/) (2025)
- [PowerSlider: Exploiting Phase Asymmetry for LLM Serving under Demand Response](https://mlsanity.com/paper/2608.21719/) (2026)
- [Learning-Augmented Competitive Algorithms for Spatiotemporal Online Allocation with Deadline Constraints](https://mlsanity.com/paper/2408.07831/) (2024)
- [PALS: Power-Aware LLM Serving for Mixture-of-Experts Models](https://mlsanity.com/paper/2605.21427/) (2026)
- [XWind: A Cross-site Router for Large Language Model Inference Serving at Renewable Energy Farms](https://mlsanity.com/paper/2605.23348/) (2026)
- [Sustainability of Data Center Digital Twins with Reinforcement Learning](https://mlsanity.com/paper/2404.10786/) (2024)
- [Carbon Connect: An Ecosystem for Sustainable Computing](https://mlsanity.com/paper/2405.13858/) (2024)
- [Towards Environmentally Equitable AI](https://mlsanity.com/paper/2412.16539/) (2024)
- [Green AI: A systematic review and meta-analysis of its definitions, lifecycle models, hardware and measurement attempts](https://mlsanity.com/paper/2511.07090/) (2025)
- [GaiaFlow: Semantic-Guided Diffusion Tuning for Carbon-Frugal Search](https://mlsanity.com/paper/2602.15423/) (2026)
- [Distributed LLM Pretraining During Renewable Curtailment Windows: A Feasibility Study](https://mlsanity.com/paper/2602.22760/) (2026)
- [Green AI Carbon Optimizer: Carbon-Efficient Training Location Recommendation and Global AI Energy Demand Forecasting](https://mlsanity.com/paper/2606.14707/) (2026)
- [Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems](https://mlsanity.com/paper/2607.03324/) (2026)
- [SLA-Constrained Carbon-Aware Routing in Geo-Distributed Serverless Clouds](https://mlsanity.com/paper/2607.22806/) (2026)
- [AgentDecarbonizer: Carbon-Aware Execution for AI Agents](https://mlsanity.com/paper/2608.20566/) (2026)

## Abstract (arXiv)

Technology companies have been leading the way to a renewable energy transformation, by investing in renewable energy sources to reduce the carbon footprint of their datacenters. In addition to helping build new solar and wind farms, companies make power purchase agreements or purchase carbon offsets, rather than relying on renewable energy every hour of the day, every day of the week (24/7). Relying on renewable energy 24/7 is challenging due to the intermittent nature of wind and solar energy. Inherent variations in solar and wind energy production causes excess or lack of supply at different times. To cope with the fluctuations of renewable energy generation, multiple solutions must be applied. These include: capacity sizing with a mix of solar and wind power, energy storage options, and carbon aware workload scheduling. However, depending on the region and datacenter workload characteristics, the carbon-optimal solution varies. Existing work in this space does not give a holistic view of the trade-offs of each solution and often ignore the embodied carbon cost of the solutions. In this work, we provide a framework, Carbon Explorer, to analyze the multi-dimensional solution space by taking into account operational and embodided footprint of the solutions to help make datacenters operate on renewable energy 24/7. The solutions we analyze include capacity sizing with a mix of solar and wind power, battery storage, and carbon aware workload scheduling, which entails shifting the workloads from times when there is lack of renewable supply to times with abundant supply.