Editing IPCC:AR6/WGIII/Chapter-16 (section)

== 16.6 Technological Change and Sustainable Development ==

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This section considers technological innovation in the broader context of sustainable development, recognising that technological change happens within social and economic systems, and therefore technologies are conceived and applied in relation to those systems ( [[#Grübler--1998|Grübler 1998]] ). Simplifications of complex interactions between physical and social systems and incomplete knowledge of the indirect effects of technological innovation may systematically lead to underestimation of environmental impacts and overestimation of our ability to mitigate climate change ( [[#Hertwich--2009|Hertwich and Peters 2009]] ; [[#Arvesen--2011|Arvesen et al. 2011]] ).

Previous sections of the chapter discussed how a systemic approach, appropriate public policies and international cooperation on innovation can enhance technological innovation. This section provides more details on how innovation and technological change, sustainable development and climate change mitigation intertwine.

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=== 16.6.1 Linking Sustainable Development and Technological Change ===

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Sustainable development and technological change are deeply related ( [[#UNCTAD--2019|UNCTAD 2019]] ). Technology has been critical for increasing productivity as the dominant driving force for economic growth. Also, the concentration of technology in few hands has boosted consumption of goods and services which are not necessarily aligned with the Sustainable Development Goals (SDGs) ( [[#Walsh--2020|Walsh et al. 2020]] ). It has been suggested that, in order to address sustainable development challenges, science and technology actors would have to change their relation to policymakers ( [[#Ravetz--1999|Ravetz and Funtowicz 1999]] ) as well as the public ( [[#Jasanoff--2003|Jasanoff 2003]] ). This has been further elaborated for the SDGs. The scale and ambition of the SDGs call for a change in development patterns that require a fundamental shift in: current best practices; guidelines for technological and investment decisions; and the wider socio-institutional systems ( [[#UNCTAD--2019|UNCTAD 2019]] ; [[#Pegels--2020|Pegels and Altenburg 2020]] ). This is needed as not all innovation will lead to sustainable development patterns ( [[#Altenburg--2012|Altenburg and Pegels 2012]] ; [[#Lema--2015|Lema et al. 2015]] ).

Current SDG implementation gaps reflect, to some extent, inadequate understanding of the complex relationships among the goals ( [[#Waiswa--2019|Waiswa et al. 2019]] ; [[#Skene--2020|Skene 2020]] ), as well as their synergies and trade-offs, including how they limit the range of responses available to communities and governments, and potential injustices ( [[#Thornton--2017|Thornton and Comberti 2017]] ). These relationships have been approached by focusing primarily on synergies and trade-offs while lacking the holistic perspective necessary to achieve all the goals ( [[#Nilsson--2016|Nilsson et al. 2016]] ; [[#Roy--2018|Roy et al. 2018]] ).

A more holistic framework could envisage the SDGs as outcomes of stakeholder engagement and learning processes directed at achieving a balance between human development and environmental protection ( [[#Gibbons--1999|Gibbons 1999]] ; [[#Jasanoff--2003|Jasanoff 2003]] ), to the extent that the two can be separated. From a science, technology and innovation perspective, [[#Fu--2019|Fu et al. (2019)]] distinguish three categories of SDGs. The first category comprises those SDGs representing essential human needs for which inputs that put pressure on sustainable development would need to be minimised. These include Zero hunger (SDG 2), Clear water and sanitation (SDG 6) and Affordable and clean energy (SDG 7) resources, which continue to rely on production technologies and practices that are eroding ecosystem services, potentially hampering the realisation of SDGs 15 (Life on land) and 14 (Life below water) ( [[#Díaz--2019|Díaz et al. 2019]] ). The second category includes those related to governance and which compete with each other for scarce resources, such as Industry, innovation and infrastructure (SDG 9) and Climate action (SDG 13), which require an interdisciplinary perspective. The third category are those that require maximum realisation, include No poverty (SDG 1), Quality education (SDG 4) and Gender equality (SDG 5) ( [[#Fu--2019|Fu et al. 2019]] ).

Resolving tensions between the SDGs requires adoption and mainstreaming of novel technologies that can meet needs while reducing resource waste and improving resource-use efficiency, and acknowledging the systemic nature of technological innovation, which involves many levels of actors, stages of innovation and scales ( [[#Anadon--2016b|Anadon et al. 2016b]] ). Changes in production technology have been found effective to overcome trade-offs between food and water goals ( [[#Gao--2017|Gao and Bryan 2017]] ). Innovative technologies at the food, water and energy nexus are transforming production processes in industrialised and developing countries, such as developments in agrivoltaics, which is co-development of land for agriculture and solar with water conservation benefits ( [[#Barron-Gafford--2019|Barron-Gafford et al. 2019]] ; [[#Lytle--2020|Lytle et al. 2020]] ; [[#Schindele--2020|Schindele et al. 2020]] ), and other renewably powered low- to zero-carbon food, water and energy systems ( [[#He--2019|He et al. 2019]] ). [[#Silvestre--2019|Silvestre and Ţîrcă (2019)]] indicate that maximising both social and environmental aims is not possible, but that sustainable innovations include satisfactory solutions for social, environmental and economic pillars (Figure 16.4).

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'''Figure 16.4 | Considerations and typology of innovations for sustainable development.''' Source: [[#Silvestre--2019|Silvestre and Ţîrcă (2019)]] .

There is evidence that technological changes can catalyse implementation of the reforms needed to the manner in which goods and services are distributed among people ( [[#Fu--2019|Fu et al. 2019]] ). A recently developed theoretical framework based on a capability approach (CA) has been used to evaluate the quality of human life and the process of development ( [[#Haenssgen--2018|Haenssgen and Ariana 2018]] ). Variations of the CA have been applied to exploratory studies of the link between technological change, human development, and economic growth ( [[#Mayer--2001|Mayer 2001]] ; [[#Mormina--2019|Mormina 2019]] ). This suggests that the transformative potential of technology as an enabling condition is not intrinsic, but is assigned to it by people within a given technological context. A failure to recognise and account for this property of technology is a root cause of many failed attempts at techno-fixing sustainable development projects ( [[#Stilgoe--2013|Stilgoe et al. 2013]] ; [[#Fazey--2020|Fazey et al. 2020]] ).

The basic rationale for governance of technological change is the creation and maintenance of an enabling environment for climate and SDG-oriented technological change ( [[#Avelino--2019|Avelino et al. 2019]] ). Such an environment poses high demands on governance and policy to coordinate with actors and provide a direction for innovation and technological change. Cross-Chapter Box 12 illustrates how the dynamics of socio-technical transitions and shifting development pathways towards sustainable development offer options for policymakers and other actors to accelerate the system transitions needed for both climate change mitigation and sustainable development. Governance interventions to implement the SDGs will need to be operationalised at sub-national, national and global levels and support integration of resource concerns in policy, planning and implementation ( [[#UNEP--2015|UNEP 2015]] ; [[#Williams--2020|Williams et al. 2020]] ).

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=== Cross-Chapter Box 12 | Transition Dynamics ===

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'''Authors:''' Anthony Patt (Switzerland), Heleen de Coninck (the Netherlands), Xuemei Bai (Australia), Paolo Bertoldi (Italy), Sarah Burch (Canada), Clara Caiafa (Brazil/the Netherlands), Felix Creutzig (Germany), Renée van Diemen (the Netherlands/United Kingdom), Frank Geels (United Kingdom/the Netherlands), Michael Grubb (United Kingdom), María Josefina Figueroa Meza (Venezuela/Denmark), Şiir Kilkiş (Turkey), Jonathan Köhler (Germany), Catherine Mitchell (United Kingdom), Lars J. Nilsson (Sweden), Patricia Perkins (Canada), Yamina Saheb (France/Algeria), Harald Winkler (South Africa)

'''Introduction'''

Numerous studies suggest that transformational changes would be required in many areas of society if climate change is to be limited to 2°C warming or less. Many of these involve shifts to low-carbon technologies, such as renewable energy, which typically involve changes in associated regulatory and social systems; others more explicitly concern behavioural shifts, such as towards plant-based diets or cleaner cooking fuels, or, at the broadest level, a shift in development pathways. [[IPCC:Wg3:Chapter:Chapter-1|Chapter 1]] establishes an analytic framework focusing on transitions, which chapters 5, 13, 14, 15 and 16 further develop. In this Cross-Chapter Box, we provide a complementary overview of the dynamics of different kinds of transformational changes for climate mitigation and sustainable development. We first focus on insights from socio-technical transitions approaches, and then expand to broader system transitions.

'''Dynamics of socio-techni''' '''cal transitions'''

A large volume of literature documents the processes associated with transformational changes in technology and the social systems associated with their production and use ( [[#Geels--2019|Geels 2019]] ; [[#Köhler--2019|Köhler et al. 2019]] ). Transformational technological change typically goes hand in hand with shifts in knowledge, behaviour, institutions, and markets ( [[#Geels--2010|Geels and Schot 2010]] ; [[#Markard--2012|Markard et al. 2012]] ); stickiness in these factors often keeps society ‘locked in’ to those technologies already in widespread use, rather than allowing a shift to new ones – even those that offer benefits ( [[#David--1985|David 1985]] ; [[#Arthur--1994|Arthur 1994]] ). Exceptions often follow consistent patterns ( [[#Geels--2002|Geels 2002]] ; [[#Unruh--2002|Unruh 2002]] ); since AR5 a growing number of scholars have suggested using these insights to design more effective climate policies and actions ( [[#Geels--2017|Geels et al. 2017]] ). [[IPCC:Wg3:Chapter:Chapter-1|Chapter 1]] ( [[IPCC:Wg3:Chapter:Chapter-1#1.7|Section 1.7]] and Figure 1.6) represents technology diffusion and a corresponding shift in policy emphasis as a continuous process; it is also useful to identify a sequence of distinct stages that typically occur, associating each stage with a distinct set of processes, challenges, and effective policies ( [[#Patt--2018|Patt and Lilliestam 2018]] ; [[#Victor--2019|Victor et al. 2019]] ). Consistent with elsewhere in this report ( [[IPCC:Wg3:Chapter:Chapter-5#5.5.2|Section 5.5.2]] and Supplementary Material 5.5.3 in Chapter 5, and [[#16.3|Section 16.3]] in Chapter 16), Cross-Chapter Box 12 Figure 1 elaborates on four distinct stages: it portrays these as occurring in a cycle, recognising that even transformative technologies will eventually be replaced with newer ones.

The ''emergence'' stage is marked by experimentation, innovation in the laboratory, and demonstration in the field, to produce technologies and system architectures ( [[#Geels--2005|Geels 2005]] ). By its very nature, experimentation includes both successes and failures, and implies high risks. Because of these risks, especially in the case of fundamentally new technologies, government funding for research, development and demonstration (RD&amp;D) projects is crucial to sustaining development ( [[#Mazzucato--2015b|Mazzucato 2015b]] ).

The second stage is ''early adoption'' , during which successful technologies jump from the laboratory to limited commercial application ( [[#Pearson--2012|Pearson and Foxon 2012]] ). Reaching this stage is often described as crossing the ‘Valley of Death’, because the cost/performance ratio for these new market entrants is too low for them to appear viable to investors ( [[#Murphy--2003|Murphy and Edwards 2003]] ). A key process in the early adoption phase is induced innovation, a result of incremental improvements in both design and production processes, and of mass-production of a growing share of key components ( [[#Nemet--2006|Nemet 2006]] ; [[#Grubb--2021|Grubb et al. 2021]] ). There is diversity across classes of technologies, and learning tends to occur faster for technologies that are modular ( [[#Wilson--2020|Wilson et al. 2020]] ) – such as photovoltaics – and slower for those that require site- or context-specific engineering, such as in the shift to low-carbon materials production ( [[#Malhotra--2020|Malhotra and Schmidt 2020]] ). Public policies that create a secure return on investment for project developers can lead to learning associated with industry expansion (Chapter 16, Figure 16.1); typically these are economically and politically viable when they promote growth within a market niche, causing little disruption to the mainstream market ( [[#Roberts--2018|Roberts et al. 2018]] ). Direct support mechanisms are effective, including cross-subsidies (such as feed-in tariffs) and market quotas (such as renewable portfolio standards) ( [[#Geels--2017|Geels et al. 2017]] b; [[#Patt--2018|Patt and Lilliestam 2018]] ; and [[IPCC:Wg3:Chapter:Chapter-9|Chapter 9]] for assessment of early adoption policies in the building sector). The value of these policies is less in their immediate emissions reductions, but more in generating the conditions for self-sustaining transformational change to take place as technologies later move from niche to mainstream ( [[#Hanna--2021|Hanna and Victor 2021]] ).

The third stage, ''diffusion'' , is where niche technologies become mainstream, with accelerating diffusion rates (Sections 1.7 and 16.4), and is marked by changes to the socio-technical ‘regime’, including infrastructure networks, value chains, user practices, and institutions. This stage is often the most visible and turbulent, because more widespread adoption of a new technology gives rise to structural changes in institutions and actors’ behaviour (e.g., increased adoption of smartphones to new payment systems and social media), and because when incumbent market actors become threatened, they often contest policies promoting the new technologies ( [[#Köhler--2019|Köhler et al. 2019]] ). In the diffusion stage, policy emphasis is shifted from financial support during the early adoption stage, towards supporting regime-level factors needed to sustain, or cope with, rapid and widespread diffusion ( [[#Markard--2018|Markard 2018]] ). These factors and policies are context specific. For example, [[#Patt--2019|Patt et al. (2019)]] document that the policies needed to expand residential charging networks for electric vehicles depend on the local structure of the housing market.

The fourth stage is ''stabilisation'' , in which the new technologies, systems, and behaviours are both standardised and insulated from rebound effects and backsliding ( [[#Andersen--2020|Andersen and Gulbrandsen 2020]] ). Sectoral bans on further investment in high-carbon technologies may become politically feasible at this point ( [[#Breetz--2018|Breetz et al. 2018]] ; [[#Economidou--2020|Economidou et al. 2020]] ). The decline of previously dominant products or industries can lead to calls for policymakers to help those negatively affected, enabling a just transition ( [[#McCauley--2018|McCauley and Heffron 2018]] ; [[#Newell--2020|Newell and Simms 2020]] ). Political opposition to the system reconfiguration that comes with integration and stabilisation can also be overcome by offering incumbent actors an attractive exit strategy ( [[#de%20Gooyert--2016|de Gooyert et al. 2016]] ).

Because different sectors are at different stages of low-carbon transitions, and because the barriers that policies need to address are stage- and often context-specific, effective policies stimulating socio-technical transitions operate primarily at the sectoral level ( [[#Victor--2019|Victor et al. 2019]] ). This is particularly the case during early adoption, where economic barriers predominate; during diffusion, policies that address regime-level factors often need to deal with cross-sectoral linkages and coupling, such as those between power generation, transportation, and heating ( [[#Patt--2015|Patt 2015]] ; [[#Bloess--2019|Bloess 2019]] ; [[#Fridgen--2020|Fridgen et al. 2020]] ). The entire cycle can take multiple decades. However, later stages can go faster by building on the earlier stages that have taken place elsewhere. For example, early RD&amp;D into wind energy took place primarily in Denmark, was followed by early adoption in Denmark, Germany, and Spain, before other countries, including the USA, India, and China, leapfrogged directly to the diffusion stage ( [[#Chaudhary--2015|Chaudhary et al. 2015]] ; [[#Dai--2015|Dai and Xue 2015]] ; [[#Lacal-Arántegui--2019|Lacal-Arántegui 2019]] ). A similar pattern played out for solar power ( [[#Nemet--2019|Nemet 2019]] ). International cooperation, geared towards technology transfer, capacity and institution-building, and finance, can help ensure that developing countries leapfrog to low-carbon technologies that have undergone commercialisation elsewhere ( [[#Adenle--2015|Adenle et al. 2015]] ; [[#Fankhauser--2018|Fankhauser and Jotzo 2018]] ) (see also Chapter 5, Box 5.9, Chapter 15, [[IPCC:Wg3:Chapter:Chapter-15#15.5|Section 15.5]] , and [[#16.5|Section 16.5]] in this chapter).

This report contains numerous examples of the positive feedbacks in the centre of Cross-Chapter Box 12, Figure 1, predominantly arising during the early adoption and diffusion stages, and leading to rapid or unexpected acceleration of change. For example, public acceptance of meat alternatives leads to firms improving the products, increasing political and economic feedbacks ( [[IPCC:Wg3:Chapter:Chapter-5#5.4|Section 5.4]] and Box 5.5). Declining costs in solar and wind cause new investment in the power-generation sector being dominated by those technologies, leading to increased political support and further cost reductions (Chapter 6). In buildings (Chapter 9) and personal mobility (Chapter 10), low-carbon heating systems and electric vehicles are gaining public acceptance, leading to improved infrastructure and human resources, more employment in those sectors, and behavioural contagion. Some have argued that technologies cross societal tipping points on account of these feedbacks ( [[#Obama--2017|Obama 2017]] ; [[#Sharpe--2021|Sharpe and Lenton 2021]] ).

'''Dynamics between enabling conditions for syst''' '''em transitions'''

[[#Abson--2017|Abson et al. (2017)]] argue that it is possible to make use of ‘leverage points’ inherent in system dynamics in order to accelerate sustainability transitions. [[#Otto--2020|Otto et al. (2020)]] argue that interventions geared towards the social factors driving change can ‘activate contagious processes’ leading to the transformative changes required for climate mitigation. These self-reinforcing dynamics involve the interaction of enabling conditions, including public policy and governance, institutional and technological innovation capacity, behaviour change, and finance. For example, [[#Mercure--2018|Mercure et al. (2018)]] simulated financial flows into fossil-fuel extraction, and showed how investors taking into account transition risk in combination with technological innovation would lead to the enhancement of investments in low-carbon assets and further enhanced innovation. As another example, behaviour, lifestyle, and policy can also initiate demand-side transitions ( [[#Tziva--2020|Tziva et al. 2020]] ) (Chapter 5), such as with food systems ( [[#Rust--2020|Rust et al. 2020]] ) ( [[IPCC:Wg3:Chapter:Chapter-7#7.4.5|Section 7.4.5]] ), and can contribute to both resilience and carbon storage ( [[#Sendzimir--2011|Sendzimir et al. 2011]] ) (Box 16.5).

In the urban context, the concept of sustainability experiments has been used to examine innovative policies and practices adopted by cities that have significant impact on transition towards low-carbon and sustainable futures ( [[#Bai--2010|Bai et al. 2010]] ; [[#Castán%20Broto--2013|Castán Broto and Bulkeley 2013]] ). Individual innovative practices can potentially be upscaled to achieve low-carbon transition in cities ( [[#Peng--2018|Peng and Bai 2018]] ), leading to a process of broadening and scaling innovative practices in other cities ( [[#Peng--2019|Peng et al. 2019]] ). Such sustainability experiments give rise to new actor networks, which in some cases may accelerate change, and in others may lead to conflict ( [[#Bulkeley--2014|Bulkeley et al. 2014]] ). As in the diffusion phase in Cross-Chapter Box 12, Figure 1, contextual factors play a strong role. Examining historical transitions to cycling across European cities, [[#Oldenziel--2016|Oldenziel et al. (2016)]] found that contextual factors, including specific configurations of actors, can lead to very different outcomes. [[#Kraus--2021|Kraus and Koch (2021)]] found a short-term social shock – such as the COVID-19 crisis – to lead to differential increases in cycling behaviour, contingent on other enabling conditions.

'''Linking system dynamics to development pathways and broader''' '''societal goals'''

Transition dynamics insights can be broadened to shifting development pathways. Development paths are characterised by particular sets of interlinking regime rules and behaviours, including inertia and cascading effects over time, and are reinforced at multiple levels, with varied capacities and constraints on local agency occurring at each level ( [[#Burch--2014|Burch et al. 2014]] ) (Cross-Chapter Box 5 in Chapter 4). This is also observed by [[#Schot--2018|Schot and Kanger (2018)]] , who identify a needed change in a ‘meta-regime’, crossing sectoral lines in linking value chains or infrastructure and overall development objectives. In the context of the UN climate change regime, international cooperation can bring together such best practices and lessons learnt ( [[#Adenle--2015|Adenle et al. 2015]] ; [[#Pandey--2021|Pandey et al. 2021]] ). This is especially relevant for developing countries, which often depend on technologies and financial resources from abroad, witnessing their pace and direction influenced by transnational actors ( [[#Marquardt--2016|Marquardt et al. 2016]] ; [[#Bhamidipati--2019|Bhamidipati et al. 2019]] ), and benefitting little in terms of participating in high value-added activities ( [[#Whittaker--2020|Whittaker et al. 2020]] ).

System transitions differ according to context, such as across industrialised and developing countries ( [[#Ramos-Mejía--2018|Ramos-Mejía et al. 2018]] ), and within countries. Lower levels of social capital and trust negatively impact niche commercialisation ( [[#Lepoutre--2018|Lepoutre and Oguntoye 2018]] ). In contexts of poverty and inequality, stakeholders’ – including users’ – capabilities for meaningful participation are limited, and transition outcomes can end up marginalising or further excluding social groups ( [[#Osongo--2017|Osongo and Schot 2017]] ; [[#Hansen--2018|Hansen et al. 2018]] ). Many studies of transitions in developing countries make note of the importance of innovation in the informal sector ( [[#Charmes--2016|Charmes 2016]] ) (Box 5.10 in Chapter 5). Facilitating informal sector access to renewable energy sources, safe and sustainable buildings, and finance can advance low-carbon transitions ( [[#McCauley--2019|McCauley et al. 2019]] ; [[#Masuku--2021|Masuku and Nzewi 2021]] ). On the contrary, disregarding its importance can result in misleading or ineffective innovation and climate strategies ( [[#Maharajh--2010|Maharajh and Kraemer-Mbula 2010]] ; Mazhar and Ummad 2014; [[#de%20Beer--2016|de Beer et al. 2016]] ; [[#Masuku--2021|Masuku and Nzewi 2021]] ).

Policies shifting innovation in climate-compatible directions can also reinforce other development benefits, for instance better health, increased energy access, poverty alleviation and economic competitiveness ( [[#Deng--2018|Deng et al. 2018]] ; [[#IPCC--2018a|IPCC 2018a]] ; [[#Karlsson--2020|Karlsson et al. 2020]] ). Development benefits, in turn, can create feedback effects that sustain public support for subsequent policies, and hence help to secure effective long-term climate mitigation ( [[#Geels--2014|Geels 2014]] ; [[#Meckling--2015|Meckling et al. 2015]] ; [[#Schmidt--2017|Schmidt and Sewerin 2017]] ; [[#Breetz--2018|Breetz et al. 2018]] ), increasing legitimacy of environmental sustainability actions ( [[#Hansen--2018|Hansen et al. 2018]] ; [[#Herslund--2018|Herslund et al. 2018]] ; [[#van%20Welie--2018|van Welie and Romijn 2018]] ) and addressing negative socio-economic impacts ( [[#Deng--2018|Deng et al. 2018]] ; [[#McCauley--2018|McCauley and Heffron 2018]] ; [[#Eisenberg--2019|Eisenberg 2019]] ; [[#Henry--2020|Henry et al. 2020]] ).

'''Summary and ga''' '''ps in knowledge'''

Strategies to accelerate climate mitigation can be most effective at accelerating and achieving transformative change when they are synchronised with transition processes in systems. They address technological stage characteristics, take advantage of high-leverage intervention points, and respond to societal dynamics ( [[#Abson--2017|Abson et al. 2017]] ; [[#Geels--2017|Geels et al. 2017]] ; [[#Köhler--2019|Köhler et al. 2019]] ). Gaps in knowledge remain on how to tailor policy mixes, the interaction of enabling conditions, the generalisability of socio-technical transition insights to other types of systems, and how to harness these insights to better shift development pathways.

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=== 16.6.2 Sustainable Development and Technological Innovation: Synergies, Trade-offs and Governance ===

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==== 16.6.2.1 Synergies and Trade-offs ====

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Policies that shift innovation in climate compatible directions can promote other development benefits, for instance, better health, increased energy access, poverty alleviation and economic competitiveness ( [[#Deng--2018|Deng et al. 2018]] ) (Cross-Chapter Box 12). Economic competitiveness co-benefits can emerge as climate mitigation policies trigger innovation that can be leveraged for promoting industrial development, job creation and economic growth, both in terms of localising low-emission energy technologies value chains as well as increased energy efficiency and avoided carbon lock-ins ( [[#16.4|Section 16.4]] ). However, without adequate capabilities, co-benefits at the local level would be minimal, and they would probably materialise far from where activities take place ( [[#Ockwell--2016|Ockwell and Byrne 2016]] ; [[#Vasconcellos--2021|Vasconcellos and Caiado Couto 2021]] ). Innovation and technological change can also empower citizens. Grass-roots innovation promotes the participation of grass-roots actors, such as social movements and networks of academics, activists and practitioners, and facilitate experimenting with alternative forms of knowledge creation ( [[#Seyfang--2007|Seyfang and Smith 2007]] ; [[#UNCTAD--2019|UNCTAD 2019]] ). Examples of ordinary people and entrepreneurs adopting and adapting technologies to local needs to address locally defined needs have been documented in the development literature ( [[#van%20Welie--2018|van Welie and Romijn 2018]] ) (Box 16.10). Digital technologies can empower citizens and communities in decentralised energy systems, contributing not only to a more sustainable but also to a more democratic and fairer energy system ( [[#Van%20Summeren--2021|Van Summeren et al. 2021]] ) ( [[IPCC:Wg3:Chapter:Chapter-5#5.4|Section 5.4]] in Chapter 5, and Cross-Chapter Box 11 in this chapter).

Therefore, even though science, technology and innovation is an explicit focus of SDG 9, it is an enabler of most SDGs ( [[#UNCTAD--2019|UNCTAD 2019]] ). Striving for synergies between innovation and technological change for climate change mitigation with other SDGs can help to secure effective long-term climate mitigation, as development benefits can create feedback effects that sustain public and political support for subsequent climate mitigation policies ( [[#Geels--2014|Geels 2014]] ; [[#Meckling--2015|Meckling et al. 2015]] ; Cross-Chapter Box 12 in this chapter). However, innovation is not always geared to sustainable development – for instance, firms tend to know how to innovate when value chains are left intact ( [[#Hall--2005|Hall and Martin 2005]] ), which is usually not the case in systemic transitions.

A comprehensive study of these effects distinguishes among ‘… anticipated-intended, anticipated-unintended, and unanticipated-unintended consequences’ ( [[#Tonn--2019|Tonn and Stiefel 2019]] ). Theoretical and empirical studies have demonstrated that unintended consequences are typical of complex adaptive systems, and while a few are predictable, a much larger number are not ( [[#Sadras--2020|Sadras 2020]] ). Even when unintended consequences are unanticipated, they can be prevented through actor responses, for instance, rebound effects following the introduction of energy-efficient technologies. Other examples of unintended consequences include worse-than-expected physical damage to infrastructure and resistance from communities in the rapidly growing ocean renewable energy sector ( [[#Quirapas--2020|Quirapas and Taeihagh 2020]] ), and gaps between expected and actual performance of building-integrated photovoltaic (BIPV) technology ( [[#Boyd--2018|Boyd and Schweber 2018]] ; [[#Gram-Hanssen--2018|Gram-Hanssen and Georg 2018]] ). In the agricultural sector, new technologies and associated practices that target the fitness of crop pests have been found to favour resistant variants. Unintended consequences of digitalisation are reported as well ( [[#Lynch--2019|Lynch et al. 2019]] ) (Cross-Chapter Box 11 in this chapter).

Innovation and climate mitigation policies can also have negative socio-economic impacts, and not all countries, actors and regions around the world benefit equally from rapid technological change ( [[#Deng--2018|Deng et al. 2018]] ; [[#McCauley--2018|McCauley and Heffron 2018]] ; [[#Eisenberg--2019|Eisenberg 2019]] ; [[#UNCTAD--2019|UNCTAD 2019]] ; [[#Henry--2020|Henry et al. 2020]] ). In fact, socio-technical transitions often create winners and losers ( [[#Roberts--2018|Roberts et al. 2018]] ). Technological change can reinforce existing divides between women and men, rural and urban populations, and rich and poor communities: older workers displaced by technological change will not qualify for jobs if they were unable to acquire new skills; weak educational systems may not prepare young people for emerging employment opportunities; and disadvantaged social groups, including women in many countries, often have fewer opportunities for formal education ( [[#McCauley--2018|McCauley and Heffron 2018]] ; [[#UNCTAD--2019|UNCTAD 2019]] ). That is a risk regarding technological change for climate change mitigation, as emerging evidence suggests that the energy transition can create jobs and productivity opportunities in the renewable energy sector, but will also lead to job losses in fossil fuel and exposed sectors ( [[#Le%20Treut--2021|Le Treut et al. 2021]] ). At the same time, these new jobs may use more intensively high-level cognitive and interpersonal skills compared to regular, traditional jobs, requiring higher levels of human capital dimensions such as formal education, work experience and on-the-job training ( [[#Consoli--2016|Consoli et al. 2016]] ). Despite the empowerment potentials of decentralised energy systems, not all societal groups are equally positioned to benefit from energy community policies, with issues of energy justice taking place within initiatives, between initiatives and related actors, as well as beyond initiatives ( [[#Calzadilla--2018|Calzadilla and Mauger 2018]] ; [[#van%20Bommel--2021|van Bommel and Höffken 2021]] ).

The opportunities and challenges of technological change can also differ within country regions and between countries ( [[#Garcia-Casals--2019|Garcia-Casals et al. 2019]] ). Within countries, [[#Vasconcellos--2021|Vasconcellos and Caiado Couto (2021)]] show that, in the absence of policies and capacity-building activities which promote local recruiting, a significant part of total benefits of wind projects, especially high-income jobs and high value-added activities, is captured by already higher-income regions. Between countries, developing countries usually have lower innovation capabilities, which means they need to import low-emission technology from abroad and are also less able to adapt these technologies to local conditions and create new markets and business models. This can lead to external dependencies and limit opportunities to leverage economic benefits from technology transfer ( [[#16.5.1|Section 16.5.1]] ).

This means that, in countries below the technological frontier, the contribution of technological change to climate change mitigation can happen primarily through the adoption and less through the development of new technologies, which can reduce potential economic and welfare benefits from rapid technological change ( [[#UNCTAD--2019|UNCTAD 2019]] ). The adoption of consumer information and communication technology (ICT) ( [[#Baller--2016|Baller et al. 2016]] ) or renewable energy technology ( [[#Lema--2021|Lema et al. 2021]] ) cannot bring least-developed economies close to the technological frontier without appropriate technological capabilities in other sectors, and an enabling innovation system ( [[#Ockwell--2012|Ockwell and Mallett 2012]] ; [[#Sagar--2014|Sagar and Majumdar 2014]] ; [[#Ockwell--2018|Ockwell et al. 2018]] ; [[#UNCTAD--2019|UNCTAD 2019]] ; [[#Malhotra--2021|Malhotra et al. 2021]] ; [[#Vasconcellos--2021|Vasconcellos and Caiado Couto 2021]] ). It has been argued widely that both hard and soft infrastructure, as well as appropriate policy frameworks and capability building, would facilitate developing countries’ engagement in long-term technological innovation and sustainable industrial development, and eventually in achieving the SDGs ( [[#Ockwell--2016|Ockwell and Byrne 2016]] ; [[#Altenburg--2017|Altenburg and Rodrik 2017]] ; [[#UNCTAD--2019|UNCTAD 2019]] ).

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==== 16.6.2.2 Challenges to Governing Innovation for Sustainable Development ====

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Dominant economic systems and centralised governance structures continue to reproduce unsustainable patterns of production and consumption, reinforcing many economic and governance structures from local through national and global scales ( [[#Johnstone--2018|Johnstone and Newell 2018]] ). Technological change, as an inherently complex process ( [[#Funtowicz--2020|Funtowicz 2020]] ), poses governance challenges ( [[#Bukkens--2020|Bukkens et al. 2020]] ) requiring social innovation ( [[#Repo--2019|Repo and Matschoss 2019]] ) ( [[IPCC:Wg3:Chapter:Chapter-5#5.6|Section 5.6]] and Chapter 13).

Prospects for effectively governing SDG-oriented technological transformations require, at a minimum, balanced views and new tools for securing the scientific legitimacy and credibility to connect public policy and technological change in society ( [[#Jasanoff--2018|Jasanoff 2018]] ; [[#Sadras--2020|Sadras 2020]] ). Many frameworks of governance have been proposed, such as reflexive governance ( [[#Voss--2006|Voss et al. 2006]] ), polycentric governance ( [[#Ostrom--2010|Ostrom 2010]] ), collaborative governance ( [[#Bodin--2017|Bodin 2017]] ), adaptive governance ( [[#Munene--2018|Munene et al. 2018]] ) and transformative governance ( [[#Rijke--2013|Rijke et al. 2013]] ; [[#Westley--2013|Westley et al. 2013]] ) (Chapters 13 and 14).

A particular class of barriers to the development and adoption of new technologies comprises entrenched power relations dominated by vested interests that control and benefit from existing technologies ( [[#Chaffin--2016|Chaffin et al. 2016]] ; [[#Dorband--2020|Dorband et al. 2020]] ). Such interests can generate balancing feedbacks within multilevel social-technological regimes that are related to technological lock-in, including allocations of investment between fossil and renewable energy technologies ( [[#Unruh--2002|Unruh 2002]] ; [[#Sagar--2009|Sagar et al. 2009]] ; [[#Seto--2016|Seto et al. 2016]] ).

Weaker coordination and implementation capacity in some developing countries can undermine the ability to avoid trade-offs with other development objectives – such as reinforced inequalities or excessive indebtedness and increased external dependency – and can limit the potential of leveraging economic benefits from technologies transferred from abroad ( [[#16.5|Section 16.5]] and Cross-Chapter Box 12 in this chapter). Van Welie and Romijn (2018) show that, in a low-income setting, the exclusion of some local stakeholders from the decision-making process may undermine sustainability transitions efforts. Countries with high levels of inequality can be more prone to elite capture, non-transparent political decision-making processes, relations based on clientelism and patronage, and no independent judiciary ( [[#Jasanoff--2018|Jasanoff 2018]] ), although in particular contexts, non-elites manage to exert influence ( [[#Moldalieva--2020|Moldalieva and Heathershaw 2020]] ). The dominance of incumbents, however, implies that sustainable technological transitions could be achieved without yielding any social and democratic benefits ( [[#Hansen--2018|Hansen et al. 2018]] ). In the cultural domain, a recurrent policy challenge that has been observed in most countries is the limited public support for development and deployment of low-carbon technologies ( [[#Bernauer--2016|Bernauer and McGrath 2016]] ). The conventional approach to mobilising such support has been to portray technological change as a means of minimising climate change. Empirical studies show that simply reframing climate policy is highly unlikely to build and sustain public support ( [[#Bernauer--2016|Bernauer and McGrath 2016]] ).

Finally, there is a link between social and technological innovation; any innovation is grounded in complex socio-economic arrangements, to which governance arrangements would need to respond (Sections 5.5 and 5.6, Chapter 13, and Cross-Chapter Box 12 in this chapter). Social innovation can contribute to maximising synergies and minimising trade-offs in relation to technological and other innovative practices, but for this to materialise, national, regional and local circumstances need to be taken into account and, if needed, changed. Even in circumstances of high capabilities, the extent that social innovation might help to promote synergies and avoid trade-offs is not easy to evaluate ( [[#Grimm--2013|Grimm et al. 2013]] ).

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=== 16.6.3 Actions that Maximise Synergies and Minimise Trade-offs Between Innovation and Sustainable Development ===

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Technological innovation may bring significant synergy in pursuing SDGs, but it may also create challenges to the economy, human well-being, and the environment ( [[#Schillo--2017|Schillo and Robinson 2017]] ; [[#Thacker--2019|Thacker et al. 2019]] ; [[#Walsh--2020|Walsh et al. 2020]] ). The degree of potential synergies and trade-offs among SDGs differs from country to country and over time ( [[#16.6.1|Section 16.6.1]] .1). These potentials will depend on available resources, geographical conditions, development stage and policy measures. Even though synergies and trade-offs related to technological innovation have received the least attention from researchers ( [[#Deng--2018|Deng et al. 2018]] ), literature show that higher synergy was found where countries’ policies take into account the linkages between sectors ( [[#Mainali--2018|Mainali et al. 2018]] ). For technology innovation to be effective in enhancing synergies and reducing trade-offs, its role and nature in production and consumption patterns, as well as in value chains and in the wider economy, requires clarification. Technology ownership and control together with its current orientation and focus towards productivity, needs to be revised if a meaningful contribution to the implementation of the SDGs is to be achieved in a transformative way ( [[#Walsh--2020|Walsh et al. 2020]] ). Responsible innovation, combining anticipation, reflexivity, inclusion and responsiveness, has been suggested as a framework for conducting innovation ( [[#Stilgoe--2013|Stilgoe et al. 2013]] ). Also inclusive innovation ( [[#Hoffecker--2021|Hoffecker 2021]] ) could make sure that unheard voices and interests are included in decision-making, and that methods for this have been implemented in practice ( [[#Douthwaite--2017|Douthwaite and Hoffecker 2017]] ).

There are several examples of how to maximise synergies and avoid or minimise trade-offs when bringing technological innovation to the ground. When implementing off-grid solar energy in Rwanda, synergies were found between 80 of the 169 SDG targets, demonstrating how mainstreaming off-grid policies and prioritising investment in the off-grid sector can realise human development and well-being, build physical and social infrastructures, and achieve sustainable management of environmental resources ( [[#Bisaga--2021|Bisaga et al. 2021]] ). Another example is related to wind power in Northeast of Brazil where the creation of direct and indirect jobs has been demonstrated in areas where capabilities are high, as well as associated improvements in wholesale and retail trade and real estate activities, though this also emphasises the need for capacity development along with international collaboration projects ( [[#Vasconcellos--2021|Vasconcellos and Caiado Couto 2021]] ). Other examples include studies raising awareness on solar energy and women’s empowerment ( [[#Winther--2018|Winther et al. 2018]] ) and recycling and waste ( [[#Cross--2018|Cross and Murray 2018]] ).

Other actions with the potential to maximise synergies are those related to community or grassroots technological innovation. The importance of the link between technological innovation and community action and its contribution to sustainable development is usually underestimated. Further research is needed on this and, most importantly, its inclusion in the political agenda on sustainable development ( [[#Seyfang--2007|Seyfang and Smith 2007]] ). On the other hand, when technological innovation occurs far from where is implemented and participation in the production, and hence training activities of local actors is minimal, co-benefits and synergies among SDGs are limited and usually far below expectations ( [[#Bhamidipati--2021|Bhamidipati and Hansen 2021]] ; [[#Vasconcellos--2021|Vasconcellos and Caiado Couto 2021]] ). Actions by policymakers that safeguard environmental and social aspects can boost synergies and maximise those co-benefits ( [[#Lema--2021|Lema et al. 2021]] ). Given that technological change impacts countries, regions and social groups differently, transition policies can be designed to ensure that all regions and communities are able to take advantage of the energy and other transitions ( [[#McCauley--2018|McCauley and Heffron 2018]] ; [[#Henry--2020|Henry et al. 2020]] ).

Box 16.10 provides insights on how a systemic approach to technological innovation can contribute to reconcile synergies and trade-offs to achieve sustainable development and mitigation goals.

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=== Box 16.10 | Agroecological Approaches: The Role of Local and Indigenous Knowledge and Innovation ===

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Major improvements in agricultural productivity have been recorded over recent decades ( [[#FAO--2018a|FAO 2018a]] ). However, progress has also come with social and environmental costs, high levels of greenhouse gas (GHG) emissions, and rising demand for natural resources (UNEP 2013; UNEP 2017; [[#FAO--2018a|FAO 2018a]] ; [[#Bringezu--2019|Bringezu 2019]] ; [[#Díaz--2019|Díaz et al. 2019]] ).

Trend analysis indicates that a large share of the global demand for land is projected to be supplied by South America, in particular the Amazon ( [[#Lambin--2011|Lambin and Meyfroidt 2011]] ; [[#TEEB--2018|TEEB 2018]] ) and Gran Chaco forests ( [[#Grau--2015|Grau et al. 2015]] ). In developing countries, land use change for satisfying international meat demand is leading to deforestation. In Brazil, the amount of GHGs emitted by the beef cattle sector alone represents 65% of the agricultural sector’s emissions and 15% of the country’s overall emissions ( [[#May--2019|May 2019]] ).

Agricultural and food systems are complex and diverse; they include traditional food systems, mixed food systems and modern food systems ( [[#Pengue--2018|Pengue et al. 2018]] ). Multiple forms of visible and invisible flows of natural resources exist in global food systems ( [[#Pascual--2017|Pascual et al. 2017]] ; [[#TEEB--2018|TEEB 2018]] ; [[#IPBES--2019|IPBES 2019]] ).

Technological practices, management and changes in the food chain could help adapt to climate change, reduce emissions and absorb carbon in soil, thus contributing to carbon dioxide removal (IPCC, 2018, 2019). A range of technologies can be implemented – from highly technological options, such as transgenic crops resistant to drought ( [[#González--2019|González et al. 2019]] ), salt or pesticides ( [[#OECD--2011b|OECD 2011b]] ; [[#Kim--2020|Kim and Kwak 2020]] ) or smart and 4.0 agriculture ( [[#Klerkx--2019|Klerkx et al. 2019]] ), to more frugal, low-cost technologies such as agroecological approaches adapted to local circumstances ( [[#Francis--2003|Francis et al. 2003]] ; [[#FAO--2018b|FAO 2018b]] ). These agroecological approaches are the subject of this box.

For developing countries, agroecological approaches could tackle climate change challenges and food security (WGII-report, Chapter 5, Box 5.10). Small Island Developing States (SIDS) support livelihoods to develop local food value chains that can promote sustainable management of natural resources, preserve biodiversity and help build resilience to climate change impacts and natural disasters ( [[#FAO--2019|FAO 2019]] ). Other advantages of agroecological practices include their adaptation to different social, economic and ecological environments ( [[#Altieri--2017|Altieri and Nicholls 2017]] ), the fact that they are physical and financial capital-extensive, and are well-integrated with the social and cultural capital of rural territories and local resources (knowledge, natural resources, etc.), without leading to technological dependencies ( [[#Côte--2019|Côte et al. 2019]] ).

Agroecology is a dynamic concept that has gained prominence in scientific, agricultural and political discourses in recent years ( [[#Wezel--2020|Wezel et al. 2020]] ; [[#Anderson--2021|Anderson et al. 2021]] ) (Chapter 7, Chapter 5, WGII Box 5.10). Three of the different agroecological approaches are briefly discussed here: agroecological intensification; agroforestry; and biochar use in rice paddy fields.

Agricultural intensification provides ways to use land, water and energy resources to ensure adequate food supply while also addressing concerns about climate change and biodiversity ( [[#Cassman--2020|Cassman and Grassini 2020]] ). The term ecological intensification ( [[#Tittonell--2014|Tittonell 2014]] ) focuses on biological and ecological processes and functions in agroecosystems. In line with the development of the concept of agroecology, agroecological intensification integrates social and cultural perspectives ( [[#Wezel--2015|Wezel et al. 2015]] ). Agroecological intensification ( [[#Mockshell--2019|Mockshell and Villarino 2019]] ) for sub-Saharan Africa aims to address employment and food security challenges ( [[#Pretty--2011|Pretty et al. 2011]] ; [[#Altieri--2015|Altieri et al. 2015]] ).

Another example of an agroecological approach is agroforestry. Agroforestry provides examples of positive agroecological feedbacks, such as ‘the regreening of the Sahel’ in Niger. The practice is based on the assisted natural regeneration of trees in cultivated fields, an old method that was slowly dying out, but which innovative public policies (the transfer of property rights over trees from the state to farmers) helped restore ( [[#Sendzimir--2011|Sendzimir et al. 2011]] ).

Rice paddy fields are a major source of methane. Climate change impacts and adaptation strategies can affect rice production and rice farmers’ net income. Biochar use in rice paddy fields has been advocated as a potential strategy to reduce GHG emissions from soils, enhance soil carbon stocks and nitrogen retention, and improve soil function and crop productivity ( [[#Mohammadi--2020|Mohammadi et al. 2020]] ).

The contributions of indigenous people ( [[#Díaz--2019|Díaz et al. 2019]] ), heritage agriculture ( [[#Koohafkan--2010|Koohafkan and Altieri 2010]] ) and peasants’ agroecological knowledge ( [[#Holt-Giménez--2002|Holt-Giménez 2002]] ) to technological innovation offer a wide array of options for management of land, soils, biodiversity and enhanced food security without depending on modern, foreign agricultural technologies ( [[#Denevan--1995|Denevan 1995]] ). In farming agriculture and food systems, innovation and technology based on nature could help to reduce climate change impacts ( [[#Griscom--2017|Griscom et al. 2017]] ). Evidence suggests that there are benefits to integrating tradition with new technologies in order to design new approaches to farming, and that these are greatest when they are tailored to local circumstances ( [[#Nicholls--2018|Nicholls and Altieri 2018]] ).

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=== 16.6.4 Climate Change, Sustainable Development and Innovation ===

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This section gives a synthesis of this chapter on innovation and technology development and transfer, connecting it to sustainable development.

In conjunction with other enabling conditions, technological innovation can support system transitions to limit warming, help shift development pathways, and bring about new and improved ways of delivering goods and services that are essential to human well-being ( ''high confidence'' ). At the same time, however, innovation can result in trade-offs that undermine progress on mitigation and towards other SDGs. Trade-offs include negative externalities, such as environmental impacts and social inequalities, rebound effects leading to lower net emission reductions or even increases in emissions, and increased dependency on foreign knowledge and providers ( ''high confidence'' ). Digitalisation, for example, holds both opportunity for emission reduction and emission-saving behaviour change, but at the same time causes significant environmental, social and greenhouse gas (GHG) impacts ( ''hig'' ''h confidence'' ).

A systemic view of innovation that takes into account the roles of actors, institutions, and their interactions, can contribute to enhanced understanding of processes and outcomes of technological innovation, and to interventions and arrangements that can help innovation. It can also play a role in clarifying the synergies and trade-offs between technological innovation and the SDGs. Effective governance and policy, implemented in an inclusive, responsible and holistic way, could make innovation policy more effective, and avoid and minimise misalignments between climate change mitigation, technological innovation, and other societal goals ( ''medium evidence'' , ''hi'' ''gh agreement'' ).

A special feature is the dynamics of transitions. Like other enabling conditions, technological innovation plays a balancing role – by inhibiting change as innovation strengthens incumbent technologies and practices – and a reinforcing role, by allowing new technologies and practices to disrupt the existing socio-technical regimes ( ''high confidence'' ). Appropriate innovation policies can help to better organise innovation systems, while other policies (technology push and demand pull) can provide suitable resources and incentives to support and guide these innovation systems towards societally-desirable outcomes, ensure the innovations are deployed at scale, and direct these dynamics towards system transitions for climate change mitigation, and also towards addressing other SDGs. This means taking into account the full lifecycle or value chain as well as analysis of synergies and trade-offs.

Against this backdrop, international cooperation on technological innovation is one of the enablers of climate action in developing countries on both mitigation and adaptation ( ''high confidence'' ). Experiences with international cooperation on technology development and deployment suggest that such activities are most effective when they: are approached as ‘innovation cooperation’ that engenders a holistic, systemic view of innovation requirements; are an equitable partnership between donors and recipients; and develop local innovation capabilities ( ''medium evidence'' , ''h'' ''igh agreement'' ).

Chapter 17, in particular [[IPCC:Wg3:Chapter:Chapter-17#17.4|Section 17.4]] , connects technological innovation with other enabling conditions, such as behaviour, institutional capacity and multilevel governance, to clarify the actions that could be taken, holistically and in conjunction, to strengthen and accelerate the system transitions required to limit warming to be in line with the Paris Agreement and to place countries in sustainable development pathways.

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