Editing IPCC:AR6/WGIII/Chapter-5 (section)

=== 5.4.5 Technological and Infrastructural Drivers ===

<div id="h2-23-siblings" class="h2-siblings"></div>

Technologies and infrastructures shape social practices and their design matters for effective mitigation measures ( ''high evidence'' , ''high agreement'' ). There are systemic interconnections between infrastructures and practices ( [[#Cass--2018|Cass et al. 2018]] ; [[#Haberl--2021|Haberl et al. 2021]] ), and their intersection explains their relevance ( [[#Thacker--2019|Thacker et al. 2019]] ). The design of a new electricity system to meet new emerging demand based on intermittent renewable sources can lead to a change in consumption habits and the adaption of lifestyles compliant with more power supply interruption ( [[#Maïzi--2017|Maïzi et al. 2017]] ; [[#Maïzi--2019|Maïzi and Mazauric 2019]] ). The quality of the service delivery impacts directly the potential user uptake of low-carbon technologies among rural households. In the state of Himachal Pradesh in India, a shift from LPG to electricity among rural households, with induction stoves, has been successful due to the availability of stable and continuous electricity, which has been difficult to achieve in any other Indian state ( [[#Banerjee--2016|Banerjee et al. 2016]] ). In contrast, in South Africa, people who were using electricity earlier are now adopting LPG to diversify the energy source for cooking due to high electricity tariffs and frequent blackouts ( [[#Kimemia--2016|Kimemia and Annegarn 2016]] ) (Box 5.5 and [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-5 Chapter 5] Supplementary Material I).

From a welfare point of view, infrastructure investments are not constrained by revealed or stated preferences ( ''high evidence, high agreement'' ). Preferences change with social and physical environment, and infrastructure interventions can be justified by objective measures, such as public health and climate change mitigation, not only given preferences ( ''high agreement'' , ''high evidence'' ). Specifically, there is a case for more investment in low-carbon transport infrastructure than assumed in environmental economics as it induces low-carbon preferences ( [[#Creutzig--2016a|Creutzig et al. 2016a]] ; [[#Mattauch--2016|Mattauch et al. 2016]] ; [[#Mattauch--2018|Mattauch et al. 2018]] ). Changes in infrastructure provision for active travel may contribute to uptake of more walking and cycling ( [[#Frank--2019|Frank et al. 2019]] ). These effects contribute to higher uptake of low-carbon travel options, albeit the magnitude of effects depends on design choices and context ( [[#Goodman--2013|Goodman et al. 2013]] ; [[#Goodman--2014|Goodman et al. 2014]] ; [[#Song--2017|Song et al. 2017]] ; [[#Javaid--2020|Javaid et al. 2020]] ; [[#Abraham--2021|Abraham et al. 2021]] ). Infrastructure is thus not only required to make low-carbon travel possible but can also be a pre-condition for the formation of low-carbon mobility preferences (see case study in Box 5.8).

The dynamic interaction of habits and infrastructures also predict CO 2 -intensive choices. When people move from a city with good public transport to a car-dependent city, they are more likely to own fewer vehicles due to learned preferences for lower levels of car ownership ( [[#Weinberger--2010|Weinberger and Goetzke 2010]] ). When individuals moving to a new city with extensive public transport were given targeted material about public transport options, the modal share of public transport increased significantly ( [[#Bamberg--2003|Bamberg et al. 2003]] ). Similarly, an exogenous change to route choice in public transport makes commuters change their habitual routes ( [[#Larcom--2017|Larcom et al. 2017]] ).

'''Table 5.4 | Main features, insights, and policyimplications of five drivers of decision and action.''' Entries in each column are independent lists, not intended to line up with each other.

{| class="wikitable"
|-
| '''Driver'''

| '''How does driver contribute to status quo bias?'''

| '''What needs to change?'''

| '''Driver’s policy implications'''

| '''Examples'''

|-
| '''Behavioural'''

| – Habits and routines formed under different circumstances do not get updated

– Present bias penalises upfront costs and discourages energy efficiency investments

– Loss aversion magnifies the costs of change

– When climate change is seen as distant, it is not feared

– Nuclear power and accident potential score high on psychological dread

| – New goals (sustainable lifestyle)

– New capabilities (online real-time communication)

– New resources (increased education)

– Use of full range of incentives and mechanisms to change demand-side behaviour

| – Policies need to be context specific and coordinate economic, legal, social, and infrastructural tools and nudges

– Relate climate action to salient local risks and issues

| – India’s new LPG scale up policy uses insights about multiple behavioural drivers of adoption and use

– Rooftop solar adoption expanded in Germany, when feed-in tariffs removed risk from upfront-cost recovery

– Nuclear power policies in Germany post Fukushima affected by emotional factors

|-
| '''Socio-cultural'''

| – Cultural norms (e.g., status, comfort, convenience) support existing behaviour

– Lack of social trust reduces willingness to shift behaviour (e.g., adopt car sharing)

– Fear of social disapproval decreases willingness to adopt new behaviours

– Lack of opportunities to participate in policy create reactance against ‘top-down’ imposition

– Unclear or dystopian narratives of climate response reduce willingness to change and to accept new policies and technologies

| – Create positive meanings and norms around low-emission service delivery (e.g., mass transit)

– Community initiatives to build social trust and engagement, capacity building, and social capital formation

– Climate movements that call out the insufficient, highly problematic state of delayed climate action

– Public participation in policymaking and technology implementation that increases trust, builds capacity and increases social acceptance

– Positive narratives about possible futures that avoid emissions (e.g., emphasis upon health and slow/active travel)

| – Embed policies in supportive social norms

– Support collective action on climate mitigation to create social trust and inclusion

– Involve arts and humanities to create narratives for policy process

| – Communicate descriptive norms to electricity end users

– Community energy initiative

– REScoop

– Fridays For Future

|-
| '''Business and corporate'''

| – Lock-in mechanisms that make incumbent firms reluctant to change: core capabilities, sunk investments in staff and factories, stranded assets

| – New companies (like car-sharing companies, renewable energy start-ups) that pioneer new business models or energy service provisions

| – Influence consumer behaviour via product innovation

– Provide capital for clean energy innovation

| – Electrification of transport opens up new markets for more than a hundred million new vehicles

|-
| '''Institutional'''

| – Lock-in mechanisms related to power struggles, lobbying, political economy

| – New policy instruments, policy discussions, policy platforms, implementation agencies, including capacity

| – Feed-in tariffs and other regulations that turn energy consumers into prosumers

| – Mobility case study, India’s LPG policy sequence

|-
| '''Infrastructural'''

| – Various lock-in mechanisms such as sunk investments, capabilities, embedding in routines/lifestyles

| – Many emerging technologies, which are initially often more expensive, but may benefit from learning curves and scale economies that drive costs down

| – Systemic governance to avoid rebound effects

| – Urban walking and bike paths

– Stable and continuous electricity supply fostering induction stoves

|}

<div id="5.5" class="h1-container"></div>

<span id="an-integrative-view-on-transitioning"></span>