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

=== Box 5.6 | Socio-behavioural Aspects of Deploying Cookstoves ===

<div id="h2-17-siblings" class="h2-siblings"></div>

Universal access to clean and modern cooking energy could cut premature deaths from household air pollution by two-thirds, while reducing forest degradation and deforestation and contributinh to the reduction of up to 50% of CO 2 emissions from cooking (relative to baseline by 2030) ( [[#IEA--2017c|IEA 2017c]] ; [[#Dagnachew--2019|Dagnachew et al. 2019]] ). However, in the absence of policy reform and substantial energy investments, 2.3 billion people will have no access to clean cooking fuels such as biogas, LPG, natural gas or electricity in 2030 ( [[#IEA--2017c|IEA 2017c]] ). Studies reveal that a combination of drivers influence adoption of new cookstove appliances, including affordability, behavioural and cultural aspects (lifestyles, social norms around cooking and dietary practices), information provision, availability, aesthetic qualities of the technology, perceived health benefits, and infrastructure (spatial design of households and cooking areas). The increasing efficiency improvements in electric cooking technologies could enable households to shift to electrical cooking at mass scale. The use of pressure cookers and rice cookers is now widespread in South Asia and beginning to penetrate the African market as consumer attitudes are changing towards household appliances with higher energy efficiencies ( [[#Batchelor--2019|Batchelor et al. 2019]] ). There are shifts towards electric and LPG stoves in Bhutan ( [[#Dendup--2019|Dendup and Arimura 2019]] ), India ( [[#Pattanayak--2019|Pattanayak et al. 2019]] ), Ecuador ( [[#Martínez--2017|Martínez et al. 2017]] ; [[#Gould--2018|Gould et al. 2018]] ) and Ethiopia ( [[#Tesfamichael--2021|Tesfamichael et al. 2021]] ); and improved biomass stoves in China ( [[#Smith--1993|Smith et al. 1993]] ). Significant subsidy, information ( [[#Dendup--2019|Dendup and Arimura 2019]] ), social marketing and availability of technology in the local markets are some of the key policy instruments helping to adopt improved cookstoves ( [[#Pattanayak--2019|Pattanayak et al. 2019]] ). There is no one-size-fits-all solution to household air pollution – different levels of shift and improvement occur in different cultural contexts, indicating the importance of socio-cultural and behavioural aspects in shifts in cooking practices. See more in [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-5 Chapter 5] Supplementary Material I, Section 5.SM.6.2.

There is ''high agreement'' in the literature that the updating of educational systems from a commercialised, individualised, entrepreneurial training model to an education cognisant of planetary health and human well-being can accelerate climate change awareness and action ( [[#Mendoza--2014|Mendoza and Roa 2014]] ; [[#Dombrowski--2016|Dombrowski et al. 2016]] ) (Supplementary Material I Chapter 5).

There is ''high evidence'' and ''high agreement'' that people’s core values affect climate-related decisions and climate policy support by shaping beliefs and identities ( [[#Dietz--2014|Dietz 2014]] ; [[#Steg--2016|Steg 2016]] ; [[#Hayward--2019|Hayward and Roy 2019]] ). People with altruistic and biospheric values are more likely to act on climate change and support climate policies than those with hedonic or egoistic values ( [[#Taylor--2014|Taylor et al. 2014]] ), because these values are associated with higher awareness and concern about climate change, stronger belief that personal actions can help mitigate climate change, and stronger feelings of responsibility for taking climate action ( [[#Dietz--2014|Dietz 2014]] ; [[#Steg--2016|Steg 2016]] ). Research also suggest that egalitarian, individualistic, and hierarchical worldviews ( [[#Wildavsky--1990|Wildavsky and Dake 1990]] ) have their role, and that successful solutions require policy-makers of all three worldviews to come together and communicate with each other ( [[#Chuang--2020|Chuang et al. 2020]] ).

Core values also influence which costs and benefits are considered ( [[#Hahnel--2015|Hahnel et al. 2015]] ; [[#Gölz--2016|Gölz and Hahnel 2016]] ; [[#Steg--2016|Steg 2016]] ). Information provision and appeals are thus more effective when tailored to those values ( [[#Bolderdijk--2013|Bolderdijk et al. 2013]] ; [[#Boomsma--2014|Boomsma and Steg 2014]] ), as implemented by the energy cultures framework ( [[#Stephenson--2015|Stephenson et al. 2015]] ; [[#Klaniecki--2020|Klaniecki et al. 2020]] ). Awareness, personal norms, and perceived behavioural control predict willingness to change energy-related behaviour above and beyond traditional socio-demographic and economic predictors ( [[#Schwartz--1977|Schwartz 1977]] ; [[#Ajzen--1985|Ajzen 1985]] ; [[#Stern--2000|Stern 2000]] ), as do perceptions of self-efficacy ( [[#Bostrom--2019|Bostrom et al. 2019]] ). However, such motivation for change is often not enough, as actors also need capacity for change and help to overcome individual, institutional and market barriers ( [[#Young--2010|Young et al. 2010]] ; [[#Bray--2011|Bray et al. 2011]] ; [[#Carrington--2014|Carrington et al. 2014]] ).

Table 5.4 describes common obstacles to demand-side energy behaviour change, from loss aversion to present bias (for more detail see [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-5 Chapter 5] Supplementary Material I). Choice architecture refers to interventions (‘nudges’) that shape the choice context and how choices are presented, with seemingly-irrelevant details (e.g., option order or labels) often more important than option price ( [[#Thaler--2009|Thaler and Sunstein 2009]] ). There is ''high evidence'' and ''high agreement'' that choice architecture nudges shape energy decisions by capturing deciders’ attention; engaging their desire to contribute to the social good; facilitating accurate assessment of risks, costs, and benefits; and making complex information more accessible ( [[#Yoeli--2017|Yoeli et al. 2017]] ; Zangheri et al. 2019). Climate-friendly choice architecture includes the setting of proper defaults, the salient positioning of green options (in stores and online), forms of framing, and communication of social norms ( [[#Johnson--2012|Johnson et al. 2012]] ). Simplifying access to greener options (and hence lowering effort) can promote ASI changes ( [[#Mani--2013|Mani et al. 2013]] ). Setting effective ‘green’ defaults may be the most effective policy to mainstream low-carbon energy choices ( [[#Sunstein--2014|Sunstein and Reisch 2014]] ), adopted in many contexts ( [[#Jachimowicz--2019|Jachimowicz et al. 2019]] ) and deemed acceptable in many countries ( [[#Sunstein--2019|Sunstein et al. 2019]] ). Table 5.3a lists how often different choice-architecture tools were used in many countries over the past 10 years to change ASI behaviours, and how often each tool was used to enhance an economic incentive. These tools have been tested mostly in developed countries. Reduction in energy use (typically electricity consumption) is the most widely studied behaviour (because metering is easily observable). All but one tool was applied to increase this ‘Avoid’ behaviour, with demand-side reductions from 0% to up to 20%, with most values below 3% (see also meta-analyses by [[#Hummel--2019|Hummel and Maedche (2019)]] ; [[#Nisa--2019|Nisa et al. (2019)]] ; [[#van%20der%20Linden--2020|van der Linden and Goldberg (2020)]] ; [[#Stankuniene--2020|Stankuniene et al. (2020)]] ; and [[#Khanna--2021|Khanna et al. (2021)]] . Behavioural, economic, and legal instruments are most effective when applied as an internally consistent ensemble where they can reinforce each other, a concept referred to as ‘policy packaging’ in transport policy research ( [[#Givoni--2014|Givoni 2014]] ). A meta-analysis, combining evidence of psychological and economic studies, demonstrates that feedback, monetary incentives and social comparison operate synergistically and are together more effective than the sum of individual interventions ( [[#Khanna--2021|Khanna et al. 2021]] ). The same meta-analysis also shows that combined with monetary incentives, nudges and choice architecture can reduce global GHG emissions from household energy use by 5–6% ( [[#Khanna--2021|Khanna et al. 2021]] ).

Choice architecture has been depicted as an anti-democratic attempt at manipulating the behaviour of actors without their awareness or approval ( [[#Gumbert--2019|Gumbert 2019]] ). Such critiques ignore the fact that there is no neutral way to present energy-use-related decisions, as every presentation format and choice environment influences choice, whether intentionally or not. Educating households and policy makers about the effectiveness of choice architecture and adding these behavioural tools to existing market- and regulation-based tools in a transparent and consultative way can provide desired outcomes with increased effectiveness, while avoiding charges of manipulation or deception. People consent to choice-architecture tools if their use is welfare-enhancing, policymakers are transparent about their goals and processes, public deliberation and participation are encouraged, and the choice architect is trusted ( [[#Sunstein--2019|Sunstein et al. 2019]] ).

'''Table 5.3a | Inventory of behavioural interventions experimentally tested to change energy behaviours.'''

{| class="wikitable"
|-
! '''Behavioural tool'''

! '''# of papers'''

! '''# in developed''' '''countries'''

! '''# in other countries'''

! '''Energy demand behaviour'''

! '''Avoid'''

! '''Shift'''

! '''Improve'''

! '''Economic incentive'''

|-
| '''Set the proper defaults'''

| 27

| 26

| 1

| '''Carbon Offset Programme (3)''' [[#Löfgren--2012|Löfgren et al. (2012)]] ; [[#Araña--2013|Araña and León (2013)]]

'''Energy Source (4)''' [[#Kaiser--2020|Kaiser et al. (2020)]] ; [[#Wolske--2020|Wolske et al. (2020)]] *

'''Energy Use (16)''' [[#Jachimowicz--2019|Jachimowicz et al. (2019)]] ; [[#Nisa--2019|Nisa et al. (2019)]] ; [[#Grilli--2021|Grilli and Curtis (2021)]] *

'''Investment in Energy Efficiency (7)''' [[#Theotokis--2015|Theotokis and Manganari (2015)]] ; [[#Ohler--2020|Ohler et al. (2020)]]

'''Mode of Transportation (1)''' [[#Goodman--2013|Goodman et al. (2013)]]

| 11

| 12

| 9

| 6

|-
| '''Reach out during transitions'''

| 10

| 9

| 1

| '''Energy Use (4)''' [[#Verplanken--2006|Verplanken (2006)]] ; [[#Jack--2016|Jack and Smith (2016)]] ; [[#Iweka--2019|Iweka et al. (2019)]] *

'''Investment in Energy Efficiency (4)''' [[#Gimpel--2020|Gimpel et al. (2020)]]

'''Mode of Transportation (2)''' [[#Verplanken--2008|Verplanken et al. (2008)]]

| 1

| 3

| 7

| 1

|-
| '''Provide timely feedback and reminders'''

| 256

| 246

| 10

| '''Energy Use (252)''' [[#Darby--2006|Darby (2006)]] ; [[#Buckley--2019|Buckley (2019)]] * [[#Abrahamse--2005|Abrahamse et al. (2005)]] ; [[#Fischer--2008|Fischer (2008)]] ; [[#Steg--2008|Steg (2008)]] ; [[#Faruqui--2010|Faruqui et al. (2010)]] ; [[#Delmas--2013|Delmas et al. (2013)]] ; [[#McKerracher--2013|McKerracher and Torriti (2013)]] ; [[#Karlin--2015|Karlin et al. (2015)]] ; [[#Andor--2018|Andor and Fels (2018)]] ; [[#Bergquist--2019|Bergquist et al. (2019)]] ; [[#Iweka--2019|Iweka et al. (2019)]] ; [[#Nisa--2019|Nisa et al. (2019)]] ; Zangheri et al. (2019); [[#Ahir--2021|Ahir and Chakraborty (2021)]] ; [[#Grilli--2021|Grilli and Curtis (2021)]] ; [[#Khanna--2021|Khanna et al. (2021)]] *

'''Mode of Transportation (3)''' [[#Steg--2008|Steg (2008)]] ; [[#Sanguinetti--2020|Sanguinetti et al. (2020)]] *

| 244

| 6

| 7

| 33

|-
| '''Make information intuitive and easy to access'''

| 247

| 235

| 12

| '''Energy Source (3)''' [[#Havas--2015|Havas et al. (2015)]] ; [[#Jagger--2019|Jagger et al. (2019)]]

'''Energy Use (202)''' [[#Henryson--2000|Henryson et al. (2000)]] ; [[#Darby--2006|Darby (2006)]] ; [[#Carlsson-Kanyama--2007|Carlsson-Kanyama and Lindén (2007)]] ; [[#Chen--2017|Chen et al. (2017)]] ; [[#Iwafune--2017|Iwafune et al. (2017)]] ; [[#Burkhardt--2019|Burkhardt et al. (2019)]] ; [[#Henry--2019|Henry et al. (2019)]] ; [[#Wong-Parodi--2019|Wong-Parodi et al. (2019)]] ; [[#Mi--2020|Mi et al. (2020)]] ; [[#Stojanovski--2020|Stojanovski et al. (2020)]] [ [[#Abrahamse--2005|Abrahamse et al. (2005)]] ; Ehrhardt-Martinez and Donnelly (2010); [[#Delmas--2013|Delmas et al. (2013)]] ; [[#Andor--2018|Andor and Fels (2018)]] ; [[#Bergquist--2019|Bergquist et al. (2019)]] ; [[#Buckley--2019|Buckley (2019)]] ; [[#Iweka--2019|Iweka et al. (2019)]] ; [[#Nisa--2019|Nisa et al. (2019)]] ; Zangheri et al. (2019); [[#Wolske--2020|Wolske et al. (2020)]] ; [[#Ahir--2021|Ahir and Chakraborty (2021)]] ; [[#Grilli--2021|Grilli and Curtis (2021)]] ; [[#Khanna--2021|Khanna et al. (2021)]] ]*

'''Investment in Energy Efficiency (30)''' [[#Larrick--2008|Larrick and Soll (2008)]] ; [[#Steg--2008|Steg (2008)]] ; [[#Andor--2018|Andor and Fels (2018)]] *

'''Mode of Transportation (19)''' [[#Steg--2008|Steg (2008)]] ; [[#Pettifor--2017|Pettifor et al. (2017)]] *

| 197

| 38

| 24

| 33

|-
| '''Make behaviour observable and provide recognition'''

| 58

| 53

| 5

| '''Energy Use (24)''' [[#Abrahamse--2005|Abrahamse et al. (2005)]] ; [[#Delmas--2013|Delmas et al. (2013)]] ; [[#Bergquist--2019|Bergquist et al. (2019)]] ; [[#Iweka--2019|Iweka et al. (2019)]] ; [[#Nisa--2019|Nisa et al. (2019)]] ; [[#Grilli--2021|Grilli and Curtis (2021)]] *

'''Investment in Energy Efficiency (30)''' [[#Pettifor--2017|Pettifor et al. (2017)]] *

'''Mode of Transportation (4)''' [[#Pettifor--2017|Pettifor et al. (2017)]] *

| 27

| 28

| 5

| 6

|-
| '''Communicate a norm'''

| 138

| 131

| 7

| '''Energy Source (1)''' [[#Hafner--2019|Hafner et al. (2019)]]

'''Energy Use (116)''' [[#Nolan--2008|Nolan et al. (2008)]] ; [[#Ayers--2009|Ayers and Forsyth (2009)]] ; [[#Allcott--2011|Allcott (2011)]] ; [[#Costa--2013|Costa and Kahn (2013)]] ; [[#Allcott--2014|Allcott and Rogers (2014)]] [[#Abrahamse--2005|Abrahamse et al. (2005)]] ; [[#Abrahamse--2013|Abrahamse and Steg (2013)]] ; [[#Delmas--2013|Delmas et al. (2013)]] ; [[#Andor--2018|Andor and Fels (2018)]] ; [[#Bergquist--2019|Bergquist et al. (2019)]] ; [[#Buckley--2019|Buckley (2019)]] ; [[#Iweka--2019|Iweka et al. (2019)]] ; [[#Nisa--2019|Nisa et al. (2019)]] ; [[#Ahir--2021|Ahir and Chakraborty (2021)]] ; [[#Khanna--2021|Khanna et al. (2021)]] *

'''Investment in Energy Efficiency (15)''' [[#Pettifor--2017|Pettifor et al. (2017)]] ; [[#Niamir--2020b|Niamir et al. (2020b)]] ; [[#Grilli--2021|Grilli and Curtis (2021)]] *

'''Mode of Transportation (7)''' [[#Bamberg--2007|Bamberg et al. (2007)]] ; [[#Bergquist--2019|Bergquist et al. (2019)]] *

| 106

| 21

| 16

| 15

|-
| '''Reframe consequences in terms people care about'''

| 74

| 68

| 6

| '''Energy Source (5)''' [[#Wolske--2018|Wolske et al. (2018)]] ; [[#Hafner--2019|Hafner et al. (2019)]] ; [[#Grilli--2021|Grilli and Curtis (2021)]] *

'''Energy Use (47)''' [[#Abrahamse--2005|Abrahamse et al. (2005)]] ; [[#Darby--2006|Darby (2006)]] ; [[#Delmas--2013|Delmas et al. (2013)]] ; [[#Chen--2017|Chen et al. (2017)]] ; [[#Eguiguren-Cosmelli--2018|Eguiguren-Cosmelli (2018)]] ; [[#Bergquist--2019|Bergquist et al. (2019)]] ; [[#Ghesla--2020|Ghesla et al. (2020)]] ; [[#Mi--2020|Mi et al. (2020)]] ; [[#Khanna--2021|Khanna et al. (2021)]] *

'''Investment in Energy Efficiency (22)''' [[#Andor--2018|Andor and Fels (2018)]] ;* [[#Forster--2021|Forster et al. (2021)]]

'''Mode of Transportation (2)''' [[#Nepal--2010|Nepal et al. (2010)]] ; [[#Mattauch--2016|Mattauch et al. (2016)]]

| 41

| 18

| 19

| 18

|-
| '''Obtain a commitment'''

| 52

| 47

| 5

| '''Energy Source (1)''' [[#Jagger--2019|Jagger et al. (2019)]]

'''Energy Use (47)''' [[#Ghesla--2020|Ghesla et al. (2020)]] ; [[#Abrahamse--2005|Abrahamse et al. (2005)]] ; [[#Steg--2008|Steg (2008)]] ; [[#Delmas--2013|Delmas et al. (2013)]] ; [[#Andor--2018|Andor and Fels (2018)]] ; [[#Iweka--2019|Iweka et al. (2019)]] ; [[#Nisa--2019|Nisa et al. (2019)]] ; [[#Grilli--2021|Grilli and Curtis (2021)]] ; [[#Khanna--2021|Khanna et al. (2021)]] *

'''Investment in Energy Efficiency (1)''' [[#Steg--2008|Steg (2008)]] *

'''Mode of Transportation (5)''' [[#Matthies--2006|Matthies et al. (2006)]] ; [[#Steg--2008|Steg (2008)]] *

| 45

| 4

| 4

| 10

|}

Note: Papers in this review of behavioural interventions to reduce household energy demand were collected through a systemic literature search up to August 2021. Studies are included in the reported counts if they are (i) experimental, (ii) peer-reviewed or highly cited reports, (iii) the intervention is behavioural, and (iv) the targeted behaviour is household energy demand. 559 papers are included in the review. Each paper was coded for: type of behavioural intervention, country of study, energy demand behaviour targeted, whether the target is an ‘Avoid’, ‘Shift’, or ‘Improve’ behaviour, and whether the intervention includes an economic incentive. Some papers do not report all elements. The energy demand behaviour column provides the count of papers that focus on each behaviour type (in parentheses after the behaviour). The citations that follow are not exhaustive but exemplify papers in the category, selected for impact, range, and recency. The asterisk (*) indicates references that are meta-analyses or systematic reviews. Papers within meta-analyses and systematic reviews that meet the inclusion criteria are counted individually in the total counts. The full reference list is available at https://osf.io/9463u/ .

'''Table 5.3b | Summary of effects of behavioural interventions in Table 5.''' '''3a.'''

{| class="wikitable"
|-
! '''Behavioural tool'''

! '''Results'''

'''(expressed in household energy savings, unless otherwise stated)'''

! '''Results summary'''

|-
| '''Set proper default'''

| Meta-analyses find a medium to strong effect of defaults on environmental behaviour. [[#Jachimowicz--2019|Jachimowicz et al. (2019)]] report a strong average effect of defaults on environmental behaviour (Cohen’s d = 0.75, confidence interval 0.39–1.12), though not as high as for consumer decisions. They find that defaults, across domains, are more effective when they reflect an endorsement (recommendation by a trusted source) or endowment (reflecting the status quo). [[#Nisa--2019|Nisa et al. (2019)]] * report a medium average effect size (Cohen’s d = 0.35; range 0.04–0.55).

| [[File:f2e683c2a4b5cee31d5ee09199e92681 IPCC_AR6_WGIII_Table_5_3b_1.png]]

|-
| '''Reach out during transitions'''

| The few interventions that focus on transitions and measure behaviour change (rather than energy savings) report mixed, moderate effect sizes. People were unwilling to change their behaviour if they were satisfied with current options ( [[#Mahapatra--2008|Mahapatra and Gustavsson 2008]] ). [[#Iweka--2019|Iweka et al. (2019)]] find that effective messages can prompt habit disruption.

| [[File:6fb964ee39ad3f109525b9e62f28209f IPCC_AR6_WGIII_Table_5_3b_2.png]]

|-
| '''Timely feedback and reminders'''

| The average effects of meta-analyses of feedback interventions on household energy use reductions range from 1.8% to 7.7%, with large variations ( [[#Delmas--2013|Delmas et al. 2013]] ; [[#Buckley--2019|Buckley 2019]] ; [[#Nisa--2019|Nisa et al. 2019]] ; [[#Buckley--2020|Buckley 2020]] ; [[#Ahir--2021|Ahir and Chakraborty 2021]] ; [[#Khanna--2021|Khanna et al. 2021]] ). The same is true for two literature reviews ( [[#Abrahamse--2005|Abrahamse et al. 2005]] ; [[#Bergquist--2019|Bergquist et al. 2019]] ). Most studies find a 4–10% average reduction during the intervention; some studies find a non-significant result ( [[#Dünnhoff--2008|Dünnhoff and Duscha 2008]] ) or a negative reduction ( [[#Winett--1978|Winett et al. 1978]] ).

Real-time feedback is most effective, followed by personalised feedback ( [[#Buckley--2019|Buckley 2019]] ; [[#Buckley--2020|Buckley 2020]] ). A review by Darby et al. (2006) finds direct feedback (from the meter or display monitor) is more effective than indirect feedback (via billing) (5–15% savings vs 0–10% savings). Feedback effects (Cohen’s d = 0.241) are increased when combined with a monetary incentive (Cohen’s d = 0.96) and with a social comparison and a monetary incentive (Cohen’s d = 0.714) ( [[#Khanna--2021|Khanna et al. 2021]] ).

[[#Sanguinetti--2020|Sanguinetti et al. (2020)]] find that onboard feedback results in a 6.6% improvement in the fuel economy of cars (Cohen’s d: 0.07, [range 0.05–0.08]).

| [[File:a64229224e5e7113ceb1bd6d0516a482 IPCC_AR6_WGIII_Table_5_3b_3.png]]

|-
| '''Timely feedback and reminders'''

| The effectiveness of feedback from in home displays is highly studied. Two reviews find them to have result in a 2–14% energy saving (Ehrhardt-Martinez and Donnelly 2010; [[#Faruqui--2010|Faruqui et al. 2010]] ). A meta-analysis by [[#McKerracher--2013|McKerracher and Torriti (2013)]] finds a smaller range of results, with 3–5% energy savings.

| [[File:d734f7c84f07204e952ae1e4d9d5785c IPCC_AR6_WGIII_Table_5_3_x1.png]]

|-
| '''Make information intuitive and easy to access'''

| Meta-analyses of information interventions on household energy use find average energy savings between 1.8–7.4% and Cohen’s d effect sizes between 0.05 and 0.30 ( [[#Delmas--2013|Delmas et al. 2013]] ; [[#Buckley--2019|Buckley 2019]] ; [[#Nisa--2019|Nisa et al. 2019]] );* [[#Buckley--2020|Buckley 2020]] ; [[#Nemati--2020|Nemati and Penn 2020]] ; [[#Ahir--2021|Ahir and Chakraborty 2021]] ; [[#Khanna--2021|Khanna et al. 2021]] ). Study quality affects the measured effect – small sample sizes, shorter measurement windows, and self-selection are correlated with larger effects ( [[#Nisa--2019|Nisa et al. 2019]] ; [[#Nemati--2020|Nemati and Penn 2020]] ). RCTs have a smaller effect size, 5.2% savings (95% confidence interval [range 0.5% –9.5%]) ( [[#Nemati--2020|Nemati and Penn 2020]] ).

Information combined with comparative feedback is more effective than information alone (d = .34 vs. 30 ( [[#Khanna--2021|Khanna et al. 2021]] ); 8.5% vs 7.4% ( [[#Delmas--2013|Delmas et al. 2013]] ). Monetary incentives make information interventions more effective ( [[#Khanna--2021|Khanna et al. 2021]] ).

Energy efficiency labeling has a heterogenous effect on investment in energy efficiency ( [[#Abrahamse--2005|Abrahamse et al. 2005]] ; [[#Andor--2018|Andor and Fels 2018]] ). Efficiency labels on houses lead to higher price mark ups (Jensen et al. 2016) and house prices ( [[#Brounen--2011|Brounen and Kok 2011]] ). Energy star labels lead to significantly higher willingness to pay for refrigerators ( [[#Houde--2013|Houde et al. 2013]] ), but energy and water conservation varies by appliance from 0–23% ( [[#Kurz--2005|Kurz et al. 2005]] ).

A meta-analysis of interventions to increase alternative fuel vehicle adoption find a small effect (d = .20–.28) ( [[#Pettifor--2017|Pettifor et al. 2017]] ).

| [[File:6280ed3534b3b06c1f235aa938b270f4 IPCC_AR6_WGIII_Table_5_3_x2.png]]

|-
| '''Make behaviour observable and provide recognition'''

| Making behaviour observable and providing recognition lead to 6–7% energy savings ( [[#Winett--1978|Winett et al. 1978]] ; [[#Handgraaf--2013|Handgraaf et al. 2013]] ; [[#Nemati--2020|Nemati and Penn 2020]] ) and a large effects size (Cohen’s d = 0.79-1.06); ( [[#Nisa--2019|Nisa et al. 2019]] *). Community-wide interventions result in 1–27% energy savings ( [[#Iweka--2019|Iweka et al. 2019]] ).

Neighbourhood social influence has a small (d = .28) effect on alternative fuel vehicle adoption ( [[#Pettifor--2017|Pettifor et al. 2017]] ).

| [[File:81d63e600e53d365b02ad2b7fa27aa18 IPCC_AR6_WGIII_Table_5_3b_8.png]]

|-
| '''Communicate a norm'''

| The effect of social norm information on household energy savings ranges from 1.7–11.5% ( [[#Delmas--2013|Delmas et al. 2013]] ; [[#Buckley--2020|Buckley 2020]] ) and Cohen’s d from 0.08–0.32, ( [[#Abrahamse--2013|Abrahamse and Steg 2013]] ; [[#Bergquist--2019|Bergquist et al. 2019]] ; [[#Khanna--2021|Khanna et al. 2021]] ); ( [[#Nisa--2019|Nisa et al. 2019]] )* with similar effects on choice of mode of transportation. [[#Pettifor--2017|Pettifor et al. (2017)]] report a small effect (d = .20–.28) on selecting a more energy efficient car.

The OPOWER study ( [[#Allcott--2011|Allcott 2011]] ), prototypical for the impact of social norms on household energy consumption, finds 2% reduction in long-term energy use and 11–20% energy reduction in the short run ( [[#Allcott--2011|Allcott 2011]] ; [[#Ayres--2013|Ayres et al. 2013]] ; [[#Costa--2013|Costa and Kahn 2013]] ; [[#Allcott--2014|Allcott and Rogers 2014]] ). Impact decays over time ( [[#Allcott--2012|Allcott and Rogers 2012]] ). Norm interventions are less effective for low energy users ( [[#Schultz--2007|Schultz et al. 2007]] ; [[#Andor--2020|Andor et al. 2020]] ). Moral licensing and negative spillover can reduce the overall positive feedback of normative feedback ( [[#Tiefenbeck--2013|Tiefenbeck et al. 2013]] ).

Interventions are more effective when the norm is implicitly inducted, in individual countries, and when people care about the norm ( [[#Nolan--2008|Nolan et al. 2008]] ; [[#Bergquist--2019|Bergquist et al. 2019]] ; [[#Khanna--2021|Khanna et al. 2021]] ). Descriptive norm interventions (social comparisons) are more effective when communicated online,by email or through in-home displays compared to billing letters ( [[#Andor--2018|Andor and Fels 2018]] ), when the reference group is more specific (Shen et al. 2015). [[#Dolan--2013|Dolan and Metcalfe (2013)]] find conservation increased from 4% to 11% when energy savings tips are added.

| [[File:41b504f354221e87aff336b2c347fc3d IPCC_AR6_WGIII_Table_5_3_x3.png]]

|-
| '''Reframe consequences in terms people care about'''

| A meta-analysis by Khanna et al. ( 2021) finds a small and variable effect of motivational interventions that reframe consequences (Cohen’s d = [0–0.423]). Effects are larger when reframing is combined with monetary incentives and feedback (d = .96). Darby et al. (2006) report 10–20% savings for US pay-as-you-go systems. Providing lifecycle cost information increases likelihood of purchasing eco-innovative products ( [[#Kaenzig--2010|Kaenzig and Wüstenhagen 2010]] ). Long term (10-year) operating cost information leads to higher willingness to pay for energy efficiency compared to short-term (1-year) cost information ( [[#Heinzle--2012|Heinzle and Wüstenhagen 2012]] ). Monetary information increases the success of energy reduction interventions ( [[#Newell--2014|Newell and Siikamäki 2014]] ; [[#Andor--2018|Andor and Fels 2018]] ). Reframing interventions are more effective when combined with feedback (d = .24–.96) and with social comparisons and feedback (d = .42) ( [[#Khanna--2021|Khanna et al. 2021]] ).

| [[File:bfddadfaf277578adfed593b4e5be37c Table_5.3b_11.png]]

|-
| '''Obtain a commitment'''

| Commitment and goal interventions result in significant energy reduction in half of studies ( [[#Abrahamse--2005|Abrahamse et al. 2005]] ; [[#Andor--2018|Andor and Fels 2018]] ; [[#Nisa--2019|Nisa et al. 2019]] *). [[#Nisa--2019|Nisa et al. (2019)]] report a moderate average effect (Cohen’s d = 0.34, [0.11–0.66]). When results are significant, the energy savings are around 10% ( [[#Andor--2018|Andor and Fels 2018]] ). Self-set goals perform better than assigned goals ( [[#van%20Houwelingen--1989|van Houwelingen and van Raaij 1989]] ; [[#McCalley--2002|McCalley and Midden 2002]] ; [[#Andor--2018|Andor and Fels 2018]] ) and reasonable goals perform better than unreasonably high or low goals ( [[#van%20Houwelingen--1989|van Houwelingen and van Raaij 1989]] ; [[#Abrahamse--2007|Abrahamse et al. 2007]] ; [[#Harding--2014|Harding and Hsiaw 2014]] ). Interventions are more effective when the commitment is public ( [[#Pallak--1976|Pallak and Cummings 1976]] ) and when combined with information and rewards ( [[#Slavin--1981|Slavin et al. 1981]] ; [[#Völlink--1999|Völlink and Meertens 1999]] ).

| [[File:4a4a0053fc73736359ee1494fdaf6a98 IPCC_AR6_WGIII_Table_5_3b_12.png]]

|}

Note: The second column describes the effects of each of the eight behavioural tools. The third column plots the results of meta-analyses and reviews that focus on each tool. Effects are reported as described in the referenced paper, either as percentage of energy saved (dotted box) or by the effect size, measured as Cohen’s d (dashed box).

\*Two responses to [[#Nisa--2019|Nisa et al. (2019)]] challenge their conclusion that behavioural interventions have a small impact on household energy use (Stern 2020; [[#van%20der%20Linden--2020|van der Linden and Goldberg, 2020]] ). We report the raw data collected and used in [[#Nisa--2019|Nisa et al. (2019)]] . Our data summary supports the arguments by Stern (2020) and [[#van%20der%20Linden--2020|van der Linden and Goldberg (2020)]] that interventions should be evaluated in combination, as well as individually, and that the results are highly sensitive to the chosen estimator.

a Range reported as 95% confidence interval of results used in the meta-analysis or review.

b Range reported as all results included in the meta-analysis or review.

c No range reported.

d Range indicates the reported results within a meta-analysis; this applies when multiple intervention types in a meta-analysis are classified as a single behavioural tool.

<div id="footnote-001" class="_idFootnote"></div>

[[#footnote-001-backlink|1]] The way choices are presented to consumers is known as ‘choice architecture’ in the field of behavioural economics.

<div id="footnote-000" class="_idFootnote"></div>

[[#footnote-000-backlink|2]] The countries and areas classification in this figure deviate from the standard classification scheme adopted by WGIII as set out in Annex II, section 1.

<div id="5.4.2" class="h2-container"></div>

<span id="socio-cultural-drivers-of-climate-mitigation"></span>