Editing IPCC:AR6/SR15/Chapter-4 (section)

=== 4.4.4 Enabling Technological Innovation ===

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This section focuses on the role of technological innovation in limiting warming to 1.5°C, and how innovation can contribute to strengthening implementation to move towards or to adapt to 1.5°C worlds. This assessment builds on information of technological innovation and related policy debates in and after AR5 (Somanathan et al., 2014) <sup>[[#fn:r1237|1237]]</sup> .

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==== 4.4.4.1 The nature of technological innovations ====

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Technological systems have their own dynamics. New technologies have been described as emerging as part of a ‘socio-technical system’ that is integrated with social structures and that itself evolves over time (Geels and Schot, 2007) <sup>[[#fn:r1238|1238]]</sup> . This progress is cumulative and accelerating (Kauffman, 2002; Arthur, 2009) <sup>[[#fn:r1239|1239]]</sup> . To illustrate such a process of co-evolution: the progress of computer simulation enables us to better understand climate, agriculture, and material sciences, contributing to upgrading food production and quality, microscale manufacturing techniques, and leading to much faster computing technologies, resulting, for instance, in better performing photovoltaic (PV) cells.

A variety of technological developments have and will contribute to 1.5°C-consistent climate action or the lack of it. They can do this, for example, in the form of applications such as smart lighting systems, more efficient drilling techniques that make fossil fuels cheaper, or precision agriculture. As discussed in Section 4.3.1, costs of PV (IEA, 2017f) <sup>[[#fn:r1240|1240]]</sup> and batteries (Nykvist and Nilsson, 2015) <sup>[[#fn:r1241|1241]]</sup> have sharply dropped. In addition, costs of fuel cells (Iguma and Kidoshi, 2015; Wei et al., 2017) <sup>[[#fn:r1242|1242]]</sup> and shale gas and oil (Wang et al., 2014; Mills, 2015) <sup>[[#fn:r1243|1243]]</sup> have come down as a consequence of innovation.

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<span id="technologies-as-enablers-of-climate-action"></span>
==== 4.4.4.2 Technologies as enablers of climate action ====

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Since AR5, literature has emerged as to how much future GHG emission reductions can be enabled by the rapid progress of general purpose technologies (GPTs), consisting of information and communication technologies (ICT), including artificial intelligence (AI) and the internet of things (IoT), nanotechnologies, biotechnologies, robotics, and so forth (WEF, 2015; OECD, 2017c) <sup>[[#fn:r1244|1244]]</sup> . Although these may contribute to limiting warming to 1.5°C, the potential environmental, social and economic impacts of new technologies are uncertain.

Rapid improvement of performance and cost reduction is observed for many GPTs. They include AI, sensors, internet, memory storage and microelectromechanical systems. The latter GPTs are not usually categorized as climate technologies, but they can impact GHG emissions.

Progress of GPT could help reduce GHG emissions more cost-effectively. Examples are shown in Table 4.9. It may however, result in more emissions by increasing the volume of economic activities, with unintended negative consequence on sustainable development. While ICT increases electricity consumption (Aebischer and Hilty, 2015) <sup>[[#fn:r1245|1245]]</sup> , the energy consumption of ICT is usually dwarfed by the energy saving by ICT (Koomey et al., 2013; Malmodin et al., 2014) <sup>[[#fn:r1246|1246]]</sup> , but rebound effects and other sustainable development impacts may be significant. An appropriate policy framework that accommodates such impacts and their uncertainties could address the potential negative impacts by GPT (Jasanoff, 2007) <sup>[[#fn:r1247|1247]]</sup> .

GHG emission reduction potentials in relation to GPTs were estimated for passenger cars using a combination of three emerging technologies: electric vehicles, car sharing, and self-driving. GHG emission reduction potential is reported, assuming generation of electricity with low GHG emissions (Greenblatt and Saxena, 2015; ITF, 2015; Viegas et al., 2016; Fulton et al., 2017) <sup>[[#fn:r1248|1248]]</sup> . It is also possible that GHG emissions increase due to an incentive to car use. Appropriate policies such as urban planning and efficiency regulations could contain such rebound effects (Wadud et al., 2016) <sup>[[#fn:r1249|1249]]</sup> .

Estimating emission reductions by GPT is difficult due to substantial uncertainties, including projections of future technological performance, costs, penetration rates, and induced human activity. Even if a technology is available, the establishment of business models might not be feasible (Linder and Williander, 2017) <sup>[[#fn:r1250|1250]]</sup> . Indeed, studies show a wide range of estimates, ranging from deep emission reductions to possible increases in emissions due to the rebound effect (Larson and Zhao, 2017) <sup>[[#fn:r1251|1251]]</sup> .

GPT could also enable climate adaptation, in particular through more effective climate disaster risk management and improved weather forecasting.

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'''Table 4.9'''

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'''Examples of technological innovations relevant to 1.5°C enabled by general purpose technologies (GPT)'''

Note: lists of enabling GPT or adaptation/mitigation options are not exhaustive, and the GPTs by themselves do not reduce emissions or increase climate change resilience.

<!-- TABLE -->
{| class="wikitable"
|-
! Sector
! Examples of Mitigation/Adaptation Technological Innovation
! Enabling GPT
|-
! rowspan="2"| Buildings
| Energy and CO <sub>2</sub> efficiency of logistics, warehouse and shops (GeSI, 2015; IEA, 2017a)
| IoT, AI
|-
| Smart lighting and air conditioning (IEA, 2016b, 2017a)
| IoT, AI
|-
! rowspan="3"| Industry
| Energy efficiency improvement by industrial process optimization (IEA, 2017a)
| Robots, IoT
|-
| Bio-based plastic production by biorefinery (OECD, 2017c)
| Biotechnology
|-
| New materials from biorefineries (Fornell et al., 2013; McKay et al., 2016)
| ICT, biotechnology
|-
! rowspan="6"| Transport
| Electric vehicles, car sharing, automation (Greenblatt and Saxena, 2015; Fulton et al., 2017)
| Biotechnology
|-
| Bio-based diesel fuel by biorefinery (OECD, 2017c)
| ICT, biotechnology
|-
| Second generation bioethanol potentially coupled to carbon capture systems (De Souza et al., 2014; Rochedo et al., 2016)
| Biotechnology
|-
| Logistical optimization, and electrification of trucks by overhead line (IEA, 2017e)
| ICT, biotechnology
|-
| Reduction of transport needs by remote education, health and other services (GeSI, 2015; IEA, 2017a)
| Biotechnology
|-
| Energy saving by lightweight aircraft components (Beyer, 2014; Faludi et al., 2015; Verhoef et al., 2018)
| Additive manufacturing (3D printing)
|-
! rowspan="3"| Electricity
| Solar PV manufacturing (Nemet, 2014)
| Nanotechnology
|-
| Smart grids and grid flexibility to accommodate intermittent renewables (Heard et al., 2017)
| IoT, AI
|-
| Plasma confinement for nuclear fusion (Baltz et al., 2017)
| AI
|-
! rowspan="4"| Agriculture
| Precision agriculture (improvement of energy and resource efficiency including reduction of fertilizer use and N2O emissions)<br />
(Pierpaoli et al., 2013; Brown et al., 2016; Schimmelpfennig and Ebel, 2016)
| Biotechnology ICT, AI
|-
| Methane inhibitors (and methane-suppressing vaccines) that reduce livestock emissions from enteric fermentation (Wedlock et al., 2013; Hristov et al., 2015; Wollenberg et al., 2016)
| Biotechnology
|-
| Engineering C3 into C4 photosynthesis to improve agricultural production and productivity (Schuler et al., 2016)
| Biotechnology
|-
| Genome editing using CRISPR to improve/adapt crops to a changing climate (Gao, 2018)
| Biotechnology
|-
! rowspan="3"| Disaster Reduction and Adaptation
| Weather forecasting and early warning systems, in combination with user knowledge (Hewitt et al., 2012; Lourenço et al., 2016)
| ICT
|-
| Climate risk reduction (Upadhyay and Bijalwan, 2015)
| ICT
|-
| Rapid assessment of disaster damage (Kryvasheyeu et al., 2016)
| ICT
|}
<!-- END TABLE -->

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Government policy usually plays a role in promoting or limiting GPTs, or science and technology in general. It has impacts on climate action, because the performance of further climate technologies will partly depend on the progress of GPTs. Governments have established institutions for achieving many social, and sometimes conflicting goals, including economic growth and addressing climate change (OECD, 2017c) <sup>[[#fn:r1273|1273]]</sup> , which include investment in basic research and development (R&amp;D) that can help develop game-changing technologies (Shayegh et al., 2017) <sup>[[#fn:r1274|1274]]</sup> . Governments are also needed to create an enabling environment for the growth of scientific and technological ecosystems necessary for GPT development (Tassey, 2014) <sup>[[#fn:r1275|1275]]</sup> .

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==== 4.4.4.3 The role of government in 1.5°C-consistent climate technology policy ====

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While literature on 1.5°C-specific innovation policy is absent, a growing body of literature indicates that governments aim to achieve social, economic and environmental goals by promoting science and a broad range of technologies through ‘mission-driven’ innovation policies, based on differentiated national priorities (Edler and Fagerberg, 2017) <sup>[[#fn:r1276|1276]]</sup> . Governments can play a role in advancing climate technology via a ‘technology push’ policy on the technology supply side (e.g., R&amp;D subsidies), and by ‘demand pull’ policy on the demand side (e.g., energy-efficiency regulation), and these policies can be complemented by enabling environments (Somanathan et al., 2014) <sup>[[#fn:r1277|1277]]</sup> . Governments may also play a role in removing existent support for incumbents (Kivimaa and Kern, 2016) <sup>[[#fn:r1278|1278]]</sup> . A growing literature indicates that policy mixes, rather than single policy instruments, are more effective in addressing climate innovation challenges ranging from technologies in the R&amp;D phase to those ready for diffusion (Veugelers, 2012; Quitzow, 2015; Rogge et al., 2017; Rosenow et al., 2017) <sup>[[#fn:r1279|1279]]</sup> . Such innovation policies can help address two kinds of externalities: environmental externalities and proprietary problems (GEA, 2012; IPCC, 2014b; Mazzucato and Semieniuk, 2017) <sup>[[#fn:r1280|1280]]</sup> . To avoid ‘picking winners’, governments often maintain a broad portfolio of technological options (Kverndokk and Rosendahl, 2007) <sup>[[#fn:r1281|1281]]</sup> and work in close collaboration with the industrial sector and society in general. Some governments have achieved relative success in supporting innovation policies (Grubler et al., 2012; Mazzucato, 2013) <sup>[[#fn:r1282|1282]]</sup> that addressed climate-related R&amp;D (see Box 4.7 on bioethanol in Brazil).

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