Energy detection is widely used in cooperative spectrum sensing because it requires little prior information about the primary signal, but its performance is sensitive to node-dependent noise power and the signal-to-noise ratio (SNR). This paper proposes a dynamic dual-threshold method under bounded noise power uncertainty. For each sensing node, the local energy statistic is modeled under the idle and occupied hypotheses, and a Bayes-optimal one-sided threshold is evaluated for every admissible noise power value in a multiplicative interval. The lower and upper thresholds are defined as the minimum and maximum of these candidate thresholds. Observations outside the interval are transmitted as one-bit hard decisions, whereas uncertain region observations are normalized, uniformly quantized, and combined at the fusion center by equal gain fusion. Controlled simulations at a matched global false alarm probability show that a well-calibrated fixed dual-threshold benchmark can be competitive near its design point, while node-specific dynamic adaptation becomes advantageous as the uncertainty level increases. The uncertain region reporting probability rises with the uncertainty bound, making the robustness–reporting tradeoff explicit. The results support dynamic threshold adaptation under moderate or relatively large noise power uncertainty and clarify its associated communication cost.