Open access peer-reviewed chapter

An Aggregated Embodied and Operational Energy Approach

Written By

Shahaboddin Resalati

Submitted: 02 February 2022 Reviewed: 07 February 2022 Published: 14 April 2022

DOI: 10.5772/intechopen.103073

From the Edited Volume

Nearly Zero Energy Building (NZEB) - Materials, Design and New Approaches

Edited by David Bienvenido-Huertas

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Highly insulated envelopes are an integral part of any net zero energy building with a target to reduce the demand that need to be supplied by the renewable energy and other mitigating measures. While stricter insulation levels can in theory reduce the operational energy demand of buildings, the additional embodied energy investment in the insulations can become significant and not recovered within the expected timeframes. Accounting for embodied energy investment requires a paradigm shift in design of highly insulated buildings and can determine U-value levels that can be justified based on an aggregated operational and embodied energy approach. The following chapter discusses the aggregated approach in more detail showcasing the shortcomings of existing building codes and standards using a case study building. The chapter also reviews the potential barriers of adopting such approaches with a specific focus on the uncertainties of embodied energy data and offers a holistic view on its implications for various end-users and stakeholders within the construction sector. The presented analyses in this chapter depict optimal insulation levels beyond which the additional embodied energy burden cannot be recovered using the associated operational energy savings highlighting the necessity of accounting for embodied energy in developing future design principles for zero energy buildings.


  • embodied carbon
  • aggregated carbon
  • insulation materials
  • optimum carbon levels
  • Life Cycle Assessment

1. Introduction

The international building codes and standards have consistently, through their various iterations, sought to reduce the energy demand of buildings with a focus on better fabric performance and lower U-value requirement among others (Table 1). This was simply due to the fact that the operational energy demand, in earlier versions of standards, was taken as 10 times greater than the embodied energy load, and therefore reasonable to be given priority [2, 3, 4, 5, 6].

FinlandU-value wall0.
U-value roof0.
Notional buildingLimiting factorNotional buildingLimiting factorNotional buildingLimiting factor
UKU-value wall0.350.
U-value roof0.250.350.
EnEV 2002EnEV 2004EnEV 2007EnEV 2009EnEV 2014EnEV 2016
GermanyU-value wall0.450.450.450.350.350.28
U-value roof0.450.450.450.350.350.28
BFS 2008BFS 2011BFS 2016
SwedenU-value wall0.180.180.18
U-value roof0.130.130.13
BCA2007NCC 2011NCC 2015
AustraliaU-value wall0.52 (Z1, 2 and 3)–0.3 (zone 8)0.35 (Z1–Z7)–0.26 (Z8)0.35 (Z1–Z7)–0.26 (Z8)
U-value roof0.37 (Z1)–0.21 (Z8)0.2 (Z1–Z7)–0.16 (Z8)0.2 (Z1–Z7)–0.16 (Z8)
IECC 2009IECC 2012IECC 2018
United StatesU-value wall0.197–0.057 (Z1–Z8)0.197–0.057 (Z1–Z8)0.197–0.057 (Z1–Z8)
U-value roof0.035–0.026 (Z1–Z8)0.035–0.026 (Z1–Z8)0.035–0.026 (Z1–Z8)

Table 1.

Changes in building codes and standards around the world.

Adapted from Resalati et al. [1].

More recently however, when reducing the carbon emissions from the built environment came under more serious scrutiny, this trend that has been cemented in building standard around the world has been questioned and analysed further and different countries have started acknowledging embodied energy in their regulations. For example, France and Belgium are pioneering the move to mandate consideration of embodied energy in their building regulations in Europe. Although this is still relatively new and low impact and the building product manufacturers are only required to report Life Cycle Assessment (LCA) data should they decide to promote the environmental performance of their products, it is a significant shift towards regulating embodied energy in buildings [7]. Other countries within Europe joining the initiative include Austrian, the Netherlands and German legislations. These although acknowledge embodied energy investment a significant contributor to the overall carbon footprint of new buildings, only focus on operational energy currently. Although not fully incorporated in building regulations there exist examples of various embodied energy inventories dedicated to the construction sector and the associated materials and products including BRE’s Green Guide and the Inventory of Carbon and Energy (ICE), U.S. Life Cycle Inventory Database, and the Canadian Building Material Life Cycle Inventory Database [1].

In recent years, the increased use of LCA evaluations to measure the environmental performance of building materials and products has emerged from the push toward integrating embodied energy in emission equations. Various environmental certification systems have been developed and used, such as the Environmental Product Declaration (EPD) [8], to independently verify documents that transparently and accurately communicate the environmental impact of various products in accordance with EN 15804 and ISO 14025. EPDs are type III environmental declarations based on the fundamental product category rules of European standards (PCR).

Although embodied energy has been acknowledged in regulations and researched substantially in the literature, it is still not fully regulated. The ratio of embodied to operational energy has changed over the years with the operational energy reducing as a result of increased adoption of renewable energy and better fabric standards. This, at the same time, increased the use of insulation in the buildings and shifted the ratio considerably [9]. As the ratio shifts, future low and zero energy buildings may see comparable embodied and operational energy measures, or even embodied energy outweighing the operational energy (Figure 1), concluded in RICS [10], Kristjansdottir et al. [11], Sartori & Hestnes [12], Dixit [13], Chau et al. [14], Stephan et al. [15], Dascalaki et al. [16], Mourao et al. [17], Gustavsson & Joelsson [18], Azari and Abbasabadi [19], and Dascalaki et al. [20]. Such drastic changes necessitate a thorough examination of the constraints and challenges that come with regulating embodied energy in the construction industry.

Figure 1.

Embodied to operational proportions for low and zero carbon buildings.

The following section examines the relative challenges and arising opportunities, focusing on issues such as the consistency and reliability of existing data data utilised in LCA analysis, as well as inconsistent modelling methodologies that produce outputs with a high level of uncertainty, and lays the foundation for future research.


2. Embodied energy data: challenges and opportunities

LCA studies are used to properly assess a product's or service's environmental impact. This strategy is largely data-driven, and is heavily reliant on the availability of precise, dependable, and high-quality data [21]. Gathering data with such qualities, on the other hand, has proven difficult for LCA end users and practitioners [22] due to a variety of issues, including manufacturer confidentiality requirements, the time and expertise required to generate reliable data, and inconsistent application of methodological approaches to data analysis [23, 24].

In theory, any LCA study in accordance with common PCRs should allow for reliable comparative analyses to take place for various building materials, products, and services. In practice however, the assumptions used in the LCA models including the service life of the product, maintenance requirements, in use operating energy, and varying system boundary options [1, 25] can significantly shift the results of the LCA models [26]. Several researchers have reported disparities between LCA results based on fundamentally different assumptions, such as functional units [27], system boundaries [2829], LCI databases [21, 30], and End-of-Life (EoL) modelling scenarios [30]; Takano [31]. Clark [32] studied embodied equivalent carbon values for commercial buildings based on different methodologies and demonstrated results ranging from 300 to 1650 kgCO2eq/m2. De Wolf et al. [22] comprehensively investigated discrepancies in the final results of LCA studies due to the quality of available data.

The other factor contributing to further discrepancies that has been studied comprehensively in the literature is the adoption of LCI techniques. The LCI techniques include process, input–output, and hybrid methods. There are fundamental differences between the way data is treated and analysed in these techniques with the process analysis formed around disintegrating the relevant life cycle stages into characteristic processes. The data associated with each stage is collected directly from relevant manufacturers or is provided by specialist data inventories including ecoinvent and GaBi. The Input–output technique is formulated around financial transaction matrices between engaged sectors. The embodied energy values are calculated using energy intensity values that have been assigned to each sector. Hybrid analysis in designed to benefit from advantages of the two techniques and at the same eliminate their shortcomings [15]. The most impactful shortcomings of the two techniques include the ‘truncation error’ which is believed to significantly underrepresent requirements for the process analysis [33, 34, 35, 36, 37] and also the ‘aggregation error’ for input–output analysis for allocating similar energy intensity measures to all products within a sector [38].

There are various studies that have highlighted the discrepancies in embodied energy results associated with adoption of different LCI techniques. Crawford [39], Stephan et al. [15] and Stephan and Stephan [40] have demonstrated in their studies of whole buildings that a hybrid LCI analysis can lead to embodied energy values of up to four times greater than those achieved using a process analysis. In a similar study Wiedmann et al. [41] explored the environmental impact of wind turbines and demonstrated twice as high environmental impacts for hybrid analysis compared with a process analysis. Bontinck et al. [42]. A hybrid LCI was used to explore structural insulated panel systems. The findings of the hybrid analysis were found to be 159 percent higher than a process analysis and 46 percent lower than an input-output analysis. Guan et al. [43] conducted a process study on a hybrid LCA of a building in China and found a 100 percent gap.

Drastic disparities of this nature highlight the need for a harmonised and standardised LCA to be adopted by building regulations allowing for an effective decision-making tool to assist in the early stages of building fabric design, or strategising future policies and product development for various stakeholders.

Gelowitz and McArthur [44] reviewed the published EPDs for building products and identified adoption of different LCI methodologies, high level of incomparability between EPDs using the same PCR, and poor verification practices as the main barriers in adopting the results in further analysis. Although conceding that the number of valid comparisons were substantially greater for EPD generated in compliance with EN 15804, Resalati et al. [1] argue that the EN 15804 harmonisation standard has not been totally effective.

Although several studies have investigated the LCA concept in detail and provided insight into how best these tools could be further optimised for decision making processes, their use is currently primarily limited to academic studies [30], and is not incorporated in the industrial ecosystems in enough depth [7]. This has been attributed to a series of factors in the literature including the lack of appropriate interoperability between LCA methodologies and high demand tools in the construction sector (Anand and Amor [30], Means and Guggemos [45]), the expertise required to carry out LCA studies reliably [45], and LCA priority for various industries at present in parallel to the confidentiality issues the manufacturers see as a barrier in publishing their LCA results [46]. As noted by Resalati et al. [1], such challenges may cause delays in the adoption of such technologies, implying that environmental policies and many of the assumptions on which current policies are founded may not accurately reflect energy and its consequent carbon investments. Several researchers such as Chastas et al. [47], Cellura et al. [48] and Moran et al. [49] have questioned whether our current energy efficiency measures with a focus on ‘operational energy only’, instead of a ‘total energy’ efficiency, are acceptable in the context of longer term strategic policy making.

This chapter, takes on an aggregated operational and embodied energy approach, aiming to demonstrate the impact of the uncertainties of embodied energy data when achieving low and zero energy buildings. The analyses aim to apply the aggregated approach on individual building elements and materials.

This chapter seeks to highlight the significance of considering the uncertainties of embodied energy data when LCA is used as decision making tool to inform the engaged stakeholder and other relevant end users. This will be carried out with a particular view of individual building components and materials, based on a total energy/carbon analysis.

This is illustrated by examining the sensitivity of optimal building insulation level to the deviations of embodied energy data. The assessments are shown in the context of residential buildings in the United Kingdom, although the methodology is not restricted to that and may be applied to a broader operational setting.


3. Aggregated embodied and operational carbon

While the connection between U-values and operational energy/carbon is generally linear (Figure 2), embodied carbon tends to rise at a faster rate as buildings are insulated to better efficiencies. Only thermal conductivity is positively associated with operational carbon (i.e. the line is not dependant on the insulation type).

Figure 2.

Linear relationship between operational carbon and U-value.

Embodied carbon however, is directly dependent on the type of insulation and increases in the level of insulation progressively increase the embodied carbon values relative to thermal conductivity of the material and its associated embodied carbon burden.

Figure 3 illustrates the total carbon curve (for PUR insulation as an example) for a typical dwelling. The key feature is that the aggregated total of the linear and non-linear relationship is inevitably non-linear. The graph demonstrates a progressively diminishing return for incremental improvement in U-value measures. Reducing the total carbon value therefore becomes more challenging to achieve using the building fabric insulation levels.

Figure 3.

Total carbon curve.

The graph indicates an optimum thickness for the insulation level beyond which the additional embodied carbon investment cannot be recovered through operational carbon savings (the marked point on Figure 3). The optimal point can change as the base assumptions are adjusted in the analysis e.g., insulation type, climate, occupancy levels, etc. The key feature however is that the three lines form a curve that repeats in all comparable scenarios. Such curves will eventually flatten for a longer service life or the use of an insulation material with lower associated embodied carbon. Such analyses demonstrate where optimum net benefit is achieved. Beyond these optima embodied carbon burdens exceed operational savings, whilst in advance of these points embodied carbon investment usefully reduced operational requirements.

This form of analysis is key when it comes to designing low/zero energy buildings where the existing standards tend to move towards even lower U-value requirements. On a material level, the analysis demonstrates that many conventional insulation materials cannot achieve very low U-values without incurring carbon disbenefits, whilst other conventional or novel materials with lower embodied carbon relative to their thermal conductivities, can justifiably achieve ambitious U-values.

The flat nature of the total carbon curve naturally creates comparative points on the graph, where lower levels of insulation show parity to the more extreme measures. The identified areas on Figure 4 are referring to the insulation levels that are within 5% variation of the sweet spot i.e., the total carbon level associated with the 300mm insulation is identical to that of 100mm, in this specific case, but within 5% similarity to the total carbon level on the sweet spot. The operational only approach suggests 50% savings for the same range. This is crucial to be incorporated in all future building design strategies if the lower emission targets are to be met where a decarbonised grid coupled with electricity dominated operational energy demand is in the horizon.

Figure 4.

Areas on the total carbon curve where lower levels of insulation show parity to the higher levels.

Such findings will have significant financial implications as well for building design where higher levels of insulation would be more difficult to justify in the future zero energy building codes and standards. This similarly applies to setting the energy efficiency targets for retrofitting the existing building stock around the world.

It is important to realise that the optimal points on the total carbon curve may move towards lower or higher insulation levels depending on the occupancy patterns, climatic conditions, building function and service life, source of energy, HVAC type and settings, and the type of insulation used, but the approach will be valid and its key feature still applicable, as identified above.

In order to demonstrate the extent of variability in results, the following section demonstrate the application of a series of insulation materials on a case study building in the UK. The assessments were conducted on a three-bedroom semi-detached house built in compliance with the most recent Building Regulations in the United Kingdom, as outlined in L1A Conservation of fuel and power. The building has a total floor area of 80 m2.


4. Thermal and environmental performance of insulation materials

The studied insulation materials and their associated reported embodied carbon values are presented in Table 2. The values are extracted from available EPDs for each insulation material.

Materialf.u.Density (kg/m3)λ (W/m k)GWP (kgCO2eq/kg)
Glass wool (GW)m3150.04251.07
Mineral wool (MW)m238.50.036760.85
Expanded polystyrene (EPS)m315.50.0352.99
Extruded polystyrene (XPS)m2350.0312.91
Polyurethane (PU)m2310.0234.03
Phenolic foam (PF)m2350.0212.83
Foam glass (FG)m31650.1030.12
Cellulose (CEL)m3280.0390.13
Vacuum insulation panel (VIP)kg2000.0079.4

Table 2.

Environmental properties of insulation materials with reference to their thermal conductivities.

A box and whisker plot was used to graphically illustrate the locality and spread of GWP/unit weight data extracted from EPD documents as presented in Table 2. The interquartile range (IQR) between the 25th and 75th percentiles, median and standard deviation values are represented with the box, the line, and the whiskers respectively.

A clear distinction can be observed between studied insulation materials with MW and GW presenting comparable median values. EPS, XPS, PU, and PF as insulation materials with a hydrocarbon base also form a distinctive group with very similar median points. The PU and MW however are demonstrating greater probability distribution due to the discrepancies in the data compared with PF and GW.

For a more meaningful comparison between the insulation materials, their relative thermal performance needs to be reflected in the analysis. The thermal resistance (R-value) target for this analysis was taken as 6.6 m2.K/W complying with the UK Building Regulation requirements as explained above. Figure 6 represents the values in Table 2 converted into the GWP values associated with the target thermal resistance for each insulation material.

The distinct insulation groups, with similar median points, that were formed in Figure 5 are not evident in Figure 6 and the GWP values in relation to the thermal resistance demonstrate smaller variation between the insulation groups. The distribution of GWP values for MW becomes significantly broader when the targeted R-value is considered, whereas these values for GW stays relatively stable.

Figure 5.

GWP per unit weight of different insulation materials.

Figure 6.

GWP per unit area of insulation materials for the target thermal resistance.

The median point for XPS insulation material demonstrates 100% higher GWP values compared with EPS. The GWP values for Cellulose-based insulations, cover a broad range from 0.7–67 kgCO2−eq/m2. This is due to the variety of base products used for making cellulose-based insulations. The base products can include refined virgin wood chips and blown recycled cellulosic products, such as wastepaper with requiring their own dedicated processing procedures.

The GWP values for VIPs are significantly higher than the other insulation materials even with factoring their considerably better thermal performance (up to 10 times better) in the analysis. This must be noted however that over 90% of the GWP values associated with VIPs are linked to their core material and specifically in the studied EPD associated with the use pyrogenic silica [50, 51]. The studied EPDs for VIPs are based on a cradle to gate approach and therefore not taking into account the recyclability potential for VIPs. Considering different end-of-life scenarios could change the impact of VIPs and other insulation materials such as cellulose based materials significantly.


5. Optimum U-value levels on the basis of a total carbon analysis

The following section utilises the range of embodied carbon values presented above in order to identify the associated optimum U-value measures and present the uncertainties such discrepancies can cause in early stage building design decision making. Figure 7 demonstrates these points and clearly presents the broad range of identified optimum points that can be achieved using the same type of insulation material depending on the source of embodied carbon values used.

Figure 7.

Optimum U-value points associated with the GWP data points generated on the total carbon curve.

The range of embodied carbon associated with Cellulose insulation as an example, leads the optimum U-value points to cover values from 0.15 W/m2.K to 0.35 W/m2.K. This applies to all other insulation materials as well with MW and PU covering 0.16–0.25 W/m2.K and 0.21–0.29 W/m2.K respectively.

Comparing VIP values with other insulation materials demonstrate that U-values lower than 0.21 W/m2.K could not be reached without leading to an increase in the total carbon values. The VIP values however show comparable results with PU insulation, although due to its higher GWP values, the total carbon value is higher for identical optimum U-values. This is also investigated by Resalati et al. [1] where it was observed for VIPs that their interquartile range was almost double of those for PU. The values demonstrate the CEL, EPS, GW, and MW insulation types allow for lower U-values to be reached, in the context of assumptions applied to this study. Figure 7 further highlights the sensitivity of identifying the optimum insulation levels for low and zero energy buildings to the assumption applied to the LCA models.

The optimum insulation levels based on an aggregated operational and embodied carbon approach allows for identifying the effectiveness of building fabric design in meeting the carbon savings targets. This has been presented that an operational carbon only approach, as is required by the current building energy codes and regulations, does not necessarily lead to a lower overall energy load when compared with an aggregated approach. This has also been concluded by Mohazabieh et al. [52], Gul and Patidar [53] and Stephan et al. [15].

The analyses here further highlights these implications for subsequent future regulatory requirements, and hence provide the building product manufacturers with appropriate tools for analysing their products’ place in any future market where a total carbon approach is applied to building design in principle. The key concept here is that the discussion of factoring embodied energy/carbon into building design decisions is well past the point of questioning its significance and more addressing the challenges of how best this could be incorporated into our existing regulations. This is also concluded in a study presented by Lutzkendorf [54]. Further delays in factoring in the environmental performance of various materials, products, and services when calculating/regulating the required U-values in building design can in principle lead to the design choices that increase the carbon footprint rather than reducing it.

The findings also provide meaningful insight for developing novel insulation technologies. Any new technology will need to have very low levels of embodied energy relative to its R-value if lower insulation levels are to be achieved with the embodied energy values factored in. This can either be achieved using low impact materials or with appropriate plans for end-of-life recyclability. VIPs for instance offer huge potential to be used in future low and zero energy buildings given their very thin nature relative to the thermal conductivity measures, and can potentially outperform the conventional insulation materials based on an aggregated carbon approach. Appropriate end of life treatments however, need to be considered for VIPs to be competitive in the market environmentally.


6. Conclusions

Although existing assessment techniques have specific shortcomings, the number of research and initiatives currently undertaken in many countries highlight that the Life Cycle Assessment of buildings will be a feature of future assessments of building environmental impacts. Regulations incentivizing additional stakeholders to use these methodologies, as recommended by Eurima, should be the driving force behind increased acceptance of assessments of this type [7]. This adoption, on the other hand, requires studies that can deliver practical roadmaps, supporting the engaged stakeholders in establishing effective and long term business strategies. A more in depth understanding of the restrictions of LCA studies is a necessary requirement for developing reliable methodologies that can deliver high accuracy and reliability in a practical way.

When aggregated operational and embodied carbon are taken into account, total carbon curves have been formed identifying sweet spots where the embodied carbon investment cannot be recovered through operational savings. Data uncertainties, occupancy patterns, climatic conditions, building function and service life, source of energy, HVAC type and settings, and the type of insulation used, all contribute to the theoretical minimum. As a result, identical optimal specifications cannot be provided for various scenarios, rather, sufficient analytical and predictive understanding is required.

Considerable total energy savings can be achieved by practices and standards based on such principles. Such studies can help determine optimum insulation levels that can be incorporated into design of a building or that may be needed by standards in the future, as well as the limits to how much present energy-saving methodologies can be increased using certain technologies. Although whole life cycle thinking is now acknowledged in several codes and standards around the world, the lack of availability of reliable and accurate data, and the differences in adopting the existing methodologies for generating EPDs and other LCA results can lead to generating misleading messages to the manufacturers and policymakers. The study provides evidence in favour of better harmonisation and standardisation of LCA and LCI databases and procedures.

Due to high variation in the LCA results as a result of current discrepancies in modelling assumptions, applied methodologies, and data, the total carbon approach can be utilised as guideline for the time being, while the onus remains on LCA specialists and practitioners, as well as other key stakeholders, to harmonise the science across all industries, including software.


  1. 1. Resalati S, Kendrick CC. Hill C. Embodied energy data implications for optimal specification of building envelopes. Building Research & Information. 2020;48(4):429-445
  2. 2. Anastaselos D, Giama E, Papadopoulos AM. An assessment tool for the energy, economic and environmental evaluation of thermal insulation solutions. Energy and Buildings. 2009;41(11):1165-1171
  3. 3. Häfliger IF, John V, Passer A, Lasvaux S, Hoxha E, Saade MRM, et al. Buildings environmental impacts’ sensitivity related to LCA modelling choices of construction materials. Journal of Cleaner Production. 2017;156:805-816
  4. 4. Koezjakov A, Urge-Vorsatz D, Crijns-Graus W, van den Broek M. The relationship between operational energy demand and embodied energy in Dutch residential buildings. Energy and Buildings. 2018;165:233-245
  5. 5. Ramesh T, Prakash R, Shukla KK. Life cycle energy analysis of buildings: An overview. Energy and Buildings. 2010;42(10):1592-1600
  6. 6. Szalay AZZ. What is missing from the concept of the new European Building Directive? Building and Environment. 2007;42(4):1761-1769
  7. 7. Eurima. Life Cycle Assessment of Buildings: A Future-proofed Solution in the Digitalised World of Tomorrow. [ebook]. 2017. Available from:
  8. 8. Tettey UYA, Dodoo A, Gustavsson L. Effects of different insulation materials on primary energy and CO2 emission of a multi-storey residential building. Energy and Buildings. 2014;82:369-377
  9. 9. Cole & Fedoruk. Shifting from net-zero to net-positive energy buildings. Building Research & Information. 2015;43(1):111-120. DOI: 10.1080/09613218.2014.950452
  10. 10. RICS. Methodology to calculate embodied carbon of materials, RICS information paper, IP32/2012. Coventry, UK: Royal Institution of Chartered Surveyors (RICS). 2012
  11. 11. Kristjansdottir TF, Heeren N, Andresen I, Brattebø H. Comparative emission analysis of low-energy and zero-emission buildings. Building Research & Information. 2018;46(4):367-382. DOI: 10.1080/09613218.2017.1305690
  12. 12. Sartori I, Hestnes AG. Energy use in the life cycle of conventional and low-energy buildings: A review article. Energy and Buildings. 2007;39(3):249-257
  13. 13. Dixit MK. Embodied energy and cost of building materials: Correlation analysis. Building Research & Information. 2017;45(5):508-523
  14. 14. Chau CK, Leung TM, Ng WY. A review on life cycle assessment, life cycle energy assessment and life cycle carbon emissions assessment on buildings. Applied Energy. 2015;143:395-413
  15. 15. Stephan A, Crawford RH, De Myttenaere K. A comprehensive assessment of the life cycle energy demand of passive houses. Applied Energy. 2013;112:23-34
  16. 16. Dascalaki EG, Argiropoulou PA, Balaras CA, Droutsa KG, Kontoyiannidis S. Benchmarks for embodied and operational energy assessment of Hellenic Single-Family Houses. Energies. 2020;13(17):4384
  17. 17. Mourao J, Gomes R, Matias L, Niza S. Combining embodied and operational energy in buildings refurbishment assessment. Energy and Buildings. 2019;197:34-46
  18. 18. Gustavsson L, Joelsson A. Life cycle primary energy analysis of residential buildings. Energy and Buildings. 2010;42(2):210-220
  19. 19. Azari R, Abbasabadi N. Embodied energy of buildings: A review of data, methods, challenges, and research trends. Energy and Buildings. 2018;168:225-235
  20. 20. Dascalaki EG, Argiropoulou P, Balaras CA, Droutsa KG, Kontoyiannidis S. Analysis of the embodied energy of construction materials in the life cycle assessment of Hellenic residential buildings. Energy and Buildings. 2021;232:110651
  21. 21. Takano A, Hughes M, Winter S. A multidisciplinary approach to sustainable building material selection: A case study in a Finnish context. Building and Environment. 2014;82:526-535
  22. 22. De Wolf C, Pomponi F, Moncaster A. Measuring embodied carbon dioxide equivalent of buildings: A review and critique of current industry practice. Energy and Buildings. 2017;140:68-80
  23. 23. Lotteau M, Loubet P, Pousse M, Dufrasnes E, Sonnemann G. Critical review of life cycle assessment (LCA) for the built environment at the neighborhood scale. Building and Environment. 2015;93:165-178
  24. 24. Soust-Verdaguer B, Llatas C, García-Martínez A. Simplification in life cycle assessment of single-family houses: A review of recent developments. Building and Environment. 2016;103:215-227
  25. 25. Moncaster AM, Song J-Y. A comparative review of existing data and methodologies for calculating embodied energy and carbon of buildings. International Journal of Sustainable Building Technology and Urban Development. 2012;3(1):26-36. DOI: 10.1080/2093761X.2012.673915
  26. 26. Hill C, Zimmer K. The Environmental Impacts of Wood Compared to Other Building Materials. Norway: Norwegian Institute of Bioeconomy Research (NIBIO); 2018
  27. 27. Cabeza LF, Rincón L, Vilariño V, Pérez G, Castell A. Life cycle assessment (LCA) and life cycle energy analysis (LCEA) of buildings and the building sector: A review. Renewable and Sustainable Energy Reviews. 2014;29:394-416
  28. 28. Rauf A, Crawford RH. Building service life and its effect on the life cycle embodied energy of buildings. Energy. 2015;79:140-148
  29. 29. Silvestre JD, De Brito J, Pinheiro MD. Environmental impacts and benefits of the end-of-life of building materials–calculation rules, results and contribution to a “cradle to cradle” life cycle. Journal of Cleaner Production. 2014;66:37-45
  30. 30. Anand CK, Amor B. Recent developments, future challenges and new research directions in LCA of buildings: A critical review. Renewable and Sustainable Energy Reviews. 2017;67:408-416
  31. 31. Frischknecht R. LCI modelling approaches applied on recycling of materials in view of environmental sustainability, risk perception and eco-efficiency. The International Journal of Life Cycle Assessment. 2010;15(7):666-671
  32. 32. Clark DH. What Colour is Your Building?: Measuring and Reducing the Energy and Carbon Footprint of Buildings. London: RIBA Publishing; 2013
  33. 33. Crawford RH. Validation of a hybrid life-cycle inventory analysis method. Journal of Environmental Management. 2008;88(3):496-506
  34. 34. Crawford RH, Bontinck PA, Stephan A, Wiedmann T. Towards an automated approach for compiling hybrid life cycle inventories. Procedia Engineering. 2017;180:157-166
  35. 35. Crawford RH, Bontinck PA, Stephan A, Wiedmann T, Yu M. Hybrid life cycle inventory methods: A review. Journal of Cleaner Production. 2018;172:1273-1288
  36. 36. Lenzen M. Errors in conventional and Input-Output—based Life—Cycle inventories. Journal of Industrial Ecology. 2000;4(4):127-148
  37. 37. Majeau-Bettez G, Strømman AH, Hertwich EG. Evaluation of process-and input–output-based life cycle inventory data with regard to truncation and aggregation issues. Environmental Science & Technology. 2011;45(23):10170-10177
  38. 38. Säynäjoki A, Heinonen J, Junnila S, Horvath A. Can life-cycle assessment produce reliable policy guidelines in the building sector? Environmental Research Letters. 2017;12(1):013001
  39. 39. Crawford R. Life Cycle Assessment in the Built Environment. London, UK: Routledge; 2011
  40. 40. Stephan A, Stephan L. Reducing the total life cycle energy demand of recent residential buildings in Lebanon. Energy. 2014;74:618-637
  41. 41. Wiedmann TO, Suh S, Feng K, Lenzen M, Acquaye A, Scott K, et al. Application of hybrid life cycle approaches to emerging energy technologies—The case of wind power in the UK. Environmental Science & Technology. 2011;45(13):5900-5907
  42. 42. Bontinck PA, Crawford RH, Stephan A. Improving the uptake of hybrid life cycle assessment in the construction industry. Procedia Engineering. 2017;196:822-829
  43. 43. Guan J, Zhang Z, Chu C. Quantification of building embodied energy in China using an input–output-based hybrid LCA model. Energy and Buildings. 2016;110:443-452
  44. 44. Gelowitz MDC, McArthur JJ. Comparison of type III environmental product declarations for construction products: Material sourcing and harmonization evaluation. Journal of Cleaner Production. 2017;157:125-133
  45. 45. Means P, Guggemos A. Framework for life cycle assessment (LCA) based environmental decision making during the conceptual design phase for commercial buildings. Procedia Engineering. 2015;118:802-812
  46. 46. Han G, Srebric J. Comparison of survey and numerical sensitivity analysis results to assess the role of life cycle analyses from building designers’ perspectives. Energy and Buildings. 2015;108:463-469
  47. 47. Chastas P, Theodosiou T, Bikas D, Kontoleon K. Embodied energy and nearly zero energy buildings: A review in residential buildings. Procedia Environmental Sciences. 2017;38:554-561
  48. 48. Cellura M, Guarino F, Longo S, Mistretta M. Energy life-cycle approach in Net zero energy buildings balance: Operation and embodied energy of an Italian case study. Energy and Buildings. 2014;72:371-381
  49. 49. Moran P, Goggins J, Hajdukiewicz M. Super-insulate or use renewable technology? Life cycle cost, energy and global warming potential analysis of nearly zero energy buildings (NZEB) in a temperate oceanic climate. Energy and Buildings. 2017;139:590-607
  50. 50. Karami P, Al-Ayish N, Gudmundsson K. A comparative study of the environmental impact of Swedish residential buildings with vacuum insulation panels. Energy and Buildings. 2015;109:183-194
  51. 51. Schonhardt U, Binz A, Wohler M, Dott R. Ökobilanz eines Vakuum-Isolations-Paneels (VIP). Basel, German: University of Applied Sciences, Institute of Energy; 2003
  52. 52. Mohazabieh SZ, Ghajarkhosravi M, Fung AS. Energy consumption and environmental impact assessment of the energy efficient houses in Toronto, Canada. Procedia Engineering. 2015;118:1024-1029
  53. 53. Gul MS, Patidar S. Understanding the energy consumption and occupancy of a multi-purpose academic building. Energy and Buildings. 2015;87:155-165
  54. 54. Lützkendorf T. Assessing the environmental performance of buildings: Trends, lessons and tensions. Building Research and Information. 2018;46:594-614. DOI: 10.1080/09613218.2017.1356126

Written By

Shahaboddin Resalati

Submitted: 02 February 2022 Reviewed: 07 February 2022 Published: 14 April 2022