Editing IPCC:AR6/WGIII/Chapter-2 (section)

=== 2.7.1 Introduction: Clarification of Concepts ===

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Carbon lock-in can be understood as inertia in a system that limits the rate of transformation by a path-dependent process ( [[#Seto--2016|Seto et al. 2016]] ). For example, long lifetimes of infrastructures such as power plants, roads, buildings or industrial plants may influence the rate of transformation substantially and lock societies into carbon-intensive lifestyles and practices for many decades ( [[#Unruh--2000|Unruh 2000]] , 2002; [[#Unruh--2006|Unruh and Carrillo-Hermosilla 2006]] ; [[#Grubler--2012|Grubler 2012]] ; [[#Seto--2016|Seto et al. 2016]] ; [[#Sovacool--2016|Sovacool 2016]] ). Infrastructure stock evolution depends on technological and economic factors, but also on institutional and behavioural ones that are often mutually reinforcing. That is, physical infrastructure such as the built environment of urban areas can shape people’s behaviour and practices, which in turn change the demand for such infrastructure and lock-in energy demand patterns ( [[#Banister--1997|Banister et al. 1997]] ; [[#Makido--2012|Makido et al. 2012]] ; [[#Creutzig--2016|Creutzig et al. 2016]] ; [[#Seto--2016|Seto et al. 2016]] ; [[#Shove--2018|Shove and Trentmann 2018]] ).

There is a broad literature on carbon lock-in related to infrastructure that has analysed different geographical scales and sectors, with a strong focus on the power sector (Fisch-Romito et al. 2020). Available quantifications differ in the time frames of analysis that can be classified as backward-looking, static for a given year, or forward-looking using scenarios (Fisch-Romito et al. 2020). Quantifications also differ in the indicators used to describe carbon lock-in. Literature has assessed how delays in climate policy affect the evolution of fossil-fuel infrastructure stock in the short term ( [[#Bertram--2015|Bertram et al. 2015]] ; [[#Kefford--2018|Kefford et al. 2018]] ; [[#McGlade--2018|McGlade et al. 2018]] ), overall mitigation costs ( [[#Riahi--2015|Riahi et al. 2015]] ; [[#Luderer--2016|Luderer et al. 2016]] ), or the transition risks from premature retirements or underutilisation of existing assets ( [[#Iyer--2015|Iyer et al. 2015]] ; [[#Johnson--2015|Johnson et al. 2015]] ; [[#Lane--2016|Lane et al. 2016]] ; [[#Luderer--2016|Luderer et al. 2016]] ; [[#Farfan--2017|Farfan and Breyer 2017]] ; [[#van%20Soest--2017|van Soest et al. 2017]] ; [[#Kefford--2018|Kefford et al. 2018]] ; [[#Cui--2019|Cui et al. 2019]] ; [[#Fofrich--2020|Fofrich et al. 2020]] ; [[#Malik--2020|Malik et al. 2020]] ; H. [[#Wang--2020|Wang et al. 2020]] ; [[#Pradhan--2021|Pradhan et al. 2021]] ). Only a few authors have relied on indicators related to institutional factors such as technology scale or employment ( [[#Erickson--2015|Erickson et al. 2015]] ; [[#Spencer--2018|Spencer et al. 2018]] ). Complementary literature has explored how the sheer size of the world’s fossil fuel reserves (and resources) and owners’ financial interests could contribute to supply-side dynamics that sustain the use of fossil fuels ( [[#Jewell--2013|Jewell et al. 2013]] ; Jakob and Hilaire 2015; [[#McGlade--2015|McGlade and Ekins 2015]] ; [[#Bauer--2016|Bauer et al. 2016]] ; [[#Heede--2016|Heede and Oreskes 2016]] ; [[#Welsby--2021|Welsby et al. 2021]] ).

One way of quantifying potential carbon lock-in is to estimate the future CO 2 emissions from existing and planned infrastructure ( [[#Davis--2010|Davis et al. 2010]] ; [[#Davis--2014|Davis and Socolow 2014]] ) based on historic patterns of use and decommissioning. Such estimates focus on CO 2 emissions from operating infrastructure and do not comprise any upstream or downstream emissions across the lifecycle, which are provided elsewhere in the literature ( [[#Müller--2013|Müller et al. 2013]] ; [[#Creutzig--2016|Creutzig et al. 2016]] ; [[#Krausmann--2020|Krausmann et al. 2020]] ; [[#Fisch-Romito--2021|Fisch-Romito 2021]] ). Estimates tend to focus on energy, while other areas, such as the agricultural sector are usually not covered. Another strand of literature quantifies lock-in by estimating fossil-fuel related CO 2 emissions that are hard to avoid in future scenarios using integrated assessment models (IAMs) ( [[#Kriegler--2018b|Kriegler et al. 2018b]] ; [[#Luderer--2018|Luderer et al. 2018]] ). The remainder of this chapter will assess potential carbon lock-in through those two related strands of literature.

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