564 stringent than the optimal policy (see the relative carbon prices in Figure II). Therefore, the political 565 environment after the delay period not only has to be amenable to the original optimal policy in the 566 delayed sectors or across the entire economy, but it may require an environment that is exceedingly 567 (perhaps unreasonably) favorable so that an even more stringent carbon price can be implemented. 568 A corollary of these results is that, in formulating climate policies under PECs, preserving the 569 optimal timing of abatement investment is a higher priority than the optimal allocation of emissions 570 across sectors; in other words, doing “something” is very much better than waiting to implement a 571 more theoretically efficient policy in the future. Comparing the overall cost increases between policy 572 suite 2 and 3 support this point: the difference in the additional policy cost induced by delaying 573 the decarbonization of energy by a decade between policy suites 2 and 3 is _∼_ $2 trillion, granted 574 this difference is smaller for industry. Of course, each of these options are better than delaying all 575 climate policies.<sup>21</sup> We conclude that, if a policymaker were facing political headwinds while still 576 wanting to achieve climate goals, implementing a sub-optimal carbon price or ETS that delays the full 577 decarbonization of challenged sectors may be an attractive policy approach. This conclusion would 578 support the idea of exemptions for politically sensitive sectors if and only if they make it possible to 579 implement the policy sooner.<sup>22</sup> 580 Throughout, we argued that energy is the more expensive sector to delay decarbonizing because of 581 its high abatement potential. We now make this notion concrete by computing the relative policy cost 582 of delaying each sector in our numerical experiments by five and ten years in Figure VII. We find that 583 our earlier arguments are well-supported: energy is consistently the most expensive sector to delay. 584 In computing the correlation of each sectoral characteristic and relative aggregate policy costs (see 585 Table IV), we find that the annual emissions rate is consistently the most correlated sectoral parameter 586 with changes in relative policy cost increases, followed by the marginal investment cost and the capital 587 depreciation rate.<sup>23</sup> These results would suggest that policymakers using delay to assuage PECs should

> 21One possible exception to this conclusion could be policies which are administratively burdensome and do not lead to tangible emissions reductions. In this case one would have the worst of both worlds: nontrivial costs and a reduced remaining carbon budget, which would demand an accelerated and costly policy schedule later to remain compliant with the emissions budget.

> 22This point would suggest that the approach taken by the EU in their establishment of ETS2 is prudent from an economic and political perspective.

> 23We note that there is significant anti-covariance between emissions rates and marginal investment costs in the IPCC AR6 data (Intergovernmental Panel on Climate Change, 2022b); energy and forestry are at once the cheapest sectors and the most emissions intensive, while waste has the lowest emissions rate and the highest marginal investment cost. This happenstantial attribute of the data does skew our results somewhat; for example, excluding energy, forestry, and waste results in unclear correlation structures between additional policy costs and sectoral characteristics. However it would be difficult to claim a relationship with only four sectors is at all statistically significant. We therefore rely on the intuition established above in our analysis: the higher the emissions rate, the more of a distortion in the optimal allocation of emissions across ETSs, causing higher additional policy costs relative to the optimal. Note this statement is

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