Integrating building information modeling and life cycle assessment to analyze the role of climate and passive design parameters in energy consumption

Abstract

With the Architecture, Engineering, and Construction industry representing a significant share of global energy consumption and greenhouse gas emissions, developing sustainable design and reducing buildings’ environmental impacts has become a priority over the past decades. Adopting building information modeling tools and implementing them into life cycle analysis techniques at the early stages of design has been an effective method for buildings sustainability evaluation. With our environment constantly changing, it is reasonable that the construction industry should also aim to adapt to these changes and use them. However, the role of local climate features and their effects on a building's energy output is often neglected. By using building information modeling–life cycle analysis integration techniques, this research aims to consider the role of climatic attributes along with some passive design strategies in the life cycle assessment of the building and see how it affects that building's energy performance. By only applying passive design strategies (not including heating, ventilation, and air conditioning system type), the models’ energy demand was reduced up to 30% of the original value. It is also witnessed that by using the proper equipment and construction materials that match the respective climate, up to 28% of the building's energy consumption during the operational phase, can be saved. Although insulation of the walls does reduce energy consumption values, it also contributes to more greenhouse gas emissions. The increase in greenhouse gas emissions was estimated to be around 3%, but since the insulation boards make up only 2% of the building's area, it is a considerable amount.

Keywords

Building information modeling-based life cycle analysis passive design parameters climate and local data (climate change)energy consumption greenhouse gas emissions

Introduction

The construction industry is known to be the largest resource consumer and is responsible for 25–40% of global carbon emissions.¹ Lowering energy intensity and environmental impacts of the construction sector have become a priority in the Architecture, Engineering, and Construction (AEC) industry. According to the Oxford dictionary, the word “sustainability” has increased approximately 40 times in academic sources from 1980 to 2019. This indicates the increasing importance of sustainable development. Avoiding the unnecessary depletion of natural resources and energy is a valuable aspect of sustainability. Sustainable construction is a practice that aims to raise the quality of life for residents by maintaining a balanced relationship between the different demands of people and affordable possibilities.²

A common tool for assessing sustainability in the construction industry is the life cycle assessment (LCA). It is a powerful method to assess and reduce the environmental impacts of the building sector that aggregates and analyzes the flows of resources and materials throughout the life cycle of buildings. LCA is defined by the International Organization for Standardization (ISO)—Standard 14040:2006.³ ISO specifies the general framework, principles, and requirements for conducting and reporting life cycle assessment studies but does not go into detail about the LCA technique or the methodologies for different phases of LCA.

Quantifying the environmental impacts of a building through LCA generally falls into three categories: pre-construction phase, construction phase, and post-construction phase. A complete life cycle analysis considers the entire life cycle of a building from extracting raw materials, manufacturing and transporting them to the site, to construction, operation and maintenance, and finally, recycling and demolition. However, due to the enormous amount of data needed, conducting a whole building LCA can take an exhaustive amount of time and effort, and results may still be prone to many errors. Furthermore, as the tendency for sustainable development grows, it seems befitting that LCA results are handed over in the early stages of design so they can be considered in the decision-making process. Otherwise, it may be too late to make any changes in the progress. Conducting sustainability evaluation at the early stages of projects, such as schematic and preliminary design phases, in which possible changes have a lower cost, would have a much higher impact on the sustainability of designs.⁴ Thus, most of the building's life cycle impacts are a consequence of decisions made in the early design stages, making it extremely important to carefully select materials with low embodied impacts.⁵ Researchers have already recognized the importance of early design stages to reduce buildings’ life cycle environmental impacts and improve building sustainability.^5–7 Eleftheriadis et al.⁸ have also identified that the early design phase is where benefits are more noticeable, as decisions cost less, are more effective, and can be easier introduced. Thus, acting in such project stages is essential to effectively reduce building environmental impacts.⁹ Designers need to apply suitable tools at the early stage of the project, which can facilitate the selection of materials to integrate different phases, including disposal for a specified outcome. Building information modeling (BIM) offers the platform in the construction arena to include correlated activities and makes room for further collaboration in design and construction practices.^10,11

BIM and LCA integration

The BIM-based development of LCA is a reliable way of solving time-consuming calculations and facilitating the generation of results while reducing the number of errors made. BIM provides designers, architects, and engineers with data required to evaluate energy consumption and environmental impacts in the construction sector throughout the entire life cycle of buildings.¹² It can be considered that BIM harmonizes both the information about building materials and the evaluation of their environmental impacts.¹³ It also potentially solves the lack of compatibility between projects, allowing the identification of future mistakes and delivery of positive impacts in the final project.¹⁴ Many frameworks have been proposed to simplify or facilitate the integration of LCA and BIM.

Najjar et al.¹⁵ analyzed the methodology of LCA from a building perspective. They presented the role of BIM and LCA integration in evaluating the environmental impacts of building materials to assist in the decision-making process while achieving more efficient, cost-effective, and sustainable design standards, especially at the early stages of design. They also concluded that most of the environmental impacts occur during the manufacturing and operation phases and encouraged reviewing the application of building materials to reduce the passive contribution to the environment. Bueno and Fabricio¹⁶ identified the consequences of simplifying LCA data and methodology toward integrating the LCA-BIM platform. They compared the results of simulation in wall systems made on the Tally^TM plug-in and the results of a full ad-hoc LCA on Gabi 6 software. They simulated five different wall systems and then compared the LCA outputs provided by two different LCA software. The results regarding the most impacting alternatives were inconsistent, showing how much data simplification could affect the accuracy of the received outputs. Soust-Verdaguer et al.¹⁷ developed a BIM-based LCA method to assess the environmental impacts of the single-family house envelope alternatives located in Uruguay during the early stages of design. The method was meant to help with decision-making (e.g. selection of materials, techniques, and transport distances) throughout the lifecycle of the building. It included a description of the data structure framework and a case study application. It also aimed to reduce data acquisition efforts and optimize the design process to assess the environmental impacts of the envelope alternatives of single-family houses during the design stages. Ramaji et al.⁴ explained the sustainability evaluation and identified the areas where BIM could be leveraged for that purpose. Finally, they assessed how IFC and LOD could potentially facilitate the implementation of BIM for sustainability evaluation. Shadram et al.¹⁸ proposed another framework and developed a prototype. The framework presented the conceptual distinctions, required processes, and the information flow for assessing embodied energy associated with the building materials supply chain. The prototype was later used to test the applicability of the framework.

According to previous research, most negative environmental impacts occur during manufacturing and operation.¹⁹ It is estimated that material production and operation stages share more than 90% of the total life cycle energy demand of a building. So LCA of buildings is usually simplified by studying these two main stages of buildings’ life cycles.²⁰ According to the European Commission, European buildings account for 42% of final energy consumption, 35% of greenhouse gas emissions, and 50% of materials produced in their production, use, and dismantling.²¹ In addition, they are responsible for 30% of water consumption and a third of all waste.²² Buildings consume about 40% of global energy and are responsible for 25% of CO₂ emissions.²³ Such a heavy toll on the environment will have many negative impacts if left unattended.

As stated earlier in this paper, LCA quantifies the impacts of the consumed amount of energy on the environment. The quantification process takes shape in the form of a few categories, namely: global warming potential (GWP), the depletion of the stratospheric ozone layer, the acidification of land and water sources, eutrophication, the formation of tropospheric ozone, and the depletion of nonrenewable energy sources. GWP is the main impact category expressed in kgCO₂ and measures how much energy the emissions of 1 ton of a gas will absorb over a given period of time relative to the emissions of 1 ton of carbon dioxide (CO₂).²⁴

Asare et al.²⁵ sought to demonstrate that BIM-based LCA and Energy Analysis can facilitate SBD (Sustainable Building Design) in Ghana. By emphasizing the process of assessment and information requirements, a framework that incorporated a guidance matrix and a workflow process was proposed. Tushar et al.¹⁵ used a systematic way to establish connectivity of LCA's phases and provide an approach to overcome the difficulties of obtaining optimized building construction. They also conducted a sensitivity analysis to find the most sensitive components. Results showed that the ceiling's insulation variation (0.84–0.96) and the walls (0.22–0.37) were the key influential factors in energy reduction than other passive design strategies. Finally, BIM's necessity was evolved in connection with energy simulation tools to optimize the design process from a sustainability perspective. A case study was used to validate the proposed framework.

LCA adoption of passive design strategies

Various studies indicate that passive design measures and orientation considerably influence energy efficiency, comfort, and safety.²⁶ Several types of research work aim to discuss the implementation of passive strategies to reduce the energy demand in buildings.^27–29 An adequate practice of passive design involves several aspects of building design,^30,31 such as the orientation of the main façades and windows, the choice of appropriate walls materials, thermal insulation, and window-to-wall ratio (WWR), along with the design of shading devices and the implementation of natural ventilation techniques.^32–36

It has been found that the adoption of passive design strategies can save up to 37% of the peak energy load during its operation phase.³⁷ This passive design strategy integrates the installation of extruded polystyrene (EPS), whitewash in external walls, reflective coating over the window's glazing, and overhanging up to 1.5 m. Possible breakdown of heat losses through various components of building façade such as external walls (29–59%), floors (6–37%), windows (13–24%), leakages (7–25%), and rooftops (2–11%) were presented in separate research.³⁸ Energy simulation tools are important in passive design applications due to their substantial benefits in improving building performance. Such passive design applications include parameters like building geometry, thermos-physical properties of building façade, sealing off the air leakages, and building layout.^39,40 Therefore, this study has performed energy simulations on developed models to assess and evaluate the role of the passive parameters in the energy consumption of the models. Furthermore, it is commonly agreed that the passive design requirements vary with the climates.⁴¹ Harkouss et al.⁴¹ simulated 25 different climates and performed a comprehensive study on passive design options for residential buildings to produce the best practices for reducing the buildings’ energy demands. This study did not take advantage of the BIM-LCA integration and instead used multi-objective optimization (MOO), where all objective functions were not simultaneously minimized/maximized. Furthermore, the resulting greenhouse gas (GHG) emissions or other relevant LCA factors were not comprehensively studied. As a result, an option that might have proved beneficial in energy saving during the building's life cycle might have left a higher carbon footprint in other stages. Kurian et al.⁴² presented a contextual investigation of a residential complex located in southern India with a warm and humid climate. The research also focused on comparing results from different LCA databases and manual computations of energy consumption. According to the authors, because none of the available databases were tailored for India's humid and warm climate, results from the LCA energy simulation appeared to be 58% higher than the original energy consumption values obtained from the actual electricity bills issued by the state electricity board.

Existing policies aiming to reduce the energy consumption of buildings usually underestimate the importance and impact of local and global climate change.⁴³ Building energy demand will change in response to future climate change, with cooling and heating demand generally going the opposite. Net increases or decreases largely depend on a region's cooling or heating demand dominance.⁴⁴ When it comes to analyzing a building's performance over its life cycle, there is not one single factor that defines the outcome of the research but multiple objects and phases that influence the performance either independently or in connection with each other. However, the influence of weather and local attributes are usually neglected because their role is not properly demonstrated. Another issue when applying LCA to different studies is how the regional and local-specific variables could affect the results of the life cycle assessment. Several life cycle inventory (LCI) analyses and life cycle impact assessment (LCIA) studies have been undertaken to study site-specific considerations.⁴⁵ However, very few attempts have been made at identifying the necessity of considering such local characteristics and their corresponding role in energy consumption and GHG emissions. Since climate zones vary from one geographical point to the other, this research aims to see how using different materials and passive design strategies could affect the buildings’ energy consumption during their operational phase and according to their corresponding climate zone. Thus, an identical prototype was developed across three states of North America that feature different weather and climate attributes. Results were then compared to see whether using the same construction system for all three prototypes was beneficial. How passive design parameters affected each climate zone was also investigated.

Methodology

This study is aimed to assess and compare the life cycle results of a two-story residential building at three locations, namely Boston, Arizona, and Quebec, which feature different climatic attributes. The methodology is therefore aggregated into four major phases, (a) a review of the integration of BIM-LCA tools and the energy analysis of the buildings industry, which was presented earlier in this paper, (b) an LCA study on a two-story building in three states with different climate attributes across North America, (c) investigating the contribution of passive design strategies in energy consumption values across the three climatic regions, and (d) comparison of the results and discussion of further optimized solutions (Figure 1).

Figure 1.

A general framework for optimized design.

Energy consumption of the buildings has been broadly categorized under two stages: firstly, the embodied energy, which is associated with materials extraction to the construction phase, and secondly, the operational energy that is used over the building service life after the construction.²⁸ This study aims to include both phases in its analysis of the environmental impacts. Figure 2 overviews the proposed framework in this research. Revit 2021 was used as the main BIM platform to assist in the modeling and integration of data. When the modeling was done, Green Building studio was used to run the building's energy simulations and optimize its fuel and energy efficiency. Next, the models were transported to insight to optimize the energy consumption and see how changing passive design parameters affect the building's energy performance in each location. This was done by changing the building's orientations, glazing type, WWR, and heating, ventilation, and air conditioning (HVAC) systems. After all the models were optimized individually, the results were compared. The model's data was then exported to the life cycle assessment plug-in, known as OneClickLCA, to assess the global warming potential (GWP) and other environmental indicators. Lastly, to assist in the assembly of the Bills of Quantities (BoQ), Revit and dynamo were used to estimate the area and volume of each construction element and export them directly into an Excel spreadsheet. The dynamo code was set so that if any changes were made to the models’ characteristics, the excel spreadsheet would update itself so long as the code was a rerun.

Figure 2.

Proposed framework.

This study compares LCA results for different types of non-structural external walls to account for the environmental impacts of each alternative depending on the specified location. Five scenarios were deployed to see how insulating the walls or changing the applied materials could contribute to the embodied energy consumption in each location. The presentation of the developed model and general design parameters is shown in Figure 3 and Table 2, respectively. Regarding the prototype, as shown in Figure 3, a typical single-family dwelling with four occupants was developed, simple enough to ensure a smooth analysis. The assigned building was designated into conditioned and unconditioned zones.

Figure 3.

Developed a 3D model (left) and plan view of the floors (right).

Table 1.

Design parameters.

Design parameters	Value
Total area (m²)	330.96
Conditioned areas (m²)	277.01
Unconditioned areas (m²)	53.01
Area per person (m²)	105.8
HVAC equipment	Central VAV, HW Heat, Chiller 5.96 cop
Cooling set point (°C)	21.11
Heating set point (°C)	23.33

HVAC: heating, ventilation, and air conditioning.

Table 2.

Design specifications for developed scenarios.

	Design specifications for external walls, floors, and windows
Scenario 1	20 cm brick walls; concrete slab floors of 15 mm; double-glazed aluminum frame windows with low-E.
Scenario 2	20 cm brick walls with 5 cm EPS insulation boards; concrete slab floors of 15 mm; double-glazed aluminum framed windows with low-E.
Scenario 3	Lightweight metal cladding of 12 mm and 5 cm non-ventilated air gap; 2 mm metal deck with 30° inclined pitch; double-glazed windows with wood frames.
Scenario 4	Lightweight metal cladding of 12 mm and 5 cm EPS insulation board; 2 mm metal deck with 30° inclined pitch; double-glazed windows with wood frames.
Scenario 5	10 mm concrete masonry unit with 5 cm EPS insulation; concrete slab floors of 15 mm; double-glazed aluminum framed windows with low-E.

EPS: extruded polystyrene.

Table 3 summarizes all the simulation parameters of passive design alternatives that lead to further optimization of the models. The aim is not only to optimize the models but also to observe how passive parameters affect each model and if one model could make more use of the changes because of its specific climate.

Table 3.

Passive design variations.

Rotation with respect to the north	45°, 90°, 135°, 180°, 225°, 270°, and 315°
Windows glass type	Double or triple-glazed windows with low-E glass.
WWR	Glazing area/gross wall area varying from 0% to 95%.
Air infiltration	Unintentional leakage of air into or out of conditioned spaces. Measured by air changes per hour (ACH), varies from 0.2 to 2 ACH
HVAC systems	The HVAC system efficiency will vary based on its type and the location of the building.
Lighting efficiency	Power consumption of electric lighting per unit floor area (W/m²), varies from 3 to 21 W/m².

HVAC: heating, ventilation, and air conditioning; WWR: window-to-wall ratio.

Results and discussion

Results of 287 simulations carried out for three different climate zones are presented in this study. The most impacting passive parameter for each climate is detected, and the role of the building's characteristics in the energy demands of the models is presented. Finally, the environmental impacts of the buildings during their 30 years of the life cycle are assessed, and GHG emissions are reported.

Investigating passive design parameters in different climates

To improve the understanding of passive strategies and their contribution to buildings’ energy demand, seven variables were studied for each climate. By varying passive design parameters, energy variations were simulated in the building. These parameters include changing building orientation, WWR, roof insulation, lighting efficiency, infiltration, and PV panel. When one of the mentioned parameters was changed, the Energy Use Intensity (EUI) variation was calculated by Insight. Amongst the 287 simulations for each climate zone the most energy efficient case was selected for further optimization through material alternation. Analysis results are shown in Figure 4 and general design parameters are represented in Table 1. The main focus of this study is to investigate the role of passive parameters in every climate zone. However, to show how active systems can also still heavily impact the energy consumption results in a building, Figure 4 represents the HVAC system type and lighting efficiency as well.

Figure 4.

EUI variation versus passive and active design parameters in different climates (scenario 5).

Figure 4 represents the EUI variation of the studied climate zones based on the change that has been applied to the model, if the alternation that was applied, makes a positive impact, then the EUI variation is negative. If the applied changes cause the models’ energy consumption to ramp up, then the EUI for that case is positive. The WWR and building orientation proved to be the least impacting factors, while roof insulation and HVAC system which happen to be active measures, were the most effective options in reducing the building's overall energy intensity despite the climate. As shown in the infiltration plot, the building in Quebec and Boston can save up to 200 kWh/m²/yr if the air change per hour is reduced to zero. Photovoltaic (PV) panels were installed on the roof to optimize the buildings’ energy output. The panel's efficiency was set to 20%, and considering the area needed for maintenance access and rooftop equipment, it was assumed that up to 90% of the roof's surface was available for coverage. With the payback limit of 30 years, the PV panels installed on the rooftop in Quebec displayed a negligible amount of energy output, while the building in Arizona contributed to more than 120 kWh/m²/yr in EUI reduction of the house, almost 7% of the buildings total annual consumption. Given the usually heat-intensive weather in Arizona, it is expected that the building can benefit widely from solar energy and reduce the energy intensity of the buildings. Figure 5 summarizes the results from the passive design optimizations shown in Figure 4, so it is easier to see which parameter affected a certain climate the most. Note that only the most optimal case of the 287 runs is displayed in the results from Figure 5. While Figure 4 represents the results from all the 287 alternatives that were investigated in each climate zone.

Figure 5.

Energy reduction potential based on the passive design parameter.

Energy and fuel consumption analysis

Results presented in this research are based on energy consumption analysis, including electricity consumption for the whole building during its life cycle. The same method was used to analyze the buildings’ energy consumption across three states in North America. The resulting values are distributed into four fields: Heating, Cooling, Interior Lighting, and Interior Equipment, as shown in Figure 6.

Figure 6.

Energy consumption (kWh) distribution in (a) Quebec, (b) Boston, and (c) Arizona.

The building in Quebec has a total EUI of 3354.7 MJ/m²/year, while the building in Boston has a EUI of 2609.7 MJ/m²/year. Lastly, the EUI for the building in Tucson, Arizona, is 1172.1 MJ/m²/year.

Based on the analysis results and assuming that all three buildings take about the same amount of energy for interior lighting and equipment, more than 40% of the total consumed energy for the building in Quebec goes for heating, while less than 15% of the energy is provided for cooling purposes. About 50% of the annual energy consumption for the building in Arizona was used for cooling, while only 2% was provided for heating the building. As for the building in Boston, 22% and 27% of the total energy was relatively used for cooling and heating purposes.

In general, more than 50% of total energy consumption, regardless of the building's location, was used either for cooling or heating the building. In other words, by increasing the energy efficiency of the building, the potential for lowering the energy intensity increases.

Roofs and walls are the interconnectors between the exterior and interior atmospheres of a building. Applying insulation to the exterior walls and roof is a pragmatic approach to maintaining a comfortable temperature inside the house.⁴⁶ Given the large area of walls in the buildings, they play a significant role in their energy efficiency. Using proper thermal insulation contributes to decreasing the size of a mechanical heating-cooling system, saving annual energy costs, and enhancing inhabitants’ thermal comfort without relying on the air-conditioning system.⁴⁷ By changing the wall setup and using thermal layers to make them more energy reserved, the analysis was rerun to see if a 5 cm thermal layer would change the energy consumption and how it would affect each area. Figure 7 shows the analysis results and the energy distribution for all three areas.

Figure 7.

Energy consumption (kWh) distribution in (a) Quebec, (b) Boston, and (c) Arizona.

As shown in Figure 7, by using a 5 cm thermal layer on the outer layer of the external walls, the building in Quebec witnessed about a 28% reduction in its annual energy consumption. The building in Arizona went through minor changes (< 5%) in its annual energy consumption.

The reason for minor changes in the building in Arizona after adding the thermal layer is that most of the energy in the building (almost 50%) was consumed for cooling purposes. Therefore, adding a thermal layer to preserve the heat inside the building did not help the building with its energy consumption.

For the building in Quebec, where the summers are comfortable and wet, and winters are snowy and freezing,²⁰ most of the buildings’ energy (about 40%) is provided for heating purposes. So by only using a 5 cm thermal layer, the changes in the buildings’ energy efficiency are drastic. The building in Boston, where summers are warm, and winters are frigid and wet, experienced a 20% reduction in annual energy consumption. Table 4 shows the amount of electricity consumption by each building and their specific locations. Option A is before changes were made to the wall system and option B contains results after modifying the walls.

Table 4.

Annual energy consumption.

	Option A	Total annual energy		Option B	Total annual energy
	EUI (MJ/m²/year)	Electricity (kWh)	Fuel (MJ)	EUI (MJ/m²/year)	Electricity (kWh)	Fuel (MJ)
Boston	2609.7	42.148	528.185	1948.5	35.184	376.786
Quebec	3354.7	60.198	893.543	2534.4	48.636	655.734
Arizona	1172.1	44.511	227.670	993.2	43.193	170.095

EUI: Energy Use Intensity.

The overall electricity and fuel consumption were reduced after the modifications, with the building in Quebec undergoing the most drastic changes.

EUI analysis of different scenarios

The suitable design and selection of building materials for its façade can substantially reduce the thermal load in harsh climatic conditions. Insulation is one of the efficient technologies that can be adapted to residential buildings to reduce thermal load.⁴⁸ An energy consumption analysis based on the developed scenarios was conducted to see how climate affects the models’ energy demand in every scenario and which material is more suitable for each climate. An extensive one-on-one comparison between the first two scenarios was presented in the last section. To broaden the scope of the research, nine climate zones across North America were studied in this section. As shown in Figure 8, the numbers obtained from EUI analysis of different scenarios in each climate zone are relatively close and the biggest difference is found when the model goes from no insulation to somewhat insulated walls. Amongst the studied cases, the most significant change is found in climate zone Dfc (26.1%) and Bsk (27.8%). Meanwhile Am (7.9%) and Aw (2.1%) climate zones were barely affected by the adopted changes. The overall scenario-based results are shown in Table 5.

Figure 8.

Energy Use Intensity (EUI) variation in a different climate zone for each scenario.

Table 5.

EUI for each scenario in different climate zones.

Energy use intensity (MJ/m²/year)
	Scenario 1	Scenario 2	Scenario 3	Scenario 4	Scenario 5
Boston	2609.7	1948.5	1738.4	1798.5	1735.4
Quebec	3354.7	2534.4	2471.4	2562.4	2472.3
Arizona	1172.1	993.2	931.2	951.1	919.6
Vancouver	2121.0	1652.3	1624.1	1675.4	1620.2
Seattle	1867.9	1486.9	1461.5	1503.8	1466.3
Los Vegas	1148.8	967.4	959.9	980.7	958.0
Honduras	857.0	794.1	803.8	812.4	789.3
Cuba	809.5	795.1	792.7	799.1	794.8
Montana	2948.9	2183.1	2129.4	2209.1	2127.8

EUI: Energy Use Intensity.

The noticeable gap between scenario 1 (the only scenario with no insulation) and the rest of the table shows the significance of adding some insulation and thermal control to the models. A quick comparison between scenarios 3 and 4 indicates that installing an insulation system is insufficient. Various types of insulations respond differently to each climate to the point that further insulation in scenario 4 has not contributed to saving energy but added to it. The concrete blockwork scenario seemed to be the most suitable alternative for the building in Boston. While for the building in Quebec, the metal clads with 5 cm EPS seemed to be the most proper option.

LCA results and GHG emissions

With the building energy demand, LCA calculations were carried out in OneClickLCA, an existing Autodesk Revit plug-in. The calculation period was set to 30 years, and a full-on cradle-to-grave was defined to include all the life cycle stages. The embodied emissions of building materials are responsible for approximately 85% of total GHGs emissions,⁴⁹ and choosing more sustainable materials in a building project can indirectly decrease GHGs emissions by 12%.⁵⁰ The change in the results for embodied GWP can have three reasons: (a) a change in the number of elements, (b) a change in the volume of the existing elements, and (c) a change in the material that is assigned to these elements.⁵¹ Since the wall material was modified, the LCA was run twice, once before the thermal layer and once after the insulation material was added. Results of the life cycle analysis were expressed in the following impacts: acidification potential (AP), eutrophication potential (EP), global warming potential (GWP), ozone depletion potential (ODP), and formation of ozone in the lower atmosphere, as shown in Table 6.

Table 6.

Environmental impacts of the building per life cycle stages.

Result category	LCA stage	Global warming potential(kg CO₂e)	Acidification(kg SO₂e)	Eutrophication(kg PO₄e)	Ozone depletion potential(kg CFC11e)	Formation of ozone of lower atmosphere(kg Ethenee)
Construction materials	A1–A3	$4.44 \times 10^{4}$	$1.7 \times 10^{2}$	$7.33 \times 10^{0}$	$1.67 \times 10^{- 2}$	$9.89 \times 10^{0}$
Transportation to site	A4	$3.37 \times 10^{3}$	$7.32 \times 10^{0}$	$1.54 \times 10^{0}$	$5.89 \times 10^{- 4}$	$4.34 \times 10^{- 1}$
Energy use	B6	$7.01 \times 10^{5}$	$1.28 \times 10^{3}$	$2.67 \times 10^{2}$	$6.24 \times 10^{- 2}$	$6.1 \times 10^{1}$
End of life	C1–C4	$1.42 \times 10^{3}$	$5.75 \times 10^{0}$	$1.31 \times 10^{0}$	$2.07 \times 10^{- 4}$	$3.13 \times 10^{- 1}$

LCA: life cycle assessment.

The table shows that, as expected, the energy use stage (B6) is the building's most impactful phase, with the material's phase (A1 to A3) following as second. The transportation and end-of-life stage turned out to have the least significant impacts on the buildings’ environmental performance. Based on the outputs of the software, more than 68% of the embodied carbon emission in the materials stage was produced by bricks, making them the main contributors to environmental impacts. Furthermore, the building's structural material, concrete, also dedicated 31% of the total embodied impact in the construction phase to itself, leaving the rest of the materials for openings and windows less impacting the building's life cycle performance.

As mentioned before, the LCA of the building was recalculated after adding 5 cm thermal insulation to the external walls. The insulation material used is expanded polystyrene insulation, more commonly referred to as EPS insulation. Table 7 shows the LCA results after adding the EPS insulation.

Table 7.

Environmental impacts of the building per life cycle stages.

Result category	LCA stage	Global warming potential(kg CO₂e)	Acidification(kg SO₂e)	Eutrophication(kg PO₄e)	Ozone depletion potential(kg CFC11e)	Formation of ozone of lower atmosphere(kg Ethenee)
Construction materials	A1–A3	$4.57 \times 10^{4}$		$7.6 \times 10^{0}$	$1.67 \times 10^{- 2}$	$1.39 \times 10^{0}$
Transportation to site	A4	$3.3 \times 10^{3}$	$7.28 \times 10^{0}$	$1.54 \times 10^{0}$	$5.86 \times 10^{- 4}$	$4.31 \times 10^{- 1}$
Energy use	B6	$5.8 \times 10^{5}$	$1.06 \times 10^{3}$	$2.22 \times 10^{2}$	$5.18 \times 10^{- 2}$	$5.06 \times 10^{1}$
End of life	C1–C4	$1.95 \times 10^{3}$	$5.84 \times 10^{0}$	$1.31 \times 10^{0}$	$2.05 \times 10^{- 4}$	$3.17 \times 10^{- 1}$

LCA: life cycle assessment.

It is important to remember that, when it comes to carbon footprint, some economically irrelevant inputs become very significant, such as, for example, the bolts, which possess a high carbon footprint, but a negligible cost compared to the total cost of the wall.¹⁴ Results show that adding an EPS layer would cause a 3% increase in the total GHG emissions of the building during its life cycle. However, given that the mass of the unit used is less than 2% of the total mass of the building, it is not to be negated.

Preparing the BoQ in Revit and Dynamo

The tendency to report LCA results at the early stages of design is growing daily. With the LCA results and energy analysis complete, an automated bill of quantities was created using Revit and Dynamo. Since it is still early to prepare a full-on list of the quantities of the materials, preparing a complete BoQ is not the focus. However, this code presents the number of materials used for the model at the current stage of design. So if any changes were to be made or if the materials were to be replaced, the code will update the list as soon as it is rerun.

In order to assist with the automation of data exchange, facilitate the exchange of information between programs and avoid errors, the code was connected to an MS Excel spreadsheet so if any changes are to be made in the building's design, the quantities or type of materials the spreadsheet would be updated so long as the code was a rerun. First, the area of elements, including external and internal walls, floors, doors, and windows, was calculated. Then the materials used within the element were summoned, and relative mass units were exported to the linked spreadsheet, as shown in Figures 9 and 10.

Figure 9.

Overview of dynamo code to actively calculate the masses.

Figure 10.

Actively transferring the masses to MS excel.

Conclusion

In this study, we aimed to suggest the direction of a forthcoming study for region-based LCA that focuses more on passive design strategies by making clear the necessity of regional characteristic consideration in BIM-based LCA and how energy demand could be reduced by paying more attention to climate based strategies in designing a building. The research's goal is to help develop a sustainable environment while considering the local and climatic attributes. It is recommended to consider the locally available materials and techniques and on-site climate conditions when designing a building to aim toward a more energy efficient and environmentally friendly structure.

Three prototypes were developed, and energy analysis was conducted on the building. HVAC systems proved to be the most impactful category in this phase. However, the point was to make use of other passive design strategies to save enough energy without needing to change the HVAC system and potentially save enough to be able to reduce the size of the heating-cooling system. By making use of passive design strategies, the annual EUI of the building was reduced by 180, 142, and 46 kWh, respectively, for Quebec, Boston, and Arizona for the first scenario.

A one-on-one comparison of a model before and after adding insulation boards was made to determine the effects of adding insulation in different climates. The EPS layer affected each building differently, and the results were compared. Adding the thermal layer reduced energy consumption by 28%, 20%, and 5%, respectively, for the building in Quebec, Boston, and Arizona.

Five scenarios featuring different design characteristics were developed to observe the energy variations in response to passive design parameters, respectively. It was deduced that adding the same insulation brings about different results depending on the climate. The non-ventilated air gaps in metal-cladding wall systems proved slightly more efficient than adding a new EPS layer. Given the budget-free choice of the non-ventilated air system, it's a more suitable alternative for all three climates.

LCA for the first and second scenarios indicates a total of 3% increase in GHG emissions. Although an EPS layer contributes to more GHG emissions, it can go as far as reducing the energy consumption of the building by more than 20% of its initial amount. However, using the thermal layer isn’t always as beneficial. For the prototype in Arizona, the building's energy consumption underwent minor changes after the thermal layer was added while producing about the same GHG emissions and environmental impacts.

In this research, five scenarios across nine climate zones were investigated. Results show that some climate zones (namely Bsk, Csb, Cfb, Dfa, and Dfc) respond a lot better to energy optimizations through passive design parameters and/or changing the building materials. Meanwhile, some other climate zones (namely Bwh, Am, Aw, and Bsh) aren’t so much affected by the mentioned variations. The studied cases prove that heat-intensive climate zones are less likely to be affected by passive design changes or alternation of materials.

An articulated linkage is established between Revit's programming platform and MS excel to facilitate the exchange of information and materials quantities. This tool and the proposed workflow create an opportunity to compare and improve other building designs and scenarios to find the optimally sustainable choice.

Recommendations and limitations

There are some limitations to the study: Exploring a wider range of materials using available databases could result in more sustainable solutions. Also, the energy consumption results are presented after an annual analysis of the building. In time, the building's performance is bound to drop due to the weathering of the construction characteristics and the efficiency of the building's equipment. How the on-site climate and weather attributes of the said building affect the building's environmental efficiency and maintenance costs over time is beyond the scope of this research. Developing a climate-based database that coordinates the local needs of a building while making use of passive design strategies to lower environmental impacts is recommended.

Committee on publication ethics

All authors contributed equally to the preparation of this manuscript.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Ali Akbar Shirzadi Javid

Sahar Falegari received her MSc degree in engineering and construction management from Iran university of science and technology (IUST) in 2021. She is currently a PhD student in IUST and is working on BIM related subjects and sustainable development issues.

Ali Akbar Shirzadi Javid is associate professor in Iran university of science and technology (IUST). Dr. Shirzadi Javid's principle research interest is concrete's durability and maintenance and recently he has been working extensively on internet of things (IOT) and building information modeling (BIM) oriented subjects.

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