Building life cycle encompasses their system boundary, which defines the processes included in their assessment. While the importance of defining the system boundary has been emphasized in numerous studies, the EN 15978 (EN 2011) standard, widely regarded as the most reliable standard in industry and academia, outlines the following life stages for building projects:
A: The embodied stage, which includes production and construction. B: The operation stage. C: The end-of-life stage.
In addition to these fundamental system boundaries, some studies also investigate the benefits and outcomes beyond the life of materials (D) (Lu et al. 2021).
2.1. Integration of Building Information Modeling (BIM) into life cycle assessment
BIM is a tool that reduces time and effort in managing building data. It can be used for cost and environmental analyses. BIM can be integrated with LCA and LCC in three ways: maintaining inventory lists, exporting models, and incorporating information. LOD 300 is often used for LCA and LCC (Lu et al. 2021). A review paper (Bernardette Soust-Verdaguer, Llatas, and García-Martínez 2017b) critically examined research on integrating Building Information Modeling and Life Cycle Assessment for building applications. Fifteen case studies were analyzed to compare methodologies, results, limitations, and future recommendations. Major challenges identified included a lack of interoperability between software tools, system boundary limitations, and data reliability concerns. Further standardization of data exchange and mapping system boundaries could improve BIM-LCA capabilities.
In another study (Bueno and Fabricio 2018), the authors compared a detailed, manual life cycle assessment following ISO standards versus a simplified LCA conducted using a BIM-LCA plugin tool. The study compared manual LCA with the BIM-LCA plugin for a residential building in Brazil. Manual LCA offered comprehensive impact analysis, while the BIM-LCA plugin was limited. BIM-LCA is helpful for early design but needs further development.
A study by Ajayi et al. (Ajayi et al. 2015) utilized an integrated Building Information Modeling - Life Cycle Assessment approach to compare material specs for a residential building. Results showed the value of combining BIM and LCA early in design. The authors recommend further development of regional LCA data and integration with BIM.
Few researches ((Basbagill et al. 2013; Hollberg, Genova, and Habert 2020; Bernardette Soust-Verdaguer, Llatas, and García-Martínez 2017b)) examined the integration and application of life cycle assessment in building design. Soust-Verdaguer reviewed BIM-LCA integration, Hollberg evaluated the consistency in results between manual life cycle assessment methods versus automated BIM-LCA integration for building design, and Basbagill applied LCA early in design to reduce embodied impacts. The studies highlighted opportunities to leverage LCA to guide design optimization and material selection to improve building sustainability.
2.2. Life Cycle Costs and Assessment
Life cycle cost analysis involves the economic assessment of existing or potential future investments, considering short-term and long-term economic effects. Life cycle costs encompass the costs of an asset or its components throughout its life cycle while meeting performance requirements (ISO 2017). Life cycle cost calculations are used to improve the selection process by creating a reasonable structure regarding the economic performance of a project over its lifetime. Although life cycle costing has a long history since the 1930s, it is a relatively novel tool in sustainability.
It is important to note that life cycle costs differ from total project life costs, with life cycle costs being a part of the overall costs. Typically, life cycle costs are divided into four parts to cover construction costs during their lifetime, including initial costs (construction costs), operation and maintenance costs, replacement costs, and end-of-life costs, which include the value of the building. Total project life costs encompass life cycle costs, externalities, non-construction costs, and revenue (Schau et al. 2011).
Most life cycle cost research focuses on one or two phases, with few considering the entire life cycle. RS Means and Spons are widely used databases, while some use local or market price lists. NPV is the most common method, with discount rates from 2 to 1.6% (Santos et al. 2019). The current value can also be applied if the research duration focuses on a specific life stage (Lu et al. 2021).
Using project life cycle costing methods at the beginning of the project is most effective. Therefore, managers and engineers often explore different options from an economic perspective, focusing on elements and construction methods (W. Li et al. n.d.). In most cases, life cycle costing presents all costs at their present value. The present value of future costs is estimated based on the future inflation rate and a discount rate. Future costs are calculated using Eq. 1 and converted into discounted costs using a specific discount rate defined in Eq. 2. For instance, research conducted by Islam et al. (Islam et al. 2015) in Australia considered a 3% inflation rate, the ten-year average inflation, and a 6% discount rate based on the Australian Manufacturing Industry Organization proposal.
\(FC=PV \times {\left(1+\text{f}\right)}^{n}\) Eq. 1
\(DPV=FC/{\left(1+\text{d}\right)}^{n}\) Eq. 2
FC represents future costs, PV indicates present value, DPV represents discounted present value, f is the inflation rate, d is the discount rate, and n is the years under consideration. Table 2 summarizes the formulas. The first formula accounts for costs at zero points, such as material, labor, and equipment. The second formula relates to annual costs associated with building use, such as energy costs and annual replacement or repair costs for various items. The third formula calculates replacement costs after a specified number of years. The fourth formula represents the current value of the building after the research period (the building's service life), where F represents the residential value of the building after n years in the future.
In a study by Marzuk et al. (M Marzouk, Azab, and Metawie 2016), a system dynamics model was used as a decision-making tool to select green materials for affordable, sustainable housing. The model was combined with the LEED index (Yung, Robotic, and 2014 2014) and a genetic algorithm to optimize life cycle costs. Marzuk et al. (Yung, Robotic, and 2014 2014) developed a framework to determine the timing of affordable housing projects and select the most suitable alternative materials based on their sustainability aspects. The framework was tested on a 5-story building in Badr, Egypt. It was found that achieving 8 out of 11 possible LEED points was less than $2.25 million. Figure 1 shows the effect of higher costs on obtaining more LEED points.
The study concluded that sustainable materials have significantly lower operating costs than traditional materials.
Marzuk et al. (Mohamed Marzouk, Azab, and Metawie 2018) also discussed the use of BIM, genetic algorithms, and Monte Carlo simulation in a study to identify the best and most suitable building materials from economic and environmental perspectives. The environmental aspect was evaluated using the maximum LEED score. The study also examined which buildings were most affected by changes. It is worth mentioning that this article builds upon previous work by utilizing two objective functions, economic and environmental, through a genetic algorithm. Additionally, the article emphasizes the impact of uncertainty on the building system from economic and environmental perspectives. A 3D model of BIM was created, and different materials were assigned to various building components. A sensitivity analysis was conducted to identify the cost components with the most significant impact on the project for each building system.
Life cycle assessment (LCA) utilizes various indicators to evaluate environmental impacts, including carbon gas emissions, energy consumption, acidification potential (AP), eutrophication potential (EP), the abiotic depletion potential of materials (ADPM), human health respiratory effects potential (HHREP), photochemical ozone creation potential (POCP), and ozone depletion potential (ODP). Among these indicators, energy is the most important and commonly used. It is often analyzed through carbon dioxide emissions, global warming potential, greenhouse gas emissions, and carbon footprint. Several databases, such as Athena, the Inventory of Carbon & Energy (ICE), GaBi, and Ecoinvent, are frequently employed in LCA research. Local databases like the Korea Life Cycle Inventory and Belgium EPDs have also been used (Lu et al. 2021).
These three literature reviews (Buyle, Braet, and Audenaert 2013; Cabeza et al. 2014; Sharma et al. 2011) summarize the state of research on applying life cycle assessment to evaluate the environmental impacts of buildings and the construction sector. The reviews covered common LCA goals, scopes, methodologies, impact indicators, findings, limitations, and future directions. Key results highlighted the significance of the use phase and benefits of LCA to inform building design and material selection to improve sustainability.
Apart from life cycle costs, significant attention has been directed towards greenhouse gas production in all industries worldwide, with buildings being a major contributor. In the United States, buildings account for over 39% of total primary energy consumption and greenhouse gas production (Hong et al. n.d.). Consequently, numerous studies have been conducted to assess and mitigate greenhouse gas emissions, with life cycle assessment (LCA) being widely employed. LCA encompasses greenhouse gas emissions and other assessments throughout the process, including product production, handling, assembly, operation, and disposal (Y. S. Shin et al. 2015).
Table 1
summarizes the disparity between life cycle costs and life cycle assessment (Zhao, Huppes, and Van der Voet 2011).
| LCC | LCA |
Decision-making point of view | Investor | supply chain actors |
Goal | For economic evaluation of business decisions and options | To assess the environmental impact of products and processes |
Range | Only direct costs or direct profits from an investment | All stages of the life cycle |
Unit | Currency (eg. Dollar, pound and ...) | Units of mass and energy (eg. Kg, kWh and ...) |
Time | Adjust costs over a period of time to reflect the effect of time using various discounting methods such as net present value. | The effect of time on environmental impacts is usually not considered, especially its decrease or increase over time |
Conclusion | In most researches, the interest rate is effective, but sometimes it is not taken into account | Future environmental effects are usually not factored into results proportionally to time |
2.3. Life Cycle Sustainability Assessment
Life cycle sustainability assessment incorporates sustainability's economic, social, and environmental dimensions. It utilizes three methods: life cycle costs (economic), life cycle assessment (environmental), and social assessment of life cycle costs (social) (K. P. Kim and Park 2018). The social aspect of sustainability assessment encompasses research on indoor and outdoor user comfort, user security, human resources, and the learning process. Security-related analyses contribute significantly to social aspect. Recent research has also explored the benefits of Building Information Modeling (BIM) for preserving and assessing cultural heritage. However, integrating social aspects with economic and environmental aspects has been limited, with individual investigations conducted (Santos et al. 2019).
BIM for sustainability faces integration, sustainability understanding, and library availability challenges. Current research focuses on costs, life cycle assessment, certifications, and sensors (J.-U. Kim et al. 2018).
A study by Alwan et al. (Alwan, Jones, and Holgate 2017) proposed a novel methodology to integrate the Framework for Strategic Sustainable Development (FSSD) principles into Building Information Modeling (BIM) workflows for construction projects. The aim was to guide sustainability transformations in the built environment by bringing strategic planning into BIM and design processes. A case study of a commercial building redevelopment project in the UK was presented. The FSSD principles were used to identify sustainability challenges and develop strategic action plans. BIM offers various tools to model and assess sustainability strategies related to energy, materials, and construction techniques. The integrated FSSD-BIM approach helped develop more sustainable building design concepts than conventional methods. Further validation across more extensive case studies was recommended.
2.4. Combination of Life Cycle Assessment and Life Cycle Costs and Optimization
Life cycle assessment and costing share three common characteristics: 1) their impact is maximized when applied early in the project. 2) They can be applied to a production system encompassing all building elements and construction methods. 3) They provide an analytical platform for selecting the most optimal option by considering economic and environmental resources (Y. S. Shin et al. 2015).
A study by Ferreira et al. (J. Ferreira, Pinheiro, and De Brito 2015) analyzed the economic and environmental savings of adding structural insulation to a residential building in Portugal over its life cycle. The life cycle assessment and life cycle cost analysis determined the optimal insulation thickness that minimized costs and carbon footprint—adding insulation significantly reduced space heating needs and emissions but added material and installation costs. The results provided an optimal insulation level for this building type and climate, demonstrating the importance of life cycle optimization to balance multiple objectives.
Liu et al. (Liu, Meng, and Tam 2015) conducted a study to optimize building design and enhance stability using a beam-based optimization method. The project's life cycle was considered, considering life cycle costs and carbon dioxide gas emissions. The Ecoect Analysis software was utilized to assess the life cycle impacts of the light and heat system. The study incorporated decision-making variables such as wall type, window-to-wall ratio, window glazing type, exterior canopy, and building orientation. The particle swarm optimization algorithm was employed for optimization purposes, as it is known for effectively solving multi-objective optimization problems. A significant aspect of this research was utilizing a work breakdown structure (WBS) to allocate resources and separately analyze direct and indirect costs and carbon emissions for different activities. However, the destruction and maintenance stages were not considered due to incomplete databases and the significant uncertainty associated with material repair and recycling. The proposed method was implemented on a commercial building in Hong Kong, demonstrating its positive impact on both economic and environmental aspects. The processing time remained relatively unchanged despite expanding the investigation range to find the best elements.
In a separate study, Islam et al. (Islam et al. 2015) aimed to find an optimal balance between life cycle costs and environmental impacts in Australian residential buildings. The research focused on two objective functions: project life cycle costs and environmental effects, which were evaluated through life cycle cost and life cycle assessment methods. Alternative options were considered for a basic house with 18 walls and four floors. Linear programming was employed as the optimization algorithm for single and multi-objective functions. Weighting coefficients were also used to compare different scenarios. The study concluded that a house with fixed life cycle costs could have up to 20% less environmental impact. PRé’s SimaPro software (Jrade and Jalaei 2013) was used for life cycle assessment, with the Ecoinvent database (Swiss) and Australian Lifecycle Bank (Akbarnezhad, Ong, and Chandra 2014) serving as the primary data sources. The research investigated four factors related to life cycle assessment: greenhouse gas (GHG) emissions (tCO2-eq), cumulative energy demand (CED) (GJ), water usage (kL), and solid waste generation (tonne). A separate database (Rawlinsons 2015) was utilized to estimate life cycle costs, assuming a building service life of 50 years. The study revealed variations in life cycle greenhouse gas emissions (approximately 20%), cumulative energy demand (approximately 15%), water usage (approximately 26%), waste production (approximately 29%), and costs (approximately 22%) across different scenarios. No single design achieved the lowest life cycle costs and environmental effects, highlighting the need for optimization methods to obtain the best design.
There are three studies focused on combining life cycle cost analysis and optimization to improve the energy efficiency of residential and commercial buildings. Kneifel (Kneifel 2010) developed a method to optimize energy efficiency measures in commercial buildings by minimizing life cycle costs and carbon emissions. Ascione et al. (Ascione et al. 2015) and Gustafsson (Gustafsson 2000) applied optimization techniques to determine cost-effective insulation levels for retrofitting existing buildings, one residential or another office building, to balance insulation costs with heating energy savings over the lifespan. The research demonstrated the value of optimization methods using simulations and life cycle cost models to support cost-effective building energy efficiency.
These two articles (Congedo et al. 2015; M. Ferreira et al. 2016) optimized school and office buildings to achieve net zero energy status. Ferreira compared cost-optimal versus net zero energy retrofits in Portugal. Congedo used optimization to design net zero schools and offices in Italy. The results quantified the costs and challenges of achieving net zero buildings in Mediterranean climates.
In another research endeavor, Sharif et al. (Sharif and Hammad 2019) developed a method to optimize the selection of appropriate renovation strategies for educational buildings. This method considers energy consumption, life cycle costs, and environmental impacts within budget constraints. The study focused on building elements, ventilation systems (Abanda and Byers 2016), and lighting systems. The research model consisted of four main parts: 1) collection of input data, 2) expansion of the database, 3) definition of reconstruction methods, and 4) multi-objective optimization based on simulation. The input data encompassed various parameters required for future calculations, including price limits and insurance models. The first phase involved determining input data and expanding the beam model. Databases containing information on building materials, ventilation systems, lighting systems, and environmental and economic data were utilized. The third phase combined the previous steps' information to generate different reconstruction strategies. This phase included determining energy performance goals, expanding reconstruction strategies, searching databases for the best equivalents, and evaluating the suitability of reconstruction methods for each strategy. The process was repeated until all reconstruction methods were exhausted. The final phase involved using MATLAB software to select the best strategy. Optimization results were presented in Fig. 2, showcasing two functions: total energy consumption and life cycle costs and total energy consumption and life cycle assessment. The left figure demonstrated a more diverse Pareto front. This indicates that using this function for optimization provided a more comprehensive range of options than the right figure, where the Pareto front members were nearly identical.