EU-CRD: Responsibility-Guided Credit Assignment for Forecast-Aware Carbon Scheduling

Geo-distributed datacentres can reduce carbon emissions by shifting delay-tolerant jobs across time and space to better match renewable supply. However, such spatial-temporal scheduling depends on weather-dependent energy forecasts, where inevitable forecasting errors yield suboptimal decisions or even mislead the scheduler. Conventional reinforcement learning learns from returns that reflect both routing quality and forecast quality, without an explicit mechanism to isolate their contributions. EU-CRD (Epistemic Uncertainty Counterfactual Responsibility Decomposition) addresses this credit-assignment problem by constructing a hindsight forecast regret proxy and a policy-action value gap, modulating the latter with critic ensemble disagreement, and using their relative magnitudes to reweight the routing advantage during training. No additional responsibility computation is required at deployment. In a five-datacentre simulation driven by measured wind, paired continuation runs from three shared checkpoints compare EU-CRD with a matched control retaining the same backbone, critic ensemble, responsibility computation and auxiliary training. Under deterministic decoding on six development windows, EU-CRD reduces carbon emissions by $14.5\%$ with a degraded forecast and by $10.1\%$ with a perfect forecast. The same models also reduce carbon by $12.2\%$ and $10.5\%$, respectively, on six confirmation windows from a separate year. All three paired seeds favour EU-CRD under both forecast settings in both evaluations, with all service requirements met. On the development windows, alternative objectives and component ablations also yield higher carbon emissions under degraded forecasts for every seed. These results support responsibility-guided reweighting as a training-only mechanism for forecast-aware carbon scheduling.