Editing IPCC:AR6/WGIII/Chapter-6 (section)

=== 6.7.3 Dependence ===

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Path dependence refers to resistance to change due to favourable socio-economic conditions with existing systems; decisions made in the past unduly shape future trajectories. Carbon lock-in is a specific type of path dependence ( [[#Seto--2016|Seto et al. 2016]] ). Given that energy system mitigation will require a major course change from recent history, lock-in is an important issue for emission reductions in the energy sector. While lock-in is typically expressed in terms of physical infrastructure that would need to be retired early to reach mitigation goals, it involves a much broader set of issues that go beyond physical systems and into societal and institutional systems (Table 6.11).

'''Table 6.11 | Lock-in types and typical mechanisms.''' Source: Kotilainen et al. 2020), Reproduced under Creative Commons 4.0 International Licence.

{| class="wikitable"
|-
! Type

! Primary lock-in mechanisms

! References

|-
| Technological (and infrastructural)

| – Economies of scale

– Economies of scope

– Learning effects

– Network externalities

– Technological interrelatedness

| – Arthur (1994); Hughes (1994); Klitkou et al (2015)

– David (1985); Panzar and Willig (1981)

– Arthur (1994)

– David (1985); Katz and Shapiro (1986)

– Arrow (1962); Arthur (1994); David (1985); Van den Bergh and Oosterhuis (2008)

|-
| Institutional

| – Collective action

– Complexity and opacity of politics

– Differentiation of power and institutions

– High density of institutions

– Institutional learning effects

– Vested interests

| – Seto et al (2016)

– Foxon (2002); Pierson (2000)

– Foxon (2002)

– Pierson (2000)

– Foxon (2002); Boschma (2005)

– Boschma (2005)

|-
| Behavioural

| – Habituation

– Cognitive switching costs

– Increasing informational returns

| – David (1985); Barnes et al. (2004); Zauberman (2003); Murray and Haubl (2007)

– Zauberman (2003); Murray and Haubl (2007); Van den Bergh and Oosterhuis (2008)

|}

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==== 6.7.3.1 Societal and Institutional Inertia ====

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A combination of factors – user, business, cultural, regulatory, and transnational – will hinder low-carbon energy transitions. Strong path dependencies, even in early formative stages, can have lasting impacts on energy systems, producing inertia that cuts across technological, economic, institutional and political dimensions ( ''high confidence'' ) ( [[#Rickards--2014|Rickards et al. 2014]] ; [[#Vadén--2019|Vadén et al. 2019]] ) (Chapter 5).

Energy systems exemplify the ways in which massive volumes of labour, capital, and effort become sunk into particular institutional configurations ( [[#Bridge--2013|Bridge et al. 2013]] , 2018). Several embedded factors affect large-scale transformation of these systems and make technological diffusion a complex process:

• '''User environments''' affect purchase activities and can involve the integration of new technologies into user practices and the development of new preferences, routines, habits and evenvalues ( [[#Kanger--2019|Kanger et al. 2019]] ).

'''•''' '''Business environments''' can shape the development of industries, business models, supply and distribution chains, instrument constituencies and repair facilities ( [[#Béland--2016|Béland and Howlett 2016]] ).

'''•''' '''Culture''' can encompass the articulation of positive discourses, narratives, and visions that enhance cultural legitimacy and societal acceptance of new technologies. Regulatory embedding can capture the variety of policies that shape production, markets and use of new technologies.

• '''Transnational community''' can reflect a shared understanding in a community of global experts related to new technologies that transcends the borders of a single place, often a country.

While low-carbon innovation involves systemic change ( [[#Geels--2018|Geels et al. 2018]] ), these are typically less popular than energy supply innovations among policymakers and the wider public. Managing low-carbon transitions is therefore not only a techno-managerial challenge (based on targets, policies, and expert knowledge), but also a broader political project that involves the building of support coalitions that include businesses and civil society ( ''moderate evidence'' , ''high agreement'' ).

Low-carbon transitions involve cultural changes extending beyond purely technical developments to include changes in consumer practices, business models, and organisational arrangements. The development and adoption of low-carbon innovations will therefore require sustained and effective policies to create appropriate incentives and support. The implementation of such policies entails political struggles because actors have different understandings and interests, giving rise to disagreements and conflicts.

Such innovation also involves pervasive uncertainty around technical potential, cost, consumer demand, and social acceptance. Such uncertainty carries governance challenges. Policy approaches facing deep uncertainty must protect against and/or prepare for unforeseeable developments, whether it is through resistance (planning for the worst possible case or future situation), resilience (making sure you can recover quickly), or adaptation (changes to policy under changing conditions). Such uncertainty can be hedged in part by learning by firms, consumers, and policymakers. Social interactions and network building (e.g., supply and distribution chains, intermediary actors) and the articulation of positive visions, such as in long-term, low-emission development strategies, all play a crucial role. This uncertainty extends to the impacts of low-carbon innovations on energy demand and other variables, where unanticipated and unintended outcomes are the norm. For instance, rapid investments in public transport networks could restrict car ownership from becoming common in developing countries ( [[#Du--2017|Du and Lin 2017]] ).

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==== 6.7.3.2 Physical Energy System Lock-In ====

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Current investments in fossil infrastructure have committed 500–700 GtCO 2 of emissions, creating significant risks for limiting warming to 1.5°C (Callaghan 2020) ( ''high confidence'' ). These current investments combined with emissions from proposed fossil infrastructure exceed the emissions required to limit warming to 1.5°C ( ''medium confidence'' ). Existing coal- and gas-fired electricity generation accounts for 200–300 GtCO 2 of committed emissions. Emissions from coal generation are larger than for gas plants ( [[#Smith--2019|Smith et al. 2019]] ; [[#Tong--2019|Tong et al. 2019]] ). The lifetime of coal-fired power plants is 25–50 years, creating long-lasting risks to climate goals ( [[#Erickson--2015|Erickson and Tempest 2015]] ). Gas-fired power plants are younger on average than coal-fired power plants. Industry sector lock-in amounts for more than 100 GtCO 2 , while buildings and transport sector together contribute another 50–100 GtCO 2 ( [[#Erickson--2015|Erickson and Tempest 2015]] ).

Lock-in is also relevant to fossil resources. Both coal and gas exploration continue, and new permits are being issued, which may cause economic ( [[#Erickson--2018|Erickson et al. 2018]] ) as well as non-economic issues ( [[#Boettcher--2019|Boettcher et al. 2019]] ).

The nature of lock-in varies across the energy system. For example, lock-in in urban and transport sectors is different from the electricity sector. Broadly, urban environments involve infrastructural, institutional, and behavioural lock-in ( [[#Ürge-Vorsatz--2018|Ürge-Vorsatz et al. 2018]] ). Addressing lock-in in these sectors requires action by multiple stakeholders and is unlikely with just technological evolution (Table 6.11).

Committed carbon emissions are unevenly distributed. The disproportionate high share of committed emissions in emerging economies is the result of rapid growth in recent years, which has led to a comparably young fossil infrastructure with substantial remaining life ( [[#Shearer--2017|Shearer et al. 2017]] ). Mature industrialised countries tend to have older infrastructures, part of which will be up for retirement in the near future ( [[#Tong--2019|Tong et al. 2019]] ). Coal-fired power plants currently planned or under construction are associated with 150–300 GtCO 2 , of which about 75% and about 10% are located in Asia and the OECD respectively ( [[#Edenhofer--2018|Edenhofer et al. 2018]] ; [[#Pfeiffer--2018|Pfeiffer et al. 2018]] ). If implemented, these new fleets will further shorten all coal plants’ lifetimes by another 10 years for meeting climate goals ( [[#Cui--2019|Cui et al. 2019]] ).

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[[File:20ae4946e03d27364072737297f50f7d IPCC_AR6_WGIII_Figure_6_34.png]]

'''Figure 6.34 | Annual emissions from existing, proposed, and future energy system infrastructure.''' Source: with permission from [[#Tong--2019|Tong et al. 2019]] .

Despite the imperative to reduce use of fossil fuels and the multiple health and other benefits from closing coal-based infrastructure ( [[#Portugal-Pereira--2018|Portugal-Pereira et al. 2018]] ; [[#Liu--2019a|Liu et al. 2019a]] ; Karlsson et al. 2020; [[#Rauner--2020|Rauner et al. 2020]] ; [[#Cui--2021|Cui et al. 2021]] ), coal power plants have continued to be commissioned globally ( [[#Jewell--2019|Jewell et al. 2019]] ; [[#Jakob--2020|Jakob et al. 2020]] ), most notably in Asian countries. Gas power plants also continue to be built. In many regions, new fossil electricity generation exceeds needed capacity ( [[#Shearer--2017|Shearer et al. 2017]] ).

Existing policies and the NDCs are insufficient to prevent an increase in fossil infrastructure and associated carbon lock-in ( ''high confidence'' ) ( [[#Bertram--2015|Bertram et al. 2015]] ; [[#Johnson--2015|Johnson et al. 2015]] ). Current investment decisions are critical because there is limited room within the carbon budget required to limit warming to well below 2°C ( [[#Kalkuhl--2019|Kalkuhl et al. 2019]] ; [[#Rosenbloom--2019|Rosenbloom 2019]] ). Delays in mitigation will increase carbon lock-in and could result in large-scale stranded assets if stringency is subsequently increased to limit warming (Box 6.11). Near-term implementation of stringent GHG mitigation policies are likely to be most effective in reducing carbon lock-in ( [[#Haelg--2018|Haelg et al. 2018]] ). Near-term mitigation policies will also need to consider different energy transition strategies as a result of different resources and carbon budgets between countries ( [[#Lucas--2016|Lucas 2016]] ; [[#Bos--2018|Bos and Gupta 2018]] ).

Near-term policy choices are particularly consequential for fast-growing economies. For example, Malik et al. (2020) found that 133 to 227 GW of coal capacity would be stranded after 2030 if India were to delay ambitious mitigation through 2030 and then pursue an ambitious, post-2030 climate strategy. [[#Cui--2021|Cui et al. (2021)]] identified 18% of old, small, inefficient coal plants for rapid near-term retirement in China to help achieve air quality, health, water, and other societal goals and a feasible coal phase-out under climate goals. Comparable magnitudes of stranded assets may also be created in Latin America when adding all announced, authorised, and procured power plants up to 2060 ( [[#González-Mahecha--2019|González-Mahecha et al. 2019]] ). Options to reduce carbon lock-in include reducing fossil fuels subsidies (Box 6.3), building CCS-ready facilities, or ensuring that facilities are appropriately designed for fuel switching ( [[#Budinis--2018|Budinis et al. 2018]] ). Substantial lock-in may necessitate considerable deployment of CDR to compensate for high cumulative emissions.

Past and present energy sector investments have created technological, institutional, and behavioural path dependencies aligned towards coal, oil, and natural gas ( ''high confidence'' ). In several emerging economies, large projects are planned that address poverty reduction and economic development. Coal infrastructure may be the default choice for these investments without policies to invest in low-carbon infrastructure instead ( [[#Joshua--2020|Joshua and Alola 2020]] ; [[#Steckel--2020|Steckel et al. 2020]] ). Path dependencies frequently have sustainability implications beyond carbon emissions. (Box 6.2 and [[#6.7.7|Section 6.7.7]] ). There are several SDG co-benefits associated with decarbonisation of energy systems ( [[#6.7.7|Section 6.7.7]] ) (Sörgel et al. 2021). For example, coal mining communities frequently experience significant health and economic burdens from resource extraction.

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