This study examines the trade-off between spatial resolution and measurement precision in satellite-based quantification of CO₂ point-source emissions using observations from DQ-1, the first spaceborne active CO₂ lidar mission. Allan deviation analysis is used to characterize scale-dependent random errors in XCO₂ retrievals over homogeneous surfaces, and the resulting error estimates are incorporated into Gaussian plume simulations to evaluate how spatial averaging affects emission retrieval under different emission strengths, wind speeds, and observation distances. The results show a nonlinear response: moderate averaging reduces random noise and improves retrieval stability, whereas excessive averaging degrades plume representation through loss of spatial resolution. The preferred averaging scale depends mainly on emission strength, transport distance, and local plume geometry. For strong idealized sources (2000 kg s⁻¹), 50–100 averaging points (3.5–7 km) generally provide the best compromise in favorable controlled simulations, with R² values up to 0.68. For weak sources (500 kg s⁻¹), single-overpass estimates remain close to the detection limit even after averaging (R² < 0.10). Application to seven DQ-1 overpasses of power plants shows that retrieved emissions are often more consistent with reference inventories at intermediate-to-coarse averaging scales, especially 75–150 points (5.25–10.5 km), but this range should not be interpreted as a universal optimum. The agreement should instead be treated as an inventory-based consistency check rather than an independent validation of instantaneous emissions. These findings provide a quantitative basis for choosing spatial averaging scales in DQ-1 point-source applications and identify the main conditions under which single-overpass lidar retrievals are likely to be informative.