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

== 9.3 New Developments in Emission Trends and Drivers ==

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=== 9.3.1 Past and Future Emission Trends ===

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Total GHG emissions in the building sector reached 12 GtCO 2- eq in 2019, equivalent to 21% of global GHG emissions that year. 57% of GHG emissions from buildings were indirect CO 2 emissions from generation of electricity and heat off-site, 24% were direct CO 2 emissions produced on-site, and 18% were from the production of cement and steel used for construction and refurbishment of buildings (see Cross-Chapter Box 3 and Cross-Working Group Box 1 in Chapter 3, and Figure 9.3a). Halocarbon emissions were equivalent to 3% of global building GHG emissions in 2019. In the absence of the breakdown of halocarbon emissions per end-use sectors, they have been calculated for the purpose of this chapter, by considering that 60% of global halocarbon emissions occur in buildings ( [[#Hu--2020|Hu et al. 2020]] ). CH 4 and N 2 O emissions were negligible, representing 0.08% each out of the 2019 global building GHG emissions. Therefore, this chapter considers only CO 2 emissions from buildings. By limiting the scope of the assessment to CO 2 emissions, the share of emissions from buildings increases to 31% of global 2019 CO 2 emissions. Energy use in residential and non-residential buildings contributed 50% and 32% respectively, while embodied emissions contributed 18% to global building CO 2 emissions.

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'''Figure 9.3: Building GHG emissions: historical based on IEA data and future emissions based on two IEA scenarios (sustainable development, and net zero emissions), IMAGE Lifestyle-Renewable scenario and Resource Efficiency and Climate Change-Low Energy Demand scenario (RECC-LED).''' RECC-LED data include only space heating and cooling and water heating in residential buildings. The IEA current policies scenario is included as a baseline scenario (IEA current policies scenario).

Over the period 1990–2019, global CO 2 emissions from buildings increased by 50%. Global indirect CO 2 emissions increased by 92%, driven by the increase of fossil fuels-based electrification, while global direct emissions decreased by 1%. At regional level, emissions in residential buildings decreased in Developed Countries, except in Australia, Japan and New Zealand, while they increased in developing countries. The highest decrease was observed in Europe and Eurasia, with 13.6% decrease of direct emissions and 33% decrease of indirect emissions, while the highest increase of direct emissions occurred in Middle East, 198%, and the highest increase of indirect emissions occurred in Eastern Asia, 2258%. Indirect emissions from non-residential buildings increased in all regions. The highest increase occurred in Eastern Asia, 1202%, and the lowest increase occurred in Europe and Central Asia, 4%, where direct emissions from non-residential buildings decreased by 51%. Embodied emissions have also increased in all regions. The highest increase occurred in Southern Asia, 334%, while the lowest increase occurred in North America, 4% (Figure 9.3b).

Future emissions were assessed using four global scenarios and their respective baselines (Box 9.2). The selection of the scenarios was based on the features of each scenario, the geographic scope, and the data availability to analyse future building emissions based on the SER framework (Box 9.1).

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=== Box 9.2 | Scenarios Used for the Purpose of This Chapter ===

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Three out of the four scenarios selected, and their related baselines, are based on top-down modelling and were submitted to AR6 scenario database, which includes in total 931 scenarios with a building module (Annex III; see also Boxes 3.1 and 3.2, and Cross-Chapter Box 3 in Chapter 3). A fourth scenario, not included in AR6 scenario database, and based on a bottom-up modelling approach was added.

The main features of these scenarios are shortly described below while the underlying modelling approaches are described in Annex III. Each scenario is assessed compared to its baseline scenario:

Box 9.2

International Energy Agency (IEA) scenarios:

'''2021 Net Zero Emissions by 2050 Scenario (NZE)''' is a normative scenario, which sets out a narrow but achievable pathway for the global energy sector to achieve net zero CO 2 emissions by 2050 ( [[#IEA--2021a|IEA 2021a]] ).

'''2020 Sustainable Development Scenario (SDS)''' '','' which integrates the impact of COVID-19 on health outcomes and economies. It is also a normative scenario, working backwards from climate, clean air, and energy access goals. SDS examines what actions would be necessary to achieve these goals. The near-term detail is drawn from the IEA Sustainable Recovery Plan, which boosts economies and employment while building cleaner and more resilient energy systems ( [[#IEA--2020c|IEA 2020c]] ).

Analysis of the IEA scenarios above was conducted compared to the 2019 Current Policies Scenario, which shows what happens if the world continues along its present path ( [[#IEA--2020c|IEA 2020c]] ), and considered as a baseline scenario.

'''IMAGE-Lifestyle-Renewable''' '''(LiRE)''' scenario is based on an updated version of the SSP2 baseline, while also meeting the RCP2.6 radiative forcing target using carbon prices, together with the increased adoption of additional lifestyle changes, by limiting the growth in the floor area per capita in Developed Countries as well as the use of appliances. Regarding energy supply, IMAGE-LiRE assumes increased electrification and increased share of renewable in the energy mix (Detlef Van [[#Vuuren--2021|Vuuren et al. 2021]] ).

'''Resource Efficiency and Climate Change-Low Energy Demand (RECC-LED) scenario''' is produced by a global bottom-up model, which assesses contributions of resource efficiency to climate change mitigation. RECC-LED estimates the energy and material flows associated with housing stock growth, driven by population and the floor area per capita ( [[#Pauliuk--2021|Pauliuk et al. 2021]] ). This scenario is informed by the Low Energy Demand Scenario (LED), which seeks convergence between developed and developing countries in the access to decent living standard ( [[#Grubler--2018|Grubler et al. 2018]] ).

For consistency between the four scenarios, aggregation of regions in this chapter differs from the one of the IPCC. Europe and Eurasia have been grouped into one single region.

The IEA-NZE scenario projects emissions from the global building stock to be lowered to 29 MtCO 2 by 2050 against 1.7 GtCO 2 in the IEA-SDS and 3.7 GtCO 2 in IMAGE-LiRE Scenario. These projections can be compared to IEA-CPS in which global emissions from buildings were projected to be at 13.5 GtCO 2 in 2050, which is equivalent to the 2018 emissions level (Figure 9.3a). By 2050, direct emissions from residential buildings are projected to be lowered to 108 MtCO 2 in the IEA-NZE, this is four times less than the projected direct emissions in RECC-LED scenario, six times less than those under the IEA-SDS and eleven times less than those in the IMAGE-LiRE scenario.

In the IEA-NZE scenario, indirect emissions are projected to be below zero by 2050 for both residential and non-residential buildings, while residual indirect emissions from residential buildings are projected to be 125 MtCO 2 in RECC-LED, 634 MtCO 2 in IEA-SDS, and 842 GtCO 2 in IMAGE-LiRE. Residual indirect emissions from non-residential buildings are projected to be at 1.7 GtCO 2 in IEA SDS and double of this in IMAGE-LiRE scenario (Figure 9.3a). Compared to IEA-SDS, the highest decrease of emissions in IEA-NZE is expected to occur after 2030. Direct emissions from residential buildings in IEA-NZE are projected to be, by 2030, at 1.37 GtCO 2 , against 1.7 GtCO 2 in the three other scenarios. The highest cut in emissions in IEA-NZE and in IMAGE-LiRE occur through the decarbonisation of energy supply.

At regional level, by 2050, the lowest emissions are projected to occur in developed Asia and Pacific, with 6.73 MtCO 2 under RECC-LED scenario and 12.4 MtCO 2 under the IEA-SDS, and the highest emissions are projected to occur in Europe and Eurasia in all three scenarios, with 152 MtCO 2 in IEA-SDS, 199 MtCO 2 in RECC-LED scenario and 381 MtCO 2 in IMAGE-LiRE scenario. Emissions in Africa are projected to decrease to 10 MtCO 2 in RECC-LED, this is nine time less than those of 2019, while they are projected to increase by 25% in IEA-SDS compared to those of 2019. Compared to IEA-SDS and IMAGE-LiRE, RECC-LED projects the highest decreases, over the period 2020–2030, of direct emissions in residential buildings in all regions, up to 45% in Australia, Japan and New Zealand, and Eastern Asia and the highest decreases of indirect emissions, ranging from 52% in Eastern Asia to 86% in Latin America and Caribbean. Over the same period, the IEA-SDS projects the highest decreases of indirect emissions to occur in Australia, Japan and New Zealand, and North America. IMAGE-LiRE projects the lowest decreases of emissions over the same decade in almost all regions (Figure 9.3b).

Emissions per capita from residential buildings at a global level reached 0.85 tCO 2 per person in 2019. The four scenarios assessed project a decrease of the global per capita emissions by 2050, ranging from 0 tCO 2 in IEA-NZE 0.21 tCO 2 per person in IMAGE-LiRE, a 75% lower than those of 2019 (Figure 9.4a). There are great differences in the projected per capita emissions under each scenario different scenarios across the regions (Figure 9.4b). Compared to IEA-SDS and IMAGE-LiRE scenarios, RECC-LED projects the lowest emissions per capita in all regions by 2050. Emissions per capita in Europe and Eurasia are projected to be the highest in all scenarios by 2050, ranging from 0.26 tCO 2 in RECC-LED and 0.31 tCO 2 in IEA-SDS to 0.65 tCO 2 in IMAGE-LiRE.

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'''Figure 9.4 | Per capita emissions: historical based on IEA data and future emissions based on two IEA scenarios (sustainable development, and net zero emissions), IMAGE Lifestyle-Renewable scenario and Resource Efficiency and Climate Change-Low Energy Demand scenario (RECC-LED).''' RECC-LED data include only space heating and cooling and water heating in residential buildings. The IEA current policies scenario is included as a baseline scenario (IEA current policies scenario).

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<span id="drivers-of-co-2-emissions-and-their-climate-impact"></span>

=== 9.3.2 Drivers of CO 2 Emissions and Their Climate Impact ===

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Building specific drivers of GHG emissions in the four scenarios described above are assessed using an index decomposition analysis with building specific identities and reflecting the three pillars of the Sufficiency, Efficiency, Renewables (SER) framework. Broad drivers of GHG emissions such as GDP and population are analysed using a Kaya decomposition in Chapter 2. Previous decompositions analysing drivers of global GHG emissions in the building sector have either assessed only the impact of GDP and population as drivers of GHG emissions ( [[#Lamb--2021|Lamb et al. 2021]] ) or the impact of building specific drivers on energy demand and not on CO 2 emissions (Lucon et al. 2014; [[#Ürge-Vorsatz--2015|Ürge-Vorsatz et al. 2015]] ; [[#IEA--2020c|IEA 2020c]] ; ODYSSEE 2020). For this assessment, the decomposition was conducted for energy-related CO 2 emissions for residential buildings only, due to lack of data for non-residential buildings.

The attribution of changes in emissions in the use phase to changes in the drivers of population, sufficiency, efficiency, and carbon intensity of energy supply is calculated using additive log-mean divisia index decomposition analysis ( [[#Ang--2000|Ang and Zhang 2000]] ). The decomposition of emissions into four driving factors is shown in Equation 1, where m 2 refers to total floor area, EJ refers to final energy demand, and MtCO 2 refers to the sum of direct and indirect CO 2 emissions in the use phase. The allocation of changes in emissions between two cases ''k'' and ''k'' – ''1'' to changes in a single driving factor D is shown in Equation 2. To calculate changes in emissions due to a single driver such as population growth, D will take on the value of population in the two compared cases. The superscript k stands for the case, defined by the time period and scenario of the emissions, for example, IEA-CPS baseline scenario in 2050. When decomposing emissions between two cases ''k'' and ''k–1'' , either the time-period, or the scenario remains constant. The decomposition was done at the highest regional resolution available from each model output, and then aggregated to regional or global level. For changes in emissions within a scenario over time, the decomposition is done for every decade, and the total 2020–2050 decomposition is then produced by summing decompositions of changes in emissions each decade.

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Over the period 1990–2019, population growth accounted for 28% of the growth in global emissions in residential buildings, the lack of sufficiency policies (growth in floor area per capita) accounted for 52% and increasing carbon intensity of the global energy mix accounted for 16%. Efficiency improvement contributed to decreasing global emissions from residential buildings by 49% (Figure 9.5a). The sufficiency potential was untapped in all regions over the same period while the decarbonisation of the supply was untapped in developing countries and to some extent in Asia-Pacific Developed. The highest untapped sufficiency and supply decarbonisation potentials occurred in Southern Asia where the lack of sufficiency measures has led to increasing emissions by 185% and the high carbon intensity of the energy mix has led to increasing emissions by 340%. In Developed Countries, the highest untapped sufficiency potential occurred in Asia-Pacific Developed region. Middle East is the only region where efficiency potential remained untapped (Figure 9.5b).

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'''Figure 9.5: Decompositions of changes in historical residential energy emissions 1990–2019, changes in emissions projected by baseline scenarios for 2020–2050, and differences between scenarios in 2050 using scenarios from''' '''three models: IEA, IMAGE, and RECC.''' '''RECC-LED data include only space heating and cooling and water heating in residential buildings (a) global resolution, and (b) for nine world regions.''' Emissions are decomposed based on changes in driver variables of population, sufficiency (floor area per capita), efficiency (final energy per floor area), and renewables (GHG emissions per final energy). ‘Renewables’ is a summary term describing changes in GHG intensity of energy supply. Emission projections to 2050, and differences between scenarios in 2050, demonstrate mitigation potentials from the dimensions of the SER framework realised in each model scenario. In most regions, historical improvements in efficiency have been approximately matched by growth in floor area per capita. Implementing sufficiency measures that limit growth in floor area per capita, particularly in developed regions, reduces the dependence of climate mitigation on technological solutions.

Scenarios assessed show an increase of the untapped sufficiency potential at the global level over the period 2020–2050. The highest untapped sufficiency potential occurs in IEA scenarios as there are no changes in the floor area per capita across different scenarios. The lack of sufficiency measures in current policies will contribute to increasing emissions by 54%, offsetting the efficiency improvement effect. By setting a cap in the growth of the floor area per capita in developed countries, 5% of emission reductions in IMAGE-LiRE scenario derives from sufficiency. However, compared to 2020, the lack of sufficiency measures in the baseline scenario will contribute to increasing emissions by 31%. RECC-LED scenario shows the highest global sufficiency potential captured compared to its baseline scenario in 2050 as this scenario assumes a reduction in the floor area per capita in Developed Countries and slower floor area growth in emerging economies. The four scenarios show a higher contribution of the decarbonisation of energy supply to reducing emissions than the reduction of energy demand through sufficiency and efficiency measures (Figure 9.6a). At regional level, the emissions reduction potential from sufficiency is estimated at 25% in North America under both IMAGE-LiRE and RECC-LED scenarios and at 19% in both Eastern Asia and Europe/Eurasia regions (Figure 9.6b). The highest decarbonisation potential due to growth of renewable energy is 75% in Southern Asia under IMAGE-LiRE scenario.

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'''Figure 9.6 | Per capita floor area: historical based on IEA data and future emissions based on two IEA scenarios (sustainable development, and net zero emissions), IMAGE Lifestyle-Renewable scenario and Resource Efficiency and Climate Change-Low Energy Demand scenario (RECC-LED).''' RECC-LED data include only space heating and cooling and water heating in residential buildings. The IEA current policies scenario is included as a baseline scenario (IEA current policies scenario).

There is a growing literature on the decarbonisation of end-use sectors while providing decent living standard for all ( [[#Rao--2017|Rao and Pachauri 2017]] ; [[#Grubler--2018|Grubler et al. 2018]] ; [[#Rao--2018|Rao and Min 2018]] ; [[#Rao--2019|Rao et al. 2019]] ; [[#Millward-Hopkins--2020|Millward-Hopkins et al. 2020]] ). The floor area per capita is among the gaps identified in the convergence between developed and developing countries in the access to decent living ( [[#Kikstra--2021|Kikstra et al. 2021]] ) while meeting energy needs. In the Low Energy Demand (LED) scenario, 30 m² per capita is the converging figure assumed by 2050 ( [[#Grubler--2018|Grubler et al. 2018]] ) while in the Decent Living with minimum Energy (DLE) scenario, ( [[#Millward-Hopkins--2020|Millward-Hopkins et al. 2020]] ) assumes 15 m² per capita.

Overall, the global residential building stock grew by almost 30% between 2005 and 2019. However, this growth was not distributed equally across regions and three out of the four scenarios assessed do not assume a convergence, by 2050, in the floor area per capita, between developed and developing countries. Only RECC-LED implements some convergence between Developed Countries and emerging economies to a range of 20–40 m² per capita. IEA scenarios assume a growth in the floor area per capita in all regions with the highest growth in Developed Countries, up to 72 m² per capita in North America from 66 m² per capita in 2019. IMAGE-LiRE projects a floor area per capita in Africa at 14 m² per person. This is lower than the one of 2019, which was at 16 m² per capita (Figure 9.6). Beyond capturing the sufficiency potential by limiting the growth in the floor area per capita in Developed Countries while ensuring decent living standard, the acceptability of the global scenarios by developing countries is getting attraction in academia ( [[#Hickel--2021|Hickel et al. 2021]] ).

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<span id="energy-demand-trends"></span>
=== 9.3.3 Energy Demand Trends ===

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Global final energy demand from buildings reached 128.8 EJ in 2019, equivalent to 31% of global final energy demand. The same year, residential buildings consumed 70% out of global final energy demand from buildings. Over the period 1990–2019, global final energy demand from buildings grew by 38%, with 54% increase in non-residential buildings and 32% increase in residential ones. At regional level, the highest increase of final energy demand occurred in Middle East and Africa in residential buildings and in all South-East Asia and Pacific in non-residential ones. By 2050, global final energy demand from buildings is projected to be at 86 EJ in IEA-NZE, 111 EJ in IEA-SDS and 138 EJ in IMAGE-LiRE. RECC-LED projects the lowest global final energy demand, at 15.7 EJ by 2050, but this refers to water heating, space heating and cooling in residential buildings only (Figure 9.7a).

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'''Figure 9.7: Final energy demand per fuel: historical based on IEA data and future emissions based on two IEA scenarios (sustainable development, and net zero emissions), IMAGE Lifestyle-Renewable scenario and Resource Efficiency and Climate Change-Low Energy Demand scenario (RECC-LED).''' RECC-LED data include only space heating and cooling and water heating in residential buildings. The IEA current policies scenario is included as a baseline scenario (IEA current policies scenario).

Over the period 1990–2019, the use of coal decreased at a global level by 59% in residential buildings and 52% in non-residential ones. Solar thermal experienced the highest increase, followed by geothermal and electricity. However, by 2019, solar thermal and geothermal contributed by only 1% each to global final energy demand, while electricity contributed by 51% in non-residential buildings and 26% in residential ones. The same year, gas contributed by 26% to non-residential final energy demand and 22% to residential final energy demand, which makes gas the second energy carrier used in buildings after electricity. Over the period 1990–2019, the use of gas grew by 75% in residential buildings and by 46% in non-residential ones. By 2050, RECC-LED projects electricity to contribute by 71% to final energy demand in residential buildings, against 62% in IEA-NZE and 59% in IMAGE-LiRE. IEA-NZE is the only scenario to project less than 1% of gas use by 2050 in residential buildings while the contribution of electricity to energy demand of non-residential buildings is above 60% in all scenarios. At regional level, the use of coal in buildings is projected to disappear while the use of electricity is projected to be above 50% in all regions by 2050 (Figure 9.7b).

Hydrogen emerged in the policy debate as an important energy carrier for the decarbonisation of the energy system. In the case of the building sector, depending on how hydrogen is sourced (Box 12.3), converting gas grids to hydrogen might be an appealing option to decarbonise heat without putting additional stress on the electricity grids. However, according to ( [[#Element%20Energy%20Ltd--2018|Element Energy Ltd 2018]] ; [[#Strbac--2018|Strbac et al. 2018]] ; [[#Frazer-Nash%20Consultancy--2018|Frazer-Nash Consultancy 2018]] ; [[#Broad--2020|Broad et al. 2020]] ; [[#Gerhardt--2020|Gerhardt et al. 2020]] ) the delivered cost of heat from hydrogen would be much higher than the cost of delivering heat from heat pumps, which could also be used for cooling. Repurposing gas grids for pure hydrogen networks will also require system modifications such as replacement of piping and replacement of gas boilers and cooking appliances, a factor cost to be considered when developing hydrogen roadmaps for buildings. There are also safety and performance concerns with domestic hydrogen appliances ( [[#Frazer-Nash%20Consultancy--2018|Frazer-Nash Consultancy 2018]] ). Over the period 1990–2019, hydrogen was not used in the building sector and scenarios assessed show a very modest role for hydrogen in buildings by 2050 (Figure 9.7).

In Developed Countries, biomass is used for generating heat and power leading to reduction of indirect emissions from buildings ( [[#Ortwein--2016|Ortwein 2016]] ) (IEA et al. 2020 c). However, according to ( [[#IEA--2019b|IEA 2019b]] ) despite the mitigation potential of biomass, if the wood is available locally, its use remains low in Developed Countries. Biomass is also used for efficient cook stoves and for heating using modern appliances such as pellet-fed central heating boilers. In developing countries, traditional use of biomass is characterised by low efficiency of combustion (due to low temperatures) leading to high levels of pollutants and CO output, as well as low efficiency of heat transfer. The traditional use of biomass is associated with public health risks such as premature deaths related to inhaling fumes from cooking ( [[#Dixon--2015|Dixon et al. 2015]] ; [[#Van%20de%20Ven--2019|Van de Ven et al. 2019]] ; [[#IEA--2019a|IEA 2019a]] ; [[#Taylor--2020|Taylor et al. 2020]] ). According to ( [[#Hanna--2016|Hanna et al. 2016]] ) policies failed in improving the use of biomass. Over the period 1990–2019, the traditional use of biomass decreased by 1% and all scenarios assessed do not project any traditional use of biomass by 2050. Biomass is also used for the construction of buildings, leading to low embodied emissions compared to concrete ( [[#Heeren--2015|Heeren et al. 2015]] ; [[#Hart--2020|Hart and Pomponi 2020]] ; [[#Pauliuk--2021|Pauliuk et al. 2021]] ).

Over the period 1990–2019, space heating was the dominant end-use in residential buildings at a global level, followed by water heating, cooking, and connected and small appliances (Figure 9.8a). However, energy demand from connected and small appliances experienced the highest increase, 280%, followed by cooking, 89%, cooling, 75%, water heating, 73% and space heating, around 10%. Space heating energy demand is projected to decline over the period 2020–2050 in all scenarios assessed. RECC-LED projects the highest decrease, 77%, of space heating energy demand, against 68% decrease in the IEA-NZE. IMAGE-LiRE projects the lowest decrease of heating energy demand, 21%. To the contrary, all scenarios confirm cooling as a strong emerging trend (Box 9.3) and project an increase of cooling energy demand. IMAGE-LiRE projects the highest increase, 143% against 45% in the IEA-NZE while RECC-LED projects the lowest increase of cooling energy demand, 32%.

There are great differences in the contribution of each end-use to the regional energy demand (Figure 9.8b). In 2019, more than 50% of residential energy demand in Europe and Eurasia was used for space heating while there was no demand for space heating in Middle East, reflecting differences in climatic conditions. To the contrary, the share of energy demand from cooking out of total represented 53% in the Middle East against 5% in Europe and Eurasia reflecting societal organisations. The highest contribution of energy demand from connected and small appliances to the regional energy demand was observed in 2019 in the Asia-Pacific Developed, 24%, followed by the region of Southern Asia, South-East Asia and Developing Pacific, with 17%. Energy demand from cooling was at 9% out of total energy demand of Southern Asia, South-East Asia and Developing Pacific and at 8% in both Middle East and North America while it was at 1% in Europe in 2019.

The increased cooling demand can be partly explained by the increased ownership of room air-conditioners per dwellings in all regions driven by increased wealth and the increased ambient temperatures due to global warming ( [[#Cayla--2011|Cayla et al. 2011]] ; [[#Liddle--2021|Liddle and Huntington 2021]] ) (Box 9.3). The highest increase, 32%, in ownership of room air-conditioners was observed in Southern Asia and South-East Asia and Developing Pacific while Europe, Latin America and Caribbean countries, Eastern Asia and Africa experienced an increase of 21% in households’ ownership of room air-conditioners. The lowest increases in room air-conditioners ownership were observed in the Middle East and North America with 1% and 8% each as these two markets are almost saturated. All scenarios assessed project an increase of ownership of cooling appliances in all regions over the period 2020–2050.

Energy demand from connected and small appliances was, at a global level, above 7 EJ in 2019 (Figure 9.8a). However, it is likely that global energy demand from connected and small appliances is much higher as reported data do not include all the connected and small appliances used by households and does not capture energy demand from data centres (Box 9.3). Over the period 1990–2019, the highest increase of energy demand from connected and small appliances, 4740%, was observed in Eastern Asia, followed by Southern Asia, 1358% while the lowest increase, 99%, occurred in Asia-Pacific Developed countries. The increase of energy demand from connected and small appliances is driven by the ownership increase of such appliances all over the world. The highest increase in ownership of connected appliances, 403%, was observed in Eastern Asia and the lowest increase in ownership of connected appliances was observed in North America, 94%. Future energy demand is expected to occur in the developing world given the projected rate of penetration of household appliances and devices ( [[#Wolfram--2012|Wolfram et al. 2012]] ). However, ( [[#Grubler--2018|Grubler et al. 2018]] ) projects a lower energy demand from connected and small appliances by assuming an increase of shared appliances and multiple appliances and equipment will be integrated into units delivering multiple services.

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'''Figure 9.8: Energy per end use: historical based on IEA data and future emissions based on two IEA scenarios (sustainable development, and net zero emissions), IMAGE Lifestyle-Renewable scenario and Resource Efficiency and Climate Change-Low Energy Demand scenario (RECC-LED).''' RECC-LED data include only space heating and cooling and water heating. The IEA current policies scenario is included as a baseline scenario (IEA current policies scenario).

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<span id="box-9.3-emerging-energy-demand-trends-in-residential-buildings"></span>
=== Box 9.3 | Emerging Energy Demand Trends in Residential Buildings ===

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Literature assessed points to three major energy demand trends:

'''Cooling energy demand'''

In a warming world ( [[#IPCC--2021|IPCC 2021]] ) with a growing population and expanding middle-class, the demand for cooling is likely to increase leading to increased emissions if cooling solutions implemented are carbon intensive ( [[#Santamouris--2016|Santamouris 2016]] ; [[#Sustainable%20Energy%20for%20All--2018|Sustainable Energy for All 2018]] ; [[#Dreyfus--2020b|Dreyfus et al. 2020b]] ; [[#Kian%20Jon--2021|Kian Jon et al. 2021]] ; [[#UNEP%20and%20IEA--2020|UNEP and IEA 2020]] ). Sufficiency measures such as building design and forms, which allow balancing the size of openings, the volume, the wall and window area, the thermal properties, shading, and orientation are all non-cost solutions, which should be considered first to reduce cooling demand. Air conditioning systems using halocarbons are the most common solutions used to cool buildings. Up to 4 billion cooling appliances are already installed and this could increase to up to 14 billion by 2050 ( [[#Peters--2018|Peters 2018]] ; [[#Dreyfus--2020b|Dreyfus et al. 2020b]] ). Energy efficiency of air conditioning systems is of a paramount importance to ensuring that the increased demand for cooling will be satisfied without contributing to global warming through halocarbon emissions ( [[#Campbell--2018|Campbell 2018]] ; [[#Shah--2015|Shah et al. 2015]] , 2019; [[#UNEP%20and%20IEA--2020|UNEP and IEA 2020]] ). The installation of highly efficient technological solutions with low global warming potential (GWP), as part of the implementation of the Kigali amendment to the Montreal Protocol, is the second step towards reducing GHG emissions from cooling. Developing renewable energy solutions integrated to buildings is another track to follow to reduce GHG emissions from cooling.

'''Electricity energy demand'''

Building electricity demand was slightly above 43 EJ in 2019, which is equivalent to more than 18% of global electricity demand. Over the period 1990–2019, electricity demand increased by 161%. The increase of global electricity demand is driven by the combination of rising incomes, income distribution and the S-curve of ownership rates ( [[#Wolfram--2012|Wolfram et al. 2012]] ; [[#Gertler--2016|Gertler et al. 2016]] ). Electricity is used in buildings for plug-in appliances, in other words, refrigerators, cleaning appliances, connected and small appliances and lighting. An important emerging trend in electricity demand is the use of electricity for thermal energy services (cooking, water and space heating). The increased penetration of heat pumps is the main driver of the use of electricity for heating. Heat pumps used either individually or in conjunction with heat networks can provide heating in cold days and cooling in hot ones. ( [[#Lowes--2020|Lowes et al. 2020]] ) suggests electricity is expected to become an important energy vector to decarbonise heating. However, the use of heat pumps will increase halocarbon emissions ( [[#UNEP%20and%20IEA--2020|UNEP and IEA 2020]] ). [[#Connolly--2017|Connolly (2017)]] , [[#Bloess--2018|Bloess et al. (2018)]] , and [[#Barnes--2020|Barnes and Bhagavathy (2020)]] argue for electrification of heat as a cost-effective decarbonisation measure, if electricity is supplied by renewable energy sources ( [[#Ruhnau--2020|Ruhnau et al. 2020]] ). The electrification of the heat supplied to buildings is likely to lead to an additional electricity demand and consequently additional investment in new power plants. [[#Thomaßen--2021|Thomaßen et al. (2021)]] identifies flexibility as a key enabler of larger heat electrification shares. Importantly, heat pumps work at their highest efficiency level in highly efficient buildings and their market uptake is likely to require incentives due to their high up-front cost ( [[#Hannon--2015|Hannon 2015]] ; [[#Heinen--2017|Heinen et al. 2017]] ).

'''Digitalisation energy demand'''

Energy demand from digitalisation occurs in data centres, which are dedicated buildings or part of buildings for accommodating large amount of information technologies equipment such as servers, data storage and communication devices, and network devices. Data centres are responsible for about 2% of global electricity consumption ( [[#Avgerinou--2017|Avgerinou et al. 2017]] ; [[#Diguet--2019|Diguet and Lopez 2019]] ). Energy demand from data centres arises from the densely packed configuration of information technologies, which is up to 100 times higher than a standard office accommodation ( [[#Chu--2019|Chu and Wang 2019]] ). Chillers combined with air handling units are usually used to provide cooling in data centres. Given the high cooling demand of data centres, some additional cooling strategies, such as free cooling, liquid cooling, low-grade waste heat recovery, absorption cooling and so on, have been adopted. In addition, heat recovery can

Box 9.3

provide useful heat for industrial and building applications. More recently, data centres are being investigated as a potential resource for demand response and load balancing ( [[#Zheng--2020|Zheng et al. 2020]] ; [[#Koronen--2020|Koronen et al. 2020]] ). Supplying data centres with renewable energy sources is increasing ( [[#Cook--2014|Cook et al. 2014]] ) and is expected to continue to increase ( [[#Koomey--2011|Koomey et al. 2011]] ). Estimates of energy demand from digitalisation (connected and small appliances, data centres, and data networks) combined vary from 5% to 12% of global electricity use ( [[#Gelenbe--2015|Gelenbe and Caseau 2015]] ; [[#Malmodin--2018|Malmodin and Lundén 2018]] ; [[#Ferreboeuf--2019|Ferreboeuf 2019]] ; [[#Diguet--2019|Diguet and Lopez 2019]] ). According to ( [[#Ferreboeuf--2019|Ferreboeuf 2019]] ) the annual increase of energy demand from digitalisation could be limited to 1.5% against the current 4% if sufficiency measures are adopted along the value chain.

Digitalisation occurs also at the construction stage. ( [[#European%20Union--2019|European Union 2019]] ; [[#Witthoeft--2017|Witthoeft and Kosta 2017]] ) identified seven digital technologies already in use in the building sector. These technologies include (i) Building Information Modelling/Management (BIM), (ii) additive manufacturing, also known as 3D printing, (iii) robots, (iv) drones, (v) 3D scanning, (vi) sensors, and (vii) internet of things (IoT). BIM supports decision making in the early design stage and allows assessing a variety of design options and their embodied emissions ( [[#Basbagill--2013|Basbagill et al. 2013]] ; [[#Röck--2018|Röck et al. 2018]] ). 3D printing reduces material waste and the duration of the construction phase as well as labour accidents ( [[#Dixit--2019|Dixit 2019]] ). Coupling 3D printing and robots allows for increasing productivity through fully automated prefabricated buildings. Drones allow for a better monitoring and inspection of construction projects through real-time comparison between planned and implemented solutions. Coupling drones with 3D scanning allows predicting building heights and energy consumption ( [[#Streltsov--2020|Streltsov et al. 2020]] ). Sensors offer a continuous data collection and monitoring of end-use services (i.e., heating, cooling, and lighting), thus allowing for preventive maintenance while providing more comfort to end-users. Coupling sensors with IoT, which connects to the internet household appliances and devices such as thermostats, enable demand-response, and flexibility to reduce peak loads ( [[#IEA--2017a|IEA 2017a]] ; [[#Lyons--2019|Lyons 2019]] ). Overall, connected appliances offer a variety of opportunities for end-users to optimise their energy demand by improving the responsiveness of energy services ( [[#IEA--2017a|IEA 2017a]] ; [[#Nakicenovic--2019|Nakicenovic et al. 2019]] ) through the use of digital goods and services ( [[#Wilson--2020|Wilson et al., 2020]] ) including peer-to-peer electricity trading ( [[#Morstyn--2018|Morstyn et al. 2018]] ).

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