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

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

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==== 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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'''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.

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