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

== Box 4.6: Bottom-up Initiatives: Adaptation Responses Initiated by Individuals and Communities ==

<div id="section-4-4-3-2-block-1"></div>

To effectively adapt to climate change, bottom-up initiatives by individuals and communities are essential, in addition to efforts of governments, organizations, and institutions (Wamsler and Brink, 2014a) <sup>[[#fn:r1178|1178]]</sup> . This box presents examples of bottom-up adaptation responses and behavioural change.

Fiji increasingly faces a lack of freshwater due to decreasing rainfall and rising temperatures (Deo, 2011; IPCC, 2014a) <sup>[[#fn:r1179|1179]]</sup> . While some villages have access to boreholes, these are not sufficient to supply the population with freshwater. Villagers are adapting by rationing water, changing diets, and setting up inter-village sharing networks (Pearce et al., 2017) <sup>[[#fn:r1180|1180]]</sup> . Some villagers take up wage employment to buy food instead of growing it themselves (Pearce et al., 2017) <sup>[[#fn:r1181|1181]]</sup> . In Kiribati, residents adapt to drought by purchasing rainwater tanks and constructing additional wells (Kuruppu and Liverman, 2011) <sup>[[#fn:r1182|1182]]</sup> . An important factor that motivated residents of Kiribati to adapt to drought was the perception that they could effectively adapt to the negative consequences of climate change (Kuruppu and Liverman, 2011) <sup>[[#fn:r1183|1183]]</sup> .

In the Philippines, seismic activity has caused some islands to flood during high tide. While the municipal government offered affected island communities the possibility to relocate to the mainland, residents preferred to stay and implement measures themselves in their local community to reduce flood damage (Laurice Jamero et al., 2017) <sup>[[#fn:r1184|1184]]</sup> . Migration is perceived as undesirable because island communities have strong place-based identities (Mortreux and Barnett, 2009) <sup>[[#fn:r1185|1185]]</sup> . Instead, these island communities have adapted to flooding by constructing stilted houses and raising floors, furniture, and roads to prevent water damage (Laurice Jamero et al., 2017) <sup>[[#fn:r1186|1186]]</sup> . While inundation was in this case caused by seismic activity, this example indicates how island-based communities may respond to rising sea levels caused by climate change.

Adaptation initiatives by individuals may temporarily reduce the impacts of climate change and enable residents to cope with changing environmental circumstances. However, they may not be sufficient to sustain communities’ way of life in the long term. For instance, in Fiji and Kiribati, freshwater and food are projected to become even scarcer in the future, rendering individual adaptations ineffective. Moreover, individuals can sometimes engage in behaviour that may be maladaptive over larger spatio-temporal scales. For example, in the Philippines, many islanders adapt to flooding by elevating their floors using coral stone (Laurice Jamero et al., 2017) <sup>[[#fn:r1187|1187]]</sup> . Over time, this can harm the survivability of their community, as coral reefs are critical for reducing flood vulnerability (Ferrario et al., 2014) <sup>[[#fn:r1188|1188]]</sup> . In Maharashtra, India, on-farm ponds are promoted as rainwater harvesting structures to adapt to dry spells during the monsoon season. However, some individuals fill these ponds with groundwater, leading to depletion of water tables and potentially maladaptive outcomes in the long run (Kale, 2015) <sup>[[#fn:r1189|1189]]</sup> .

Integration of individuals’ adaptation initiatives with top-down adaptation policy is critical (Butler et al., 2015) <sup>[[#fn:r1190|1190]]</sup> , as failing to do so may lead individual actors to mistrust authority and can discourage them from undertaking adequate adaptive actions (Wamsler and Brink, 2014a) <sup>[[#fn:r1191|1191]]</sup> .

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Goal setting can promote mitigation action when goals are not set too low or too high (Loock et al., 2013) <sup>[[#fn:r1192|1192]]</sup> . Commitment strategies where people make a pledge to engage in climate actions can encourage mitigation behaviour (Abrahamse and Steg, 2013; Lokhorst et al., 2013) <sup>[[#fn:r1193|1193]]</sup> , particularly when individuals also indicate how and when they will perform the relevant action and anticipate how to cope with possible barriers (i.e., implementation intentions) (Bamberg, 2000, 2002) <sup>[[#fn:r1194|1194]]</sup> . Such strategies take advantage of individuals’ desire to be consistent (Steg, 2016) <sup>[[#fn:r1195|1195]]</sup> . Similarly, hypocrisy-related strategies that make people aware of inconsistencies between their attitudes and behaviour can encourage mitigation actions (Osbaldiston and Schott, 2012) <sup>[[#fn:r1196|1196]]</sup> .

Actions that reduce climate risks can be rewarded and facilitated, while actions that increase climate risks can be punished and inhibited, and behaviour change can be voluntary (e.g., information provision) or imposed (e.g., by law); voluntary changes that involve rewards are more acceptable than imposed changes that restrict choices (Eriksson et al., 2006, 2008; Steg et al., 2006; Dietz et al., 2007) <sup>[[#fn:r1197|1197]]</sup> . Policies punishing maladaptive behaviour can increase vulnerability when they reinforce socio-economic inequalities that typically produce the maladaptive behaviour in the first place (Adger et al., 2003) <sup>[[#fn:r1198|1198]]</sup> . Change can be initiated by governments at various levels, but also by individuals, communities, profit-making organizations, trade organizations, and other non-governmental actors (Lindenberg and Steg, 2013; Robertson and Barling, 2015; Stern et al., 2016b) <sup>[[#fn:r1199|1199]]</sup> .

Strategies can target intrinsic versus extrinsic motivation. It may be particularly important to enhance intrinsic motivation so that people voluntarily engage in climate action over and again (Steg, 2016) <sup>[[#fn:r1200|1200]]</sup> . Endorsement of mitigation and adaptation actions are positively related (Brügger et al., 2015; Carrico et al., 2015) <sup>[[#fn:r1201|1201]]</sup> ; both are positively related to concern about climate change (Brügger et al., 2015) <sup>[[#fn:r1202|1202]]</sup> . Strategies that target general antecedents that affect a wide range of actions, such as values, identities, worldviews, climate change beliefs, awareness of the climate impacts of one’s actions, and feelings of responsibility to act on climate change, can encourage consistent actions on climate change (van Der Werff and Steg, 2015; Hornsey et al., 2016; Steg, 2016) <sup>[[#fn:r1203|1203]]</sup> . Initial climate actions can lead to further commitment to climate action (Juhl et al., 2017) <sup>[[#fn:r1204|1204]]</sup> , when people learn that such actions are easy and effective (Lauren et al., 2016) <sup>[[#fn:r1205|1205]]</sup> , when they engaged in the initial behaviour for environmental reasons (Peters et al., 2018) <sup>[[#fn:r1206|1206]]</sup> , hold strong pro-environmental values and norms (Thøgersen and Ölander, 2003) <sup>[[#fn:r1207|1207]]</sup> , and when initial actions make them realise they are an environmentally sensitive person, motivating them to act on climate change in subsequent situations so as to be consistent (van der Werff et al., 2014a; Lacasse, 2015, 2016) <sup>[[#fn:r1208|1208]]</sup> . Yet some studies suggest that people may feel licensed not to engage in further mitigation actions when they believe they have already done their part (Truelove et al., 2014) <sup>[[#fn:r1209|1209]]</sup> .

<div id="section-4-4-3-3"></div>

<span id="acceptability-of-policy-and-system-changes"></span>
==== 4.4.3.3 Acceptability of policy and system changes ====

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Public acceptability can shape, enable or prevent policy and system changes. Acceptability reflects the extent to which policy or system changes are evaluated (un)favourably. Acceptability is higher when people expect more positive and less negative effects of policy and system changes (Perlaviciute and Steg, 2014; Demski et al., 2015; Drews and Van den Bergh, 2016) <sup>[[#fn:r1210|1210]]</sup> , including climate impacts (Schuitema et al., 2010b) <sup>[[#fn:r1211|1211]]</sup> . Because of this, policy ‘rewarding’ climate actions is more acceptable than policy ‘punishing’ actions that increase climate risks (Steg et al., 2006; Eriksson et al., 2008) <sup>[[#fn:r1212|1212]]</sup> . Pricing policy is more acceptable when revenues are earmarked for environmental purposes (Steg et al., 2006; Sælen and Kallbekken, 2011) <sup>[[#fn:r1213|1213]]</sup> or redistributed towards those affected (Schuitema and Steg, 2008) <sup>[[#fn:r1214|1214]]</sup> . Acceptability can increase when people experience positive effects after a policy has been implemented (Schuitema et al., 2010a; Eliasson, 2014; Weber, 2015) <sup>[[#fn:r1215|1215]]</sup> ; effective policy trials can thus build public support for climate policy (see Box 4.8).

Climate policy and renewable energy systems are more acceptable when people strongly value other people and the environment, or support egalitarian worldviews, left-wing or green political ideologies (Drews and Van den Bergh, 2016) <sup>[[#fn:r1216|1216]]</sup> , and less acceptable when people strongly endorse self-enhancement values, or support individualistic and hierarchical worldviews (Dietz et al., 2007; Perlaviciute and Steg, 2014; Drews and Van den Bergh, 2016) <sup>[[#fn:r1217|1217]]</sup> . Solar radiation modification is more acceptable when people strongly endorse self-enhancement values, and less acceptable when they strongly value other people and the environment (Visschers et al., 2017) <sup>[[#fn:r1218|1218]]</sup> . Climate policy is more acceptable when people believe climate change is real, when they are concerned about climate change (Hornsey et al., 2016) <sup>[[#fn:r1219|1219]]</sup> , when they think their actions may reduce climate risks, and when they feel responsible to act on climate change (Steg et al., 2005; Eriksson et al., 2006; Jakovcevic and Steg, 2013; Drews and Van den Bergh, 2016; Kim and Shin, 2017) <sup>[[#fn:r1220|1220]]</sup> . Stronger environmental awareness is associated with a preference for governmental regulation and behaviour change rather than free-market and technological solutions (Poortinga et al., 2002) <sup>[[#fn:r1221|1221]]</sup> .

Climate policy is more acceptable when costs and benefits are distributed equally, when nature and future generations are protected (Sjöberg and Drottz-Sjöberg, 2001; Schuitema et al., 2011; Drews and Van den Bergh, 2016) <sup>[[#fn:r1222|1222]]</sup> , and when fair procedures have been followed, including participation by the public (Dietz, 2013; Bernauer et al., 2016a; Bidwell, 2016) <sup>[[#fn:r1223|1223]]</sup> or public society organizations (Bernauer and Gampfer, 2013) <sup>[[#fn:r1224|1224]]</sup> . Providing benefits to compensate affected communities for losses due to policy or systems changes enhanced public acceptability in some cases (Perlaviciute and Steg, 2014) <sup>[[#fn:r1225|1225]]</sup> , although people may disagree on what would be a worthwhile compensation (Aitken, 2010; Cass et al., 2010) <sup>[[#fn:r1226|1226]]</sup> , or feel they are being bribed (Cass et al., 2010; Perlaviciute and Steg, 2014) <sup>[[#fn:r1227|1227]]</sup> .

Public support is higher when individuals trust responsible parties (Perlaviciute and Steg, 2014; Drews and Van den Bergh, 2016) <sup>[[#fn:r1228|1228]]</sup> . Yet, public support for multilateral climate policy is not higher than for unilateral policy (Bernauer and Gampfer, 2015) <sup>[[#fn:r1229|1229]]</sup> ; public support for unilateral, non-reciprocal climate policy is rather strong and robust (Bernauer et al., 2016b) <sup>[[#fn:r1230|1230]]</sup> . Public opposition may result from a culturally valued landscape being affected by adaptation or mitigation options, such as renewable energy development (Warren et al., 2005; Devine-wright and Howes, 2010) <sup>[[#fn:r1231|1231]]</sup> or coastal protection measures (Kimura, 2016) <sup>[[#fn:r1232|1232]]</sup> , particularly when people have formed strong emotional bonds with the place (Devine-Wright, 2009, 2013) <sup>[[#fn:r1233|1233]]</sup> .

Climate actions may reduce human well-being when such actions involve more costs, effort or discomfort. Yet some climate actions enhance well-being, such as technology that improves daily comfort and nature-based solutions for climate adaptation (Wamsler and Brink, 2014b) <sup>[[#fn:r1234|1234]]</sup> . Further, climate action may enhance well-being (Kasser and Sheldon, 2002; Xiao et al., 2011; Schmitt et al., 2018) <sup>[[#fn:r1235|1235]]</sup> because pursuing meaning by acting on climate change can make people feel good (Venhoeven et al., 2013, 2016; Taufik et al., 2015) <sup>[[#fn:r1236|1236]]</sup> , more so than merely pursuing pleasure ''.''

<span id="enabling-technological-innovation"></span>
=== 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> .

<div id="section-4-4-4-1"></div>

<span id="the-nature-of-technological-innovations"></span>
==== 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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<span id="table-4.9"></span>
<!-- START TABLE -->
'''Table 4.9'''

<span id="examples-of-technological-innovations-relevant-to-1.5c-enabled-by-general-purpose-technologies-gpt"></span>
'''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> .

<div id="section-4-4-4-3"></div>

<span id="the-role-of-government-in-1.5c-consistent-climate-technology-policy"></span>
==== 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).

<div id="section-4-4-4-3-block-2" class="box"></div>

<span id="box-4.7-bioethanol-in-brazil-innovation-and-lessons-for-technology-transfer"></span>