Among policy instruments to control future greenhouse gas emissions, well-calibrated general intensity targets are known to lead to lower uncertainty on the amount of abatement than emissions quotas (Jotzo and Pezzey 2004). The authors test whether this result holds in a broader framework, and whether it applies to other policy-relevant variables as well. To do so, they provide a general representation of the uncertainty on future GDP, future business-as-usual emissions, and future abatement costs. The authors derive the variances of four variables, namely (effective) emissions, abatement effort, marginal abatement costs, and total abatement costs over GDP under a quota, a linear (LIT) and a general intensity target (GIT)-where the emissions ceiling is a power-law function of GDP. They confirm that GITs can yield a lower variance than a quota for marginal costs, but find that this is not true for total costs over GDP. Using economic and emissions scenarios and forecast errors of past projections, the authors estimate ranges of values for key parameters in their model. They find that quotas dominate LITs over most of this range, that calibrating GITs over this wide range is difficult, and that GITs would yield only modest reductions in uncertainty relative to quotas.