Editing IPCC:AR6/WGIII/Chapter-9 (section)

=== 9.5.2 Insights From Non-technological and Behavioural Interventions ===

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A range of NT actions can substantially reduce buildings energy demand and emissions (Figure 9.14; see Supplementary Material 9.SM.2 for details). The subsections below present insights on the variations depending on the solution, subsector, and region.

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[[File:eb668198025488ca55ed8d8d5e2544e3 IPCC_AR6_WGIII_Figure_9_14.png]]

'''Figure 9.14''' | Energy saving and GHG mitigation potentials for categories of NT interventions for Residential (R) and Non-Residential (NR) buildings, from studies with worldwide coverage. Sources: [[#Roussac--2012|Roussac and Bright (2012)]] ; [[#Van%20Den%20Wymelenberg--2012|Van Den Wymelenberg (2012)]] ; [[#Rupp--2015|Rupp et al. (2015)]] ; [[#Creutzig--2016|Creutzig et al. (2016)]] ; [[#Khosrowpour--2016|Khosrowpour et al. (2016)]] ; Ruparathna et al. (2016b); [[#van%20Sluisveld--2016|van Sluisveld et al. (2016)]] ; [[#Ohueri--2018|Ohueri et al. (2018)]] ; [[#Ahl--2019|Ahl et al. (2019)]] ; [[#Bierwirth--2019b|Bierwirth and Thomas (2019b)]] ; [[#Derungs--2019|Derungs et al. (2019)]] ; [[#Grover--2019|Grover (2019)]] ; [[#Kaminska--2019|Kaminska (2019)]] ; Levesque et al. (2019a); [[#Bavaresco--2020|Bavaresco et al. (2020)]] ; [[#Cantzler--2020|Cantzler et al. (2020)]] ; Ivanova and Büchs (2020b); Wilson et al. (2020b); [[#Harris--2021|Harris et al. (2021)]] .

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==== 9.5.2.1 Passive and Active Design, Management, and Operation ====

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Bioclimatic design and passive strategies for natural heating, cooling and lighting, can greatly reduce buildings’ climate impact, and avoid cooling in developing countries ( [[#Bienvenido-Huertas--2021|Bienvenido-Huertas et al. 2021]] , 2020; [[#Amirifard--2019|Amirifard et al. 2019]] ). Design can provide additional small savings, for example, by placing refrigerator away from the oven, radiators or windows ( [[#Christidou--2014|Christidou et al. 2014]] ). Passive management refers to adjustments in human behaviour such as adapted clothing, allocation of activities in the rooms of the building to minimise the energy use ( [[#Klein--2012|Klein et al. 2012]] ; [[#Rafsanjani--2015|Rafsanjani et al. 2015]] ) or manual operation of the building envelope ( [[#Rijal--2012|Rijal et al. 2012]] ; [[#Volochovic--2012|Volochovic et al. 2012]] ). Quantitative modelling of such measures is most common for non-residential buildings, in which adaptive behaviours are affected by the office space distribution and interior design, amount of occupants, visual comfort, outdoor view, and easy-to-use control mechanisms ( [[#O’Brien--2014|O’Brien and Gunay 2014]] ; [[#Talele--2018|Talele et al. 2018]] ). Socio-demographic factors, personal characteristics and contextual factors also influence occupant behaviour and their interactions with buildings ( [[#D’Oca--2018b|D’Oca et al. 2018b]] ; [[#Hong--2020|Hong et al. 2020]] ).

Active management refers to human control of building energy systems. Efficient lighting practices can effectively reduce summer peak demand ( [[#Dixon--2015|Dixon et al. 2015]] ; [[#Taniguchi--2016|Taniguchi et al. 2016]] ). On the contrary, the application of the daylight-saving time in the US increases up to 7% lighting consumption ( [[#Rakha--2018|Rakha et al. 2018]] ). Efficient cooking practices for cooking, appliance use (e.g., avoid stand-by regime, select eco-mode), or for hot water can save up to 25% ( [[#Peschiera--2012|Peschiera and Taylor 2012]] ; [[#Teng--2012|Teng et al. 2012]] ; [[#Abrahamse--2013|Abrahamse and Steg 2013]] ; [[#Berezan--2013|Berezan et al. 2013]] ; [[#Hsiao--2014|Hsiao et al. 2014]] ; [[#Dixon--2015|Dixon et al. 2015]] ; [[#Reichert--2016|Reichert et al. 2016]] ). High behavioural control is so far proven difficult to achieve ( [[#Ayoub--2014|Ayoub et al. 2014]] ; [[#Sköld--2018|Sköld et al. 2018]] ). Automated controls and technical measures to trigger occupant operations are addressed in [[#9.4|Section 9.4]] .

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==== 9.5.2.2 Limited Demands for Services ====

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Adjustment in the set-point temperature in winter and summer results in savings between 5% and 25% ( [[#Ayoub--2014|Ayoub et al. 2014]] ; [[#Christidou--2014|Christidou et al. 2014]] ; [[#Taniguchi--2016|Taniguchi et al. 2016]] ; [[#Sun--2017|Sun and Hong 2017]] ). As introduced in [[#9.3|Section 9.3]] , a series of recent works study a cap on the living area ( [[#Mata--2021a|Mata et al. 2021a]] ) or an increase in household size ( [[#Berrill--2021|Berrill et al. 2021]] ). These studies are promising but of limited complexity in terms of rebounds, interactions with other measures, and business models, thus require further investigation. Professional assistance and training on these issues is limited ( [[#Maxwell--2018|Maxwell et al. 2018]] ).

Willingness to adopt is found for certain measures (full load to laundry appliances, lid on while cooking, turning lights off, defer electricity usage and HVAC systems, adjust set-point temperature by 1°C) but not for others (appliances on standby, using more clothes, avoid leaving the TV on while doing other things, defer ovens, ironing or heating systems, adjust set-point temperature by 3°C, move to a low energy house or smaller apartment) ( [[#Yohanis--2012|Yohanis 2012]] ; [[#Brown--2013|Brown et al. 2013]] ; [[#Li--2017|Li et al. 2017]] ; [[#Sköld--2018|Sköld et al. 2018]] ). A positive synergy with digitalisation and smart home appliances is identified, driven by a combination of comfort requirements and economic interest, confirmed by a willingness to defer electricity usage in exchange for cost savings ( [[#Ferreira--2018|Ferreira et al. 2018]] ; [[#Mata--2020c|Mata et al. 2020c]] ).

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==== 9.5.2.3 Flexibility of Demand and Comfort Requirements ====

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In a flexible behaviour, the desired level of service is the same, but it can be shifted over time, typically allowing automated control, for the benefit of the electricity or district heating networks. There are substantial economic, technical, and behavioural benefits from implementing flexibility measures ( [[#Mata--2020c|Mata et al. 2020c]] ), with unknown social impacts.

With demand-side measures (DSM), such as shifting demand a few hours, peak net demand can be reduced by up to 10–20% ( [[#Stötzer--2015|Stötzer et al. 2015]] ); a similar potential is available for short-term load shifting during evening hours ( [[#Aryandoust--2017|Aryandoust and Lilliestam 2017]] ). Although different household types show different consumption patterns and thus an individual availability of DSM capacity during the day (Fischer et al. 2017), there is limited ( [[#Shivakumar--2018|Shivakumar et al. 2018]] ) or inexistent ( [[#Drysdale--2015|Drysdale et al. 2015]] ; [[#Nilsson--2017|Nilsson et al. 2017]] ) information of consumers’ response to time of use pricing, specifically among those living in apartments ( [[#Bartusch--2014|Bartusch and Alvehag 2014]] ). Behavioural benefits are identified in terms of increased level of energy awareness of the users ( [[#Rehm--2018|Rehm et al. 2018]] ), measured deliberate attempts of the consumers to reduce and/or shift their electricity usage ( [[#Bradley--2016|Bradley et al. 2016]] ). Real-time control and behavioural change influence 40% of the electricity use during the operational life of non-residential buildings ( [[#Kamilaris--2014|Kamilaris et al. 2014]] ).

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==== 9.5.2.4 Circular and Sharing Economy (CSE) ====

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Non-technological CSE solutions, based on the Regenerate, Share, Optimise, Loop, Virtualise, Exchange (ReSOLVE) framework ( [[#CE100--2016|CE100 2016]] ; [[#ARUP--2018|ARUP 2018]] ) include sharing, virtualising and exchanging. These are less studied than circular materials, with notably less investigation of existing buildings and sharing solutions ( [[#Pomponi--2017|Pomponi and Moncaster 2017]] ; Høibye and Sand 2018; [[#Kyrö--2020|Kyrö 2020]] ; [[#European%20Commission--2020|European Commission 2020]] ).

The sharing economy generates an increased utilisation rate of products or systems by enabling or offering shared use, access or ownership of products and assets that have a low ownership or use rate. Measures include conditioned spaces (accommodation, facility rooms, offices) as well as tools and transfer of ownership (i.e., second-hand or donation) ( [[#Rademaekers--2017|Rademaekers et al. 2017]] ; [[#Mercado--2018|Mercado 2018]] ; [[#Hertwich--2020|Hertwich et al. 2020]] ; [[#Cantzler--2020|Cantzler et al. 2020]] ; [[#Harris--2021|Harris et al. 2021]] ; [[#Mata--2021a|Mata et al. 2021a]] ). The evidence on the link between user behaviour and net environmental impacts of sharing options is still limited ( [[#Laurenti--2019|Laurenti et al. 2019]] ; [[#Mata--2020a|Mata et al. 2020a]] ; [[#Harris--2021|Harris et al. 2021]] ) and even begins to be questioned, due to rebounds that partially or fully offset the benefits ( [[#Agrawal--2017|Agrawal and Bellos 2017]] ; [[#Zink--2017|Zink and Geyer 2017]] ). For example, the costs savings from reduced ownership can be allocated to activities with a higher carbon intensity, or result in increased mobility. Both reduced ownership and other circular consumption habits show no influence on material footprint, other than mildly positive influence in low-income households ( [[#Junnila--2018|Junnila et al. 2018]] ; [[#Ottelin--2020|Ottelin et al. 2020]] ).

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==== 9.5.2.5 Value-chain, Social and Institutional Innovations ====

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Cooperative efforts are necessary to improve buildings energy efficiency ( [[#Masuda--2014|Masuda and Claridge 2014]] ; [[#Kamilaris--2014|Kamilaris et al. 2014]] ; [[#Ruparathna--2016|Ruparathna et al. 2016]] ). For instance, interdisciplinary understanding of organisational culture, occupant behaviour, and technology adoption is required to set up occupancy/operation best practises ( [[#Janda--2014|Janda 2014]] ). Similarly, close collaboration of all actors along the value chain can reduce by 50% emissions from concrete use ( [[#Habert--2020|Habert et al. 2020]] ); such collaboration can be enhanced in a construction project by transforming the project organisation and delivery contract to reduce costs and environmental impact ( [[#Hall--2021|Hall and Bonanomi 2021]] ). Building commissioning helps to reduce energy consumption by streamlining the systems, but benefits may not persist. Energy communities are discussed later in the chapter.

NT challenges include training and software costs (tailored learning programs, learning-by-doing, human capital mobilisation), client and market demand (service specification, design and provision, market and financial analysis) and legal issues (volatile energy prices, meeting regulation); and partnership, governance and commercialisation. These challenges are identified for Building Information Modelling (BIM) ( [[#Oduyemi--2017|Oduyemi et al. 2017]] ; [[#Rahman--2019|Rahman and Ayer 2019]] ), PV industry ( [[#Triana--2018|Triana et al. 2018]] ), smart living ( [[#Solaimani--2015|Solaimani et al. 2015]] ) or circular economy ( [[#Vence--2019|Vence and Pereira 2019]] ).

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