**Goune Kang 1,\*, Hunhee Cho <sup>2</sup> and Dongyoun Lee <sup>2</sup>**


Received: 17 May 2019; Accepted: 5 July 2019; Published: 8 July 2019

**Abstract:** Understanding the structure and behavior of emissions in building systems is the first step toward improving the reliability of the environmental impact assessment of buildings. The shortcomings of current building lifecycle assessment (LCA) research is the lack of understanding of embodied emissions and static analysis. This study presents a methodology for the dynamic LCA of buildings, combined with the system dynamics technique. Dynamic factors related to recurrent embodied emissions are explored through a literature review. Applying the dynamic factors based on the review, a causal map and stock-flow diagram are invented. Collecting the field data and establishing the assumptions based on the literature, a case study is performed for the proposed model. As a result, through dynamic analysis, it was found that recurrent embodied emissions have a considerably different behavior from static ones during their whole life. Additionally, it was found that the environmental impacts changed by more than 10%, according to the variation of the users' required performance level in sensitivity analysis. This result thoroughly addressed the necessity and appropriateness of dynamic LCA. The dynamic LCA model developed in this study can contribute to the long-term behavioral understanding of the embodied environmental impacts of building LCA.

**Keywords:** lifecycle assessment; recurrent embodied carbon; system dynamics; buildings

#### **1. Introduction**

Buildings are key factors in energy consumption and global warming, consuming as much as 40% of the resources entering the global economy [1]. Life cycle assessment (LCA), which quantifies the environmental impacts during the whole life of a product, is a useful decision-making tool for green buildings [2]. It has been increasingly used by researchers to assist with decision-making for environment-related strategies and to reduce buildings' life cycle environmental impacts for the last 25 years [3]. Geng S. et al. [4] reviewed the literature related to building LCA using bibliometrics and showed that the number of publications related to building LCA grew steadily over the past 15 years and more rapidly since 2010.

Additionally, embodied carbon (EC) has recently become especially crucial for estimating the life-cycle carbon of buildings. EC refers to carbon dioxide emitted during the manufacture, transport, and construction of building materials, compared to operational carbon (OC), which means carbon dioxide emitted from the use of buildings, including heating, cooling, and lighting. In a recent review paper by Anand and Amor [5], numerous building LCA studies were explored, and it was shown that the areas of embodied energy had seen the maximum growth in the most recent years. Nevertheless, Pomponi and Moncaster [6] showed an extremely incomplete and short-sighted approach to life cycle

studies through their meta-analysis of EC. According to the review, most studies only assess the manufacturing stages, often completely overlooking impacts occurring during the occupancy stage or at the end of life of the building. In other words, carbon emissions from construction and maintenance and repair (M&R) recurring during the building usage have been paid little attention so far. They emphasized that the LCA research community has the responsibility to address such shortcomings and work towards more complete and meaningful assessments. Considering the growing M&R proportion in the contemporary construction industry, embracing recurrent EC caused by M&R intervention activities in building lifecycle must not be overlooked.

Another limitation of LCA is that its analytical method is static. Traditional LCA methods are used to conduct building environmental impact assessment, with little consideration of influential factors that vary in time. Because the lifecycle of a building is quite long, such details have a significant influence on the accuracy of evaluation results [7]. Recently, dynamic LCA (DLCA) studies on buildings were conducted. Collinge [8–11] built DLCA research and identified significant time-related changes of variables, then developed a dynamic model based on the general LCA equation. Fouquet et al. [12] used a DLCA method to assess the global warming impact of three low-energy houses over time, considering the future electricity mix and innovation of materials for refurbishment. Su et al. [7] developed a dynamic assessment framework based on LCA principles and identified four dynamic building properties.

Previous building DLCA studies have accounted for economic and social progress (e.g., energy mix and Input–Output matrix) and characterization factors (e.g., global warming potential (GWP)). Su et al. [7] have proposed a DLCA framework that considers resident behavioral dynamics. They deal with the dynamic changes, focusing on the operational impacts, but embodied impacts from repair or replacement were not included. Fouquet et al. [12] added the materials used for refurbishment in the dynamic analysis, but they did not reflect the dynamic behavior in the usage stage, only dealing with the differences in the material types themselves. Overcoming the present limitations, this study developed the DLCA model, covering dynamic factors in recurrent embodied impacts in the usage stage and applying the system dynamics.

The objective of the research is to explore the impact of dynamic factors and their interaction with recurrent EC to obtain a better understanding of the recurrent EC in building LCA. This study utilizes system dynamics methodology for DLCA simulation. System dynamics involves the ability to represent and assess the dynamic complexity of the behavior that arises from the interaction of a factors in a system over time [13]. It is an advantageous modeling technique to reflect circular causality in simulation. Since a feedback loop is investigated while searching dynamic factors, system dynamics is considered a highly suitable method for DLCA simulations in this study. Meanwhile, LCA has defined a systematic set of procedures for compiling and examining the inputs and outputs of materials and energy and the associated environmental impacts, directly attributable to the functioning of a product or service system throughout its life cycle [14]. While this study focuses on the usage phase during the whole building life, the systematic process of the LCA principle is used for the equivalent calculation of embodied carbon dioxide in building systems.

The research procedure is as follows. First, dynamic factors related to recurrent EC are discussed in a literature review. According to condition-based management, intervention causing recurrent embodied emissions is determined by the performance. Several factors that impact the building performance are investigated. Second, the DLCA model for recurrent embodied impacts in the usage stage is developed using system dynamics. Applying the dynamic factors based on the review, a causal map and a stock-flow diagram are invented. The causal map is described for the relationship among the dynamic factors. The stock and flow diagram were invented for the DLCA simulation. Third, collecting the field data and establishing the assumptions based on the literature, a case study is performed to validate the proposed model. A base run is performed, with the optimization benchmarking the guideline data, substituted for real data. The base run simulation result, which displays the DLCA, is compared to the static analysis. Sensitivity analysis for embodied recurrent impacts is also performed, varying according to the change of the dynamic factors. This result can address the necessity and appropriateness of DLCA.

#### **2. Literature Review**

Most global policy has a tendency to focus on reducing OC since the majority of carbon emissions arise from the building usage [15]. However, recent studies have shown the growing significance of EC because much effort has already been invested into reducing OC [16]. Anand and Amor [5] showed that the research related to embodied energy has been significantly increased in recent years. Gavotsis and Moncaster [15] demonstrates that embodied carbon is also a significant proportion of the whole life impacts from buildings through a detailed case study of a low-energy school building. They also discussed about the uncertainties for post-construction stages. Brown et al. [17] pair attention to significant impacts arising from material production for buildings, and evaluated the importance of EC from refurbishment for operational energy reduction. This study displayed that EC for refurbishment actions take considerable share of the reduction of carbon dioxide emissions achieved by the refurbishment. Dixit [18] conducted a systematic survey of literature to identify parameters specifically affecting the recurrent embodied energy of buildings. It emphasized the need to standardize the parameters and quantify their uncertainties by developing appropriate models. Especially, service life, durability, aesthetics fashion, technology change, tenant change, and functional appropriateness factor, which are strongly related to supplied or required performance of building, were found as parameters affecting the recurrent embodied energy.

Several studies dealing with DLCAs have been recently conducted on the environmental impact of buildings. Negishi et al. [19] identified the time-dependent characteristics of a building system for performing DLCA. In this study, degradation of technical performances of building components, replacement and refurbishment with new technology factors were identified as a part of the key dynamic characteristics of building system. Su et al. [20] formalized four identified dynamic assessment elements in their recent study by examining the data transformation pathway in accordance with the standard LCA framework. Dynamics related to recurrent embodied consumption containing maintenance and demolition were included in the DLCA model.

Su et al. [7] organized dynamic factors in building LCA, classifying them into four categories: technological progress, variation in occupancy, dynamic characteristic factors, and dynamic weighting factors. Dynamic characteristic factors, dynamic weighting factors, and technological progress are related to calculation by the impact category. These factors reflect the variation of social change. The characteristic factors for impact categories could change according to the investigated relative impact for the substance. The weighting factors could vary according to the region, environmental issue, or social trend. Technological progress means the variability of the energy mix, which causes variation in the emission factor value. Variation in occupancy means the variability of the energy consumption behavior. Bringing the variation in the occupancy factor into the embodied sector, it can be expressed as a variation in the intervention behavior. Since this study deals only with the greenhouse effect among the impact categories, dynamic weighting factors are not considered in identifying the dynamic model variables. Moreover, this study primarily explores the factors related to a building itself.

#### **3. Dynamic Factors in Recurrent EC**

Since this study focuses on recurrent EC, it is concentrated on the variation in the intervention behavior to explore the dynamic factors. Intervention behavior is a dynamic factor, influenced by the performance of the building. That is, the performance of the building determines the time and amount of intervention. This relationship has been observed in the study of Tarefder & Rahman [21], which developed the lifecycle cost (LCC) model of airport pavement maintenance. In this study, they compared the performance improvement and LCC of the maintenance strategy using two condition

indexes of airport pavement, with system dynamics. The initial condition, minimum acceptable condition, condition rise after maintenance, and deterioration were used as model parameters.

The relationship between performance and intervention also appears in existing asset management models. In the US federal facilities portfolios [22], the performance indicators that are used for maintenance decisions are the Facilities Condition Index (FCI) and the Facilities Rehabilitation Rate (FRR). In this model, if the FCI exceeds the acceptable condition level, intervention is applied. The FRR accounts for the required repairs and upgrades. The acceptable condition level will vary according to the mission, agency, organization and importance of specific facilities.

In addition, intervention behavior may differ from the occupants' characteristic, and likewise, the energy consumption is affected by the occupant behavior in terms of the operational impact. Observing that some home-owners invest heavily in repairs and improvements of their home, but some do not, Leather et al. [23] studied the reason why some occupants delay the maintenance of their home. In their report, different points of view in identifying repair needs, difficulties in finding trustworthy builders, financial problems and several other reasons are revealed as reasons for delayed maintenance. This report clearly showed the effect of occupants' characteristics on housing maintenance. Additionally, several studied the mentioned users' tendency to perform maintenance action in mechanical maintenance. Bitan and Meyer [24] examined users' tendencies to perform preventive maintenance actions. Shavartzon et al. [25] suggested a personalized alert agent for optimal user performance in computing, considering the users' preferences. It is possible to introduce the occupants' tendencies to perform the intervention in the housing maintenance field.

As a result of the literature review, dynamic factors to explain the relationship between intervention behavior and performance are identified: (Initial or current) performance, acceptable performance, deterioration, intervention rate, performance rise, and occupant's tendency. Applying values in a range for the application of several parameters in the existing literature shows the dynamic nature of these factors. This study utilizes these dynamic factors in DLCA modelling.

#### **4. System Dynamics Model for DLCA**

#### *4.1. Causal Map and Feedback Loop*

In the system dynamics methodology, a system may be represented as a causal map [26]. A causal map is a simple map of a system, with all its constituent components and their interactions. By capturing interactions and feedback loops, a causal map reveals the structure of a system. By understanding the structure of a system, it becomes possible to ascertain a system's behavior over a certain time period [27].

Since intervention, which causes recurrent embodied carbon during the building usage, strongly depends on the performance of a building, this study invented a causal map, containing the building performance. There is a simple causal relationship among the performance, intervention, and environmental impact. When the performance of a building deteriorates over time, intervention activities, such as repair or replacement, occur. These interventions generate environmental impacts and lead to a performance improvement. Thus, the performance of the building is recovered. If the building is in a sufficiently good condition, the intervention activity does not occur, and the environmental impact also does not occur. Without intervention for a while, the performance of the building is degraded again, and intervention activities occur again. This process will continue to be repeated during the life of buildings. This relationship is shown in the causal map below (Figure 1).

**Figure 1.** Causal map of building performance and environmental impact.

Especially, the causal map showed the feedback loop among the performance, performance improvement, and intervention. Combining the positive and negative relationships between the variables, the causal map has a negative feedback loop (balancing loop). This means that an increase of a parameter in the feedback loop consequently caused a decrease of itself. For example, a performance increase engenders zero or less intervention, and an intervention decrease causes a performance decrease. This balancing feedback loop suggests that the embodied environmental impact of the usage phase will converge, instead of diverting upward or downward.

Figure 2 displays the causal relationship in the initial construction and intervention. The typical environmental impact calculation process is also presented. The environmental impact is affected by the quantity of construction activity and emission factors, which are calculated by multiples of them. Additionally, recurring environmental impacts from intervention during the usage phase is influenced by the initial construction, because the quantity of materials and activities of intervention is likely to be determined based on the initial one.

**Figure 2.** Causal map of construction activity and environmental impact.

#### *4.2. Stock and Flow Diagram (SFD)*

To perform a more detailed quantitative analysis, a causal map is transformed into an SFD. A stock and flow model aids in the study and analysis of the system in a quantitative way. A stock is the term for any entity that accumulates or depletes over time. A flow is the rate of change in a stock.

Considering the invented causal map, which contains the feedback loop, SFD was developed. In this process, additional parameters are required to answer several questions for the quantitative model: (1) When should intervention activities be applied? (2) How much intervention should be applied? (3) How will performance be improved by intervention activities?

(1) When should intervention activities be applied: ratio of supplied performance (SP) to required performance (RP), and maintenance of strategy strength.

Studies that estimate the existing M&R-related embodied environmental impacts are based on several guidelines that provide the durability of construction materials. The guidelines are based on 'InterNACHI's estimated life expectancy chart [28]' and the 'Study of life expectancy of home components' [29]. Previously, the material life expectancy was provided by the experimental data under the daily conditions, and the latter provides a value based on the data surveyed by the manufacturer. The purpose of these guidelines is mainly to provide a lifetime warranty of home components for residents. Many respondents noted that this lifetime is variable in terms of maintenance levels and emphasized that the lifetime of the component is changed before consumers are satisfied [30]. However, previous studies have deterministically decided the timing of interventions based on a guideline with a fixed lifecycle.

This study deals with the intervention times, as a variable that is determined by the performance. The intervention activity will occur when the supplied performance is lower than the required performance. Executing repair or replacement, the supplied performance can be improved to a level that satisfies the required performance. Therefore, this study uses the ratio of supplied performance to the required performance (SP/RP) as a parameter. If the value is greater than 1, the supply performance is good. If the value is less than 1, the supply performance does not satisfy the required performance.

In addition to the ratio of the supplied performance to the demand performance, this study introduces a parameter that can represent the maintenance strategy strength (λ). The occupants or building managers may want to prevent falling below the required performance through proactive maintenance, although the SP/RP is greater than 1. On the other hand, even if the SP/RP is less than 1, the building may not require immediate intervention to save maintenance costs or for other reasons, unless it is physically dangerous. λ is a parameter representing this occupants' tendency. If λ is greater than 1, a preemptive strategy is adopted. If λ is less than or equal to 1, a cost-effective strategy is adopted. Intervention is performed when SP/RP becomes smaller than λ, as shown below.

#### IF THEN ELSE ("SP/RP" < λ, intervention, 0))

(2) How much intervention should be applied: repairing rate.

According to the Multi-Family Housing Management Act in Korea, the repairing rate is defined as a percentage of the cost of repairs for unpredictable partial damage or failure of a specific part of a building. The Enforcement Decree of the Multi-Family Housing Management Act in Korea proposed the repairing cycle and the repairing rate for each component in the 'Guidelines for a long-term repair program'. These guidelines propose what repairing cost is charged for fixed repairing periods for establishing long-term repairing plans.

The concept of this repairing rate can be used to estimate the extent of repair. The original rate is used to estimate the repairing cost, but in this study, it is used to estimate the quantity. In assessing environmental impacts, the volume of the material is the key factor that determines the amount of activity involved. The intervention quantity can be defined as the product of the initial construction quantity and the repairing rate, as shown below.
