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

== 9.4 Mitigation Technological Options and Strategies Towards Zero Carbon Buildings ==

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Literature in this topic is extensive, but unfortunately, most studies and reviews do not relate themselves to climate change mitigation, therefore there is a clear gap in reporting the mitigation potential of the different technologies ( [[#Cabeza--2020|Cabeza et al. 2020]] ). It should be highlighted that when assessing the literature, it is clear that a lot of new research is focused on the improvement of control systems, including the use of artificial intelligence or internet of things (IoT).

This section is organised as follow. First, the key points from AR5 and special reports are summarised, following with a summary of the technological developments since AR5, specially focusing on residential buildings.

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<span id="key-points-from-ar5-and-special-reports"></span>
=== 9.4.1 Key Points From AR5 and Special Reports ===

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The AR5 WG3 [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-9 Chapter 9] on Buildings (Lucon et al. 2014) presents mitigation technology options and practices to achieve large reductions in building energy use as well as a synthesis of documented examples of large reductions in energy use achieved in real, new, and retrofitted buildings in a variety of different climates and examples of costs at building level. A key point highlighted is the fact that the conventional process of designing and constructing buildings and its systems is largely linear, losing opportunities for the optimisation of whole buildings. Several technologies are listed as being able to achieve significant performance improvements and cost potentials (daylighting and electric lighting, household appliances, insulation materials, heat pumps, indirect evaporative cooling, advances in digital building automation and control systems, and smart meters and grids to implement renewable electricity sources).

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=== 9.4.2 Embodied Energy and Embodied Carbon ===

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==== 9.4.2.1 Embodied Energy and Embodied Carbon in Building Materials ====

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As building energy demand is decreased the importance of embodied energy and embodied carbon in building materials increases ( [[#Ürge-Vorsatz--2020|Ürge-Vorsatz et al. 2020]] ). Buildings are recognised as built following five building frames: concrete, wood, masonry, steel, and composite frames (International Energy Agency 2019a); but other building frames should be considered to include worldwide building construction practice, such as rammed earth and bamboo in vernacular design ( [[#Cabeza--2021|Cabeza et al. 2021]] ).

The most prominent materials used following these frames classifications are the following. Concrete, a man-made material, is the most widely used building material. Wood has been used for many centuries for the construction of buildings and other structures in the built environment; and it remains as an important construction material today. Steel is the strongest building material; it is mainly used in industrial facilities and in buildings with big glass envelopes. Masonry is a heterogeneous material using bricks, blocks, and others, including the traditional stone. Composite structures are those involving multiple dissimilar materials. Bamboo is a traditional building material throughout the world tropical and sub-tropical regions. Rammed earth can be considered to be included in masonry construction, but it is a structure very much used in developing countries and it is finding new interest in developed ones ( [[#Cabeza--2021|Cabeza et al. 2021]] ).

The literature evaluating the embodied energy in building materials is extensive, but that considering embodied carbon is much more scarce ( [[#Cabeza--2021|Cabeza et al. 2021]] ). Recently this evaluation is done using the methodology lifecycle assessment (LCA), but since the boundaries used in those studies are different, varying, for example, in the consideration of cradle to grave, cradle to gate, or cradle to cradle, the comparison is very difficult ( [[#Moncaster--2019|Moncaster et al. 2019]] ). A summary of the embodied energy and embodied carbon cradle to gate coefficients reported in the literature are found in Figure 9.9 ( [[#Alcorn--1998|Alcorn and Wood 1998]] ; [[#Crawford--2010|Crawford and Treolar 2010]] ; [[#Vukotic--2010|Vukotic et al. 2010]] ; [[#Symons--2011|Symons 2011]] ; [[#Moncaster--2012|Moncaster and Song 2012]] ; [[#Cabeza--2013|Cabeza et al. 2013]] ; [[#De%20Wolf--2016|De Wolf et al. 2016]] ; [[#Birgisdottir--2017|Birgisdottir et al. 2017]] ; [[#Pomponi--2016|Pomponi and Moncaster 2016]] , 2018; [[#Omrany--2020|Omrany et al. 2020]] ; [[#Cabeza--2021|Cabeza et al. 2021]] ). Steel represents the materials with higher embodied energy, 32–35 MJ kg –1 ; embodied energy in masonry is higher than in concrete and earth materials, but surprisingly, some types of wood have more embodied energy than expected; there are dispersion values in the literature depending on the material. On the other hand, earth materials and wood have the lowest embodied carbon, with less than 0.01 kgCO 2 per kg of material ( [[#Cabeza--2021|Cabeza et al. 2021]] ). The concept of buildings as carbon sinks arise from the idea that wood stores considerable quantities of carbon with a relatively small ratio of carbon emissions to material volume and concrete has substantial embodied carbon emissions with minimal carbon storage capacity ( [[#Sanjuán--2019|Sanjuán et al. 2019]] ; [[#Churkina--2020|Churkina et al. 2020]] ).

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[[File:5dd3ecad963e07fff5d2f6aad9092f41 IPCC_AR6_WGIII_Figure_9_9.png]]

'''Figure 9.9 | Building materials (a) embodied energy and (b) embodied carbon.''' Source: [[#Cabeza--2021|Cabeza et al. (2021)]] .

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<span id="embodied-emissions"></span>
==== 9.4.2.2 Embodied Emissions ====

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Embodied emissions from production of materials are an important component of building sector emissions, and their share is likely to increase as emissions from building energy demand decrease ( [[#Röck--2020|Röck et al. 2020]] ). Embodied emissions trajectories can be lowered by limiting the amount of new floor area required ( [[#Berrill--2021|Berrill and Hertwich 2021]] ; [[#Fishman--2021|Fishman et al. 2021]] ), and reducing the quantity and GHG intensity of materials through material efficiency measures such as light-weighting and improved building design, material substitution to lower-carbon alternatives, higher fabrication yields and scrap recovery during material production, and re-use or lifetime extension of building components ( [[#Allwood--2011|Allwood et al. 2011]] ; [[#Heeren--2015|Heeren et al. 2015]] ; [[#Hertwich--2019|Hertwich et al. 2019]] ; [[#Churkina--2020|Churkina et al. 2020]] ; [[#Pamenter--2021|Pamenter and Myers 2021]] ; [[#Pauliuk--2021|Pauliuk et al. 2021]] ). Reducing the GHG intensity of energy supply to material production activities also has a large influence on reducing overall embodied emissions. Figure 9.10 shows projections of embodied emissions to 2050 from residential buildings in a baseline scenario (SSP2 baseline) and a scenario incorporating multiple material efficiency measures and a much faster decarbonisation of energy supply (LED and 2°C policy) ( [[#Pauliuk--2021|Pauliuk et al. 2021]] ). Embodied emissions are projected to be 32% lower in 2050 than 2020 in a baseline scenario, primarily due to a lower growth rate of building floor area per population. This is because the global population growth rate slows over the coming decades, leading to less demand for new floor area relative to total population. Further baseline reductions in embodied emissions between 2020 and 2050 derive from improvements in material production and a gradual decline in GHG intensity of energy supply. In a LED + 2°C policy scenario, 2050 embodied emissions are 86% lower than the baseline. This reduction of 2050 emissions comes from contributions of comparable magnitude from three sources; slower floor area growth leading to less floor area of new construction per capita (sufficiency), reductions in the mass of materials required for each unit of newly built floor area (material efficiency), and reduction in the GHG intensity of material production, from material substitution to lower carbon materials, and faster transition of energy supply.

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[[File:c8c51147f55dee1c5bf920fc8f72ff21 IPCC_AR6_WGIII_Figure_9_10.png]]

'''Figure 9.10 | Decompositions of changes in residential embodied emissions projected by baseline scenarios for 2020–2050, and differences between scenarios in 2050 using two scenarios from the RECC model.''' '''(a)''' Global resolution, and '''(b)''' for nine world regions. Emissions are decomposed based on changes in driver variables of population, sufficiency (floor area of new construction per capita), material efficiency (material production per floor area), and renewables (GHG emissions per unit material production). ‘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.

The attribution of changes in embodied emissions to changes in the drivers of population, sufficiency, material efficiency, and GHG intensity of material production 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 9.3, where ''m'' 2 NC refers to floor area of new construction, ''kg'' Mat refers to mass of materials used for new construction, and ''kg'' CO2e refers to embodied GHG emissions in CO 2e . 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 9.4. For instance, to calculate changes in emissions due to population growth, ''D'' will take on the value of population in the two cases being compared. The superscript ''k'' stands for the time period and scenario of the emissions, for example, SSP2 baseline scenario in 2050. When decomposing emissions between two cases ''k'' and ''k–1'' , either the time period or the scenario stays constant. The decomposition is done for every region at the highest regional resolution available, and aggregation (e.g., to global level) is then done by summing over regions. For changes in emissions within a scenario over time (e.g., SSP baseline emissions in 2020 and 2050), the decomposition is made for every decade, and the total 2020–2050 decomposition is then produced by summing decompositions of changes in emissions each decade.

[[File:134c91929fc8eb30196a00f1dfc6fba3 IPCC_AR6_WGIII_Equation_9_3-4.png]]

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<span id="technological-developments-since-ar5"></span>
=== 9.4.3 Technological Developments Since AR5 ===

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==== 9.4.3.1 Overview of Technological Developments ====

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There are many technologies that can reduce energy use in buildings ( [[#Finnegan--2018|Finnegan et al. 2018]] ; [[#Kockat--2018|Kockat et al. 2018]] a), and those have been extensively investigated. Other technologies that can contribute to achieving carbon zero buildings are less present in the literature. Common technologies available to achieve zero energy buildings were summarised in ( [[#Cabeza--2020|Cabeza and Chàfer 2020]] ) and are presented in Tables 9.SM.1 to 9.SM.3 in detail, where Figure 9.11 shows a summary.

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[[File:1c923bafc43e65ff8f31662d597f50ef IPCC_AR6_WGIII_Figure_9_11.png]]

'''Figure 9.11''' | Energy savings potential of technology strategies for climate change mitigation in buildings. Sources: adapted from [[#Imanari--1999|Imanari et al. (1999)]] ; [[#Cabeza--2010|Cabeza et al. (2010)]] ; [[#Fallahi--2010|Fallahi et al. (2010)]] ; [[#Prívara--2011|Prívara et al. (2011)]] ; [[#Radhi--2011|Radhi (2011)]] ; [[#Asdrubali--2012|Asdrubali et al. (2012)]] ; [[#Capozzoli--2013|Capozzoli et al. (2013)]] ; [[#Chen--2013|Chen et al. (2013)]] ; [[#de%20Gracia--2013|de Gracia et al. (2013)]] ; [[#Seong--2013|Seong and Lim (2013)]] ; [[#Sourbron--2013|Sourbron et al. (2013)]] ; [[#Bojić--2014|Bojić et al. (2014)]] ; [[#Haggag--2014|Haggag et al. (2014)]] ; [[#Sarbu--2014|Sarbu and Sebarchievici (2014)]] ; [[#Spanaki--2014|Spanaki et al. (2014)]] ; [[#Vakiloroaya--2014|Vakiloroaya et al. (2014)]] ; [[#Djedjig--2015|Djedjig et al. (2015)]] ; [[#Mujahid%20Rafique--2015|Mujahid Rafique et al. (2015)]] ; [[#Yang--2015|Yang et al. (2015)]] ; [[#Andjelković--2016|Andjelković et al. (2016)]] ; [[#Costanzo--2016|Costanzo et al. (2016)]] ; [[#Coma--2016|Coma et al. (2016)]] ; [[#Harby--2016|Harby et al. (2016)]] ; [[#Navarro--2016|Navarro et al. (2016)]] ; [[#Pomponi--2016|Pomponi et al. (2016)]] ; [[#Coma--2017|Coma et al. (2017)]] ; [[#Khoshbakht--2017|Khoshbakht et al. (2017)]] ; [[#Saffari--2017|Saffari et al. (2017)]] ; [[#Luo--2017|Luo et al. (2017)]] ; [[#Jedidi--2018|Jedidi and Benjeddou (2018)]] ; [[#Romdhane--2018|Romdhane and Louahlia-Gualous (2018)]] ; [[#Lee--2018|Lee et al. (2018)]] ; [[#Alam--2019|Alam et al. (2019)]] ; [[#Bevilacqua--2019|Bevilacqua et al. (2019)]] ; [[#Gong--2019|Gong et al. (2019)]] ; [[#Hohne--2019|Hohne et al. (2019)]] ; [[#Irshad--2019|Irshad et al. (2019)]] ; [[#Langevin--2019|Langevin et al. (2019)]] ; [[#Liu--2019|Liu et al. (2019)]] ; [[#Omara--2019|Omara and Abuelnour (2019)]] ; [[#Rosado--2019|Rosado and Levinson (2019)]] ; [[#Soltani--2019|Soltani et al. (2019)]] ; [[#Varela%20Luján--2019|Varela Luján et al. (2019)]] ; [[#Zhang--2019|Zhang et al. (2019)]] ; [[#Annibaldi--2020|Annibaldi et al. (2020)]] ; [[#Cabeza--2020|Cabeza and Chàfer (2020)]] ; [[#Dong--2020|Dong et al. (2020)]] ; [[#Nematchoua--2020|Nematchoua et al. (2020)]] ; [[#Ling--2020|Ling et al. (2020)]] ; [[#Mahmoud--2020|Mahmoud et al. (2020)]] ; [[#Peng--2020|Peng et al. (2020)]] ; [[#Zhang--2020c|Zhang et al. (2020c)]] ; [[#Yu--2020|Yu et al. (2020)]] .

Other opportunities exist, such as building light-weighting or more efficient material production, use and disposal ( [[#Hertwich--2020|Hertwich et al. 2020]] ), fast-growing biomass sources such as hemp, straw or flax as insulation in renovation processes ( [[#Pittau--2019|Pittau et al. 2019]] ), bamboo-based construction systems as an alternative to conventional high-impact systems in tropical and subtropical climates ( [[#Zea%20Escamilla--2018|Zea Escamilla et al. 2018]] ). Earth architecture is still limited to a niche ( [[#Morel--2019|Morel and Charef 2019]] ). See also Cross-Chapter Box 9 in [[IPCC:Wg3:Chapter:Chapter-13|Chapter 13]] for carbon dioxide removal and its role in mitigation strategies.

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==== 9.4.3.2 Appliances and Lighting ====

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Electrical appliances have a significant contribution to household electricity consumption ( [[#Pothitou--2017|Pothitou et al. 2017]] ). Ownership of appliances, the use of appliances, and the power demand of the appliances are key contributors to domestic electricity consumption ( [[#Jones--2015|Jones et al. 2015]] ). The drivers in energy use of appliances are the appliance type (e.g., refrigerators), number of households, number of appliances per household, and energy used by each appliance ( [[#Chu--2006|Chu and Bowman 2006]] ; [[#Cabeza--2014|Cabeza et al. 2014]] ; [[#Spiliotopoulos--2019|Spiliotopoulos 2019]] ). At the same time, household energy-related behaviours are also a driver of energy use of appliances ( [[#Khosla--2019|Khosla et al. 2019]] ) ( [[#9.5|Section 9.5]] ). Although new technologies such as IoT linked to the appliances increase flexibility to reduce peak loads and reduce energy demand ( [[#Kramer--2020|Kramer et al. 2020]] ), trends show that appliances account for an increasing amount of building energy consumption (Figure 9.8). Appliances used in Developed Countries consume electricity and not fuels (fossil or renewable), which often have a relatively high carbon footprint. The rapid increase in appliance ownership ( [[#Cabeza--2018b|Cabeza et al. 2018b]] ) can affect the electricity grid. Moreover, energy intensity improvement in appliances such as refrigerators, washing machines, TVs, and computers has counteracted the substantial increase in ownership and use since the year 2000 (International Energy Agency 2019b).

But appliances are also a significant opportunity for energy efficiency improvement. Research on energy efficiency of different appliances worldwide showed that this research focused in different time frames in different countries (Figure 9.12). This figure presents the number of occurrences of a term (the name of a studied appliance) appearing per year and per country, according to the references obtained from a Scopus search. The figure shows that most research carried out was after 2010. And again, this figure shows that research is mostly carried out for refrigerators and for brown appliances such as smart phones. Moreover, the research carried out worldwide is not only devoted to technological aspects, but also to behavioural aspects and quality of service (such as digital television or smart phones).

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[[File:a72b324a6d6a060c609085cfabe07c8b IPCC_AR6_WGIII_Figure_9_12.png]]

'''Figure 9.12''' | '''Energy efficiency in appliances research.''' Year and number of occurrences of different appliances in each studied country/territory.

Lighting energy accounts for around 19% of global electricity consumption ( [[#Attia--2017|Attia et al. 2017]] ; [[#Enongene--2017|Enongene et al. 2017]] ; [[#Baloch--2018|Baloch et al. 2018]] ). Many studies have reported the correlation between the decrease in energy consumption and the improvement of the energy efficiency of lighting appliances (Table 9.1). Today, the new standards recommend the phase out of incandescent light bulbs, linear fluorescent lamps, and halogen lamps and their substitution by more efficient technologies such as compact fluorescent lighting (CFL) and light-emitting diodes (LEDs) (Figure 9.8). Due to the complexity of these systems, simulation tools are used for the design and study of such systems, which can be summarised in [[#Baloch--2018|Baloch et al. (2018)]] .

Single-phase induction motors are extensively used in residential appliances and other building low-power applications. Conventional motors work with fixed speed regime directly fed from the grid, giving unsatisfactory performance (low efficiency, poor power factor, and poor torque pulsation). Variable speed control techniques improve the performance of such motors ( [[#Jannati--2017|Jannati et al. 2017]] ).

Within the control strategies to improve energy efficiency in appliances, energy monitoring for energy management has been extensively researched. [[#Abubakar--2017|Abubakar et al. (2017)]] present a review of those methods. The paper distinguishes between intrusive load monitoring (ILM), with distributed sensing, and non-intrusive load monitoring (NILM), based on a single point sensing.

'''Table 9.1 | Typesof domestic lighting devices and their characteristics.''' Source: adapted from [[#Attia--2017|Attia et al. (2017)]] .

{| class="wikitable"
|-
| '''Type of lighting device'''

| '''Code in plan'''

| '''Lumens per watt [lm W''' –1 ''']'''

| '''Colour temperature [K]'''

| '''Lifespan [h]'''

| '''Energy use [W]'''

|-
| Incandescent

| InC

| 13.9

| 2700

| 1000

| 60

|-
| Candle incandescent

| CnL

| 14.0

| 2700

| 1000

| 25

|-
| Halogen

| Hal

| 20.0

| 3000

| 5000

| 60

|-
| Fluorescent TL8

| FluT8

| 80.0

| 3000–6500

| 20,000

| 30–40

|-
| Compact fluorescent

| CfL

| 66.0

| 2700–6500

| 10,000

| 20

|-
| LED GLS

| LeD

| 100.0

| 2700–5000

| 45,000

| 10

|-
| LED spotlight

| LeD Pin

| 83.8

| 2700–6500

| 45,000

| 8

|-
| Fluorescent T5

| FluT5

| 81.8

| 2700–6500

| 50,000

| 22

|-
| LED DT8

| LeDT8

| 111.0

| 2700–6500

| 50,000

| 15

|}

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=== 9.4.4 Case Studies ===

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==== 9.4.4.1 Warehouses ====

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Warehouses are major contributors to the rise of greenhouse gas emissions in supply chains ( [[#Bartolini--2019|Bartolini et al. 2019]] ). The expanding e-commerce sector and the growing demand for mass customisation have even led to an increasing need for warehouse space and buildings, particularly for serving the uninterrupted customer demand in the business-to-consumer market. Although warehouses are not specifically designed to provide their inhabitants with comfort because they are mainly unoccupied, the impact of their activities in the global GHG emissions is remarkable. Warehousing activities contribute roughly 11% of the total GHG emissions generated by the logistics sector across the world. Following this global trend, increasing attention to green and sustainable warehousing processes has led to many new research results regarding management concepts, technologies, and equipment to reduce warehouses carbon footprint, that is, the total emissions of GHG in carbon equivalents directly caused by warehouses activities.

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==== 9.4.4.2 Historical and Heritage Buildings ====

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Historical buildings, defined as those built before 1945, are usually low-performance buildings by definition from the space heating point of view and represent almost 30–40% of the whole building stock in European countries ( [[#Cabeza--2018a|Cabeza et al. 2018a]] ). Historical buildings often contribute to townscape character, they create the urban spaces that are enjoyed by residents and attract tourist visitors. They may be protected by law from alteration not only limited to their visual appearance preservation, but also concerning materials and construction techniques to be integrated into original architectures. On the other hand, a heritage building is a historical building which, for their immense value, is subject to legal preservation. The integration of renewable energy systems in such buildings is more challenging than in other buildings. In the review carried out by [[#Cabeza--2018a|Cabeza et al. (2018a)]] different case studies are presented and discussed, where heat pumps, solar energy and geothermal energy systems are integrated in such buildings, after energy efficiency is considered.

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==== 9.4.4.3 Positive Energy or Energy Plus Buildings ====

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The integration of energy generation on-site means further contribution of buildings towards decarbonisation ( [[#Ürge-Vorsatz--2020|Ürge-Vorsatz et al. 2020]] ). Integration of renewables in buildings should always come after maximising the reduction in the demand for energy services through sufficiency measures and maximising efficiency improvement to reduce energy consumption, but the inclusion of energy generation would mean a step forward to distributed energy systems with high contribution from buildings, becoming prosumers ( [[#Sánchez%20Ramos--2019|Sánchez Ramos et al. 2019]] ). Decrease price of technologies such as photovoltaic (PV) and the integration of energy storage (de Gracia and Cabeza 2015) are essential to achieve this objective. Other technologies that could be used are photovoltaic/thermal ( [[#Sultan--2018|Sultan and Ervina Efzan 2018]] ), solar/biomass hybrid systems ( [[#Zhang--2020b|Zhang et al. 2020b]] ), solar thermoelectric ( [[#Sarbu--2018|Sarbu and Dorca 2018]] ), solar powered sorption systems for cooling ( [[#Shirazi--2018|Shirazi et al. 2018]] ), and on-site renewables with battery storage ( [[#Liu--2021|Liu et al. 2021]] ).

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==== 9.4.4.4 District Energy Networks ====

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District heating networks have evolved from systems where heat was produced by coal or waste and storage was in the form of steam, to much higher energy efficiency networks with water or glycol as the energy carrier and fuelled by a wide range of renewable and low carbon fuels. Common low carbon fuels for district energy systems include biomass, other renewables (i.e., geothermal, PV, and large solar thermal), industry surplus heat or power-to-heat concepts, and heat storage including seasonal heat storage ( [[#Lund--2018|Lund et al. 2018]] ). District energy infrastructure opens opportunities for integration of several heat and power sources and is ‘future proof’ in the sense that the energy source can easily be converted or upgraded in the future, with heat distributed through the existing district energy network. Latest developments include the inclusion of smart control and AI ( [[#Revesz--2020|Revesz et al. 2020]] ), and low temperature thermal energy districts. Authors show carbon emissions reduction up to 80% compared to the use of gas boilers.

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<span id="low--and-net-zero-energy-buildings-exemplary-buildings"></span>
=== 9.4.5 Low- and Net Zero-energy Buildings – Exemplary Buildings ===

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Nearly zero energy (NZE) buildings or low-energy buildings are possible in all world relevant climate zones ( [[#Mata--2020b|Mata et al. 2020b]] ; [[#Ürge-Vorsatz--2020|Ürge-Vorsatz et al. 2020]] ) (Figure 9.13). Moreover, they are possible both for new and retrofitted buildings. Different envelope design and technologies are needed, depending on the climate and the building shape and orientation. For example, using the Passive House standard an annual heating and cooling energy demand decrease between 75% and 95% compared to conventional values can be achieved. Table 9.2 lists several exemplary low- and NZE-buildings with some of their feature.

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[[File:8df06c43acb880558731bb947919e999 IPCC_AR6_WGIII_Figure_9_13.png]]

'''Figure 9.13''' | Regional distribution of documented low-energy buildings. Source: [[#New%20Building%20Institute--2019|New Building Institute (2019)]] ; [[#Ürge-Vorsatz--2020|Ürge-Vorsatz et al. (2020)]] .

'''Table 9.2 | Selected exemplary low- and net zero- energy buildings worldwide.''' Sources: adapted from [[#Mørck--2017|Mørck (2017)]] ; [[#Schnieders--2020|Schnieders et al. (2020)]] ; [[#Ürge-Vorsatz--2020|Ürge-Vorsatz et al. (2020)]] .

{| class="wikitable"
|-
| '''Building name and organisation'''

| '''Location'''

| '''Building type'''

| '''Energy efficiency and renewable energy features'''

| '''Measured energy performance'''

|-
| SDB-10 at the software development company, Infosys

| India

| Software development block

| – Hydronic cooling and a district cooling system with a chilled beam installation

– Energy-efficient air conditioning and leveraged load diversity across categorised spaces: comfort air conditioning (workstations, rooms), critical load conditioning (server, hub, UPS, battery rooms), ventilated areas (restrooms, electrical, transformer rooms), and pressurised areas (staircases, lift wells, lobbies)

– BMS to control and monitor the HVAC system, reduced face velocity across DOAS filters, and coils that allow for low pressure drop

| EPI of 74 kWh m –2 , with an HVAC peak load of 5.2 W m –2 for a total office area of 47,340 m 2 and total conditioned area of 29,115 m 2

|-
| YS Sun Green Building by an electronics manufacturing company Delta Electronics Inc.

| Taiwan, Province of China

| University research green building

| – Low cost and high efficiency are achieved via passive designs, such as large roofs and protruded eaves which are typical shading designs in hot-humid climates and could block around 68% of incoming solar radiation annually

– Porous and wind-channelling designs, such as multiple balconies, windowsills, railings, corridors, and make use of stack effect natural ventilation to remove warm indoor air

– Passive cooling techniques that help reduce the annual air conditioning load by 30%

| EUI of the whole building is 29.53 kWh m –2 (82% more energy-saving compared to the similar type of buildings)

|-
| BCA Academy Building

| Singapore

| Academy Building

| – Passive design features such a green roof, green walls, daylighting, and stack effect ventilation

– Active designs such as energy-efficient lighting, air conditioning systems, building management system with sensors and solar panels

– Well-insulated, thermal bridge free building envelope

| First net zero energy retrofitted building in Southeast Asia

|-
| Energy-Plus Primary School

| Germany

| School

| – Highly insulated Passive House standard

– Hybrid (combination of natural and controlled ventilation) ventilation for thermal comfort, air quality, user acceptance and energy efficiency

– Integrated photovoltaic plant and wood pellet driven combined heat and power generation

– Classrooms are oriented to the south to enable efficient solar shading, natural lighting and passive solar heating

– New and innovative building components including different types of innovative glazing, electrochromic glazing, LED lights, filters and control for the ventilation system

| Off grid building with an EPI of 23 kWh m –2 yr –1

|-
| NREL Research Support Facility

| USA

| Office and research facility

| – The design maximises passive architectural strategies such as building orientation, north and south glazing, daylighting which penetrates deep into the building, natural ventilation, and a structure which stores thermal energy

– Radiant heating and cooling with radiant piping through all floors, using water as the cooling and heating medium in the majority of workspaces instead of forced air

– Roof-mounted photovoltaic system and adjacent parking structures covered with PV panels

| EPI of 110 kWh m –2 yr –1 with a project area of 20,624.5 m 2 to become the then largest commercial net zero energy building in the country

|-
| Mohammed Bin Rashid Space Centre ( [[#Schnieders--2020|Schnieders et al. 2020]] )

| United Arab Emirates, Dubai

| Non-residential, offices

| – Exterior walls U-value = 0.08 W m –2 K –1

– Roof U-value = 0.08 W m –2 K –1

– Floor slab U-value = 0.108 W m –2 K –1

– Windows UW = 0.89 W m –2 K –1

– PVC and aluminium frames, triple solar protective glazing with krypton filling

– Ventilation = MVHR, 89% efficiency

– Heat pump for cooling with recovery of the rejected heat for DHW and reheating coil

| Cooling and dehumidification demand = 40 kWh m –2 yr –1 sensible cooling +10 kWh m – 2 yr –1 latent cooling

Primary energy demand = 143 kWh m –2 yr –1

|-
| Sems Have ( [[#Mørck--2017|Mørck 2017]] )

| Roskilde, Denmark

| Multi-family residential (retrofit)

| – Pre-fabricated, lightweight walls

– Low-energy glazed windows, basement insulated with expanded clay clinkers under concrete

– Balanced mechanical ventilation with heat recovery

– PV

| Final Energy Use: 24.54 kWh m –2

Primary energy use: 16.17 kWh m –2

|}

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