Editing IPCC:AR6/WGIII/Chapter-1 (section)

==== 1.7.1.2 Dynamic Efficiency and Uncertainty ====

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Care is required to clarify what is optimised ( [[#Dietz--2019|Dietz and Venmans 2019]] ). Optimising a path towards a given temperature goal ''by a fixed date'' (e.g., 2100) gives time-inconsistent results backloaded to large, last-minute investment in carbon dioxide removal (CDR). ‘Cost-effective’ optimisations generate less initial effort than ''equivalent'' cost-benefit models ( [[#Dietz--2019|Dietz and Venmans 2019]] ; [[#Gollier--2021|Gollier 2021]] ) as they do not incorporate benefits of reducing impacts earlier.

‘Efficient pathways’ are affected by inertia and innovation. Inertia implies amplifying action on long-lived investments and infrastructure that could otherwise lock-in emissions for many decades ( [[#Vogt-Schilb--2018|Vogt-Schilb et al. 2018]] ; [[#Baldwin--2020|Baldwin et al. 2020]] ). [[IPCC:Wg3:Chapter:Chapter-3|Chapter 3]] ( [[IPCC:Wg3:Chapter:Chapter-3#3.5|Section 3.5]] ) discusses interactions between near-, medium- and long-term actions in global pathways, particularly vis-à-vis inertia. Also, to the extent that early action induces low-carbon innovation, it ‘multiplies’ the optimal effort (for given damage assumptions), because it facilitates subsequent cheaper abatement. For example, a ‘learning-by-doing’ analysis concludes that early deployment of expensive PV was of net global economic benefit, due to induced innovation ( [[#Newbery--2018|Newbery 2018]] ).

Research thus increasingly emphasises the need to understand climate transformation in terms of dynamic, rather than static, efficiency ( [[#Gillingham--2018|Gillingham and Stock 2018]] ). This means taking account of inertia, learning and various additional sources of ‘path-dependence’. Including induced innovation in stylised IAMs can radically change the outlook ( [[#Acemoglu--2012|Acemoglu et al. 2012]] , 2016), albeit with limitations ( [[#Pottier--2014|Pottier et al. 2014]] ); many more detailed-process IAMs now do include endogenous technical change (as reviewed in [[#Yang--2018|Yang et al. 2018]] and [[#Grubb--2021b|Grubb et al. 2021b]] ) (Annex III).

These dynamic and uncertainty effects typically justify greater upfront effort ( [[#Kalkuhl--2012|Kalkuhl et al. 2012]] ; [[#Bertram--2015|Bertram et al. 2015]] ), including accelerated international diffusion ( [[#Schultes--2018|Schultes et al. 2018]] ), and strengthen optimal initial effort in cost-benefit models ( [[#Baldwin--2020|Baldwin et al. 2020]] ; [[#Grubb--2021b|Grubb et al. 2021b]] ). Approaches to risk premia common in finance would similarly amplify the initial mitigation effort, declining as uncertainties reduce ( [[#Daniel--2019|Daniel et al. 2019]] ).

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