45 In addition to assessing the environmental impacts of the direct flows, LCA will be used to estimate the embodied energy based on a cradle-to-gate boundary. This boundary can be defined as the energy consumed up to the use phase. While this embodied energy can be divided in two parts, initial embodied energy, and recurring embodied energy (Ramesh, Prakash and Shukla, 2010), this study only considered the initial embodied energy and did not consider the entire life cycle of the system studied.
The components of material flow considered to assess embodied energy are water production, energy production, and construction material production. The initial embodied energy (𝐸𝐸𝑖) is expressed as follows:
𝐸𝐸𝑖 = ∑ 𝑚𝑖. 𝑀𝑖 Equation 3.1
Where mi is quantity of the materials studied, and Mi is the energy content.
The embodied energy values will be calculated in SimaPro using the Cumulative Energy Demand (CED) method (also known as primary energy consumption). Using this method will enable determining the energy intensity of energy and material flows understudy (Röhrlich et al., 2000). Further details on estimating the embodied energy for each MFA component are given in Chapter (4).
3.3 Dynamic MFA-LCA approach
The method presented here is related to research questions (3, 4, 5). The MFA-LCA approach presented in section 3.2 quantifies and assess the flows and stocks in a defined system (defined here as the city ecosystem) for a given time. To further the understanding of the city’s ecosystem, this thesis will develop a hybrid Dynamic MFA-LCA method and applied to the housing stock in the city of Riyadh, Saudi Arabia. This step takes advantage of the valuable information provided by the MFA-LCA approach in section 3.2 in that it will shed light on how the system performs over time.
3.3.1 System definition
The model proposed in Figure 3-3, consists of three sub-systems, as indicated in In subsystem (1) Ap represents floor area per capita. The rectangles represent processes, while the oval shape depicts flows and the hexagons show drivers or determinants.
Moreover, the dashed lines show the influence between variables. These variables include:
A: floor area, dA/dt: stock accumulation of floor area, dAin/dt: input flow of floor area, dAout/dt: output flow of floor area, M: materials stock, dM/dt: sock accumulation of materials,
46 dMin/dt: input flow of materials, dMout/dt: output flow of materials. The factors characterised as determinants are population (P), per capita floor area (Ap), and building lifetime.
For sub-system (2), service intensity factors are introduced. The extension of the stock dynamic model here considers energy and water consumption to be related to dwelling stock. The factors used are based on historical per capita values. Using intensity values to estimate resources (e.g., material, energy, water etc.) consumption has been the main method reported in the literature (Lanau et al., 2019). Demand associated with increased stock can be estimated either by per capita (Kapur et al., 2008; Pauliuk, Wang and Daniel B Müller, 2012; Zucaro et al., 2014; Pardo Martínez, 2015), by per square metre of built area (Sandberg and Bratteb, 2012; Sandberg et al., 2016), or per GDP (Xiang, Xu and Sha, 2013;
Fishman, Schandl and Tanikawa, 2015). In this study, an intensity per capita factor was used. Adopting this approach permits evaluation of dwelling stock over time (Fang et al., 2017; Göswein et al., 2019; Lanau et al., 2019).
Figure 3-3 System definition for the integrative approach.
(1): Stock dynamic model, (2): extended service intensity factors, (3): LCA on background processes. Hexagons represent input parameters, rectangles represent processes, ovals represent flows
47 Resource intensity provides measurement of materials and energy needed for the production of a unit of a good or service (Müller, 2006). In this thesis, the intensity-of-use (floor area) is defined as floor area demand per capita.
Data on intensity per square metre is rarely available in the Saudi context. In this study, the main drivers forces of the model are population (P) and lifestyle, as represented by floor area per capita (Ap) and shown in equation (3.2).
A t( )= P t( )A tp( ) Equation (3.2)
To determine the input and output flows for floor area, information about the building’s lifetime needed to be added. There is no data available regarding building lifetime in Saudi Arabia; therefore, a normal distribution is used with the default mean lifetime τ and standard deviation equal to 40 years and 8 years, respectively, as shown in equation (3.3).
2
Building upon data from equation (3.3), the probability that housing units built in year t will be demolished in year t is determined by equation (3.4).
housing and outflow of floor area together. Up to this point, the model deals with inflows and outflows affecting stock changes in floor area.
( ) ( ) ( )
in out
dA t dA t dA t
dt = dt + dt Equation (3.5)
By adding two more parameters, we can estimate corresponding materials and energy.
Equation (3.6) determines the dynamics of corresponding materials (stocks and flows), by introducing a material intensity parameter (Min).
( ) ( )
Equation (3.7) determines material outflow based on the probability that materials entering the system in year t would exit the system as outflow in year t.
Equation (3.8) determines the balance equation for material stock.
( ) in( ) out( )
dM t dM t dM t
dt = dt − dt Equation (3.8)
Similarly, by adding an energy intensity parameter (ei), the corresponding energy flows (E) are determined as shown in equation (3.9) and the same approach is used for the water intensity parameter, as shown in equation (3.10)
( ) ( ) i( )
E t = A t e t Equation (3.9)
As mentioned before, the use of energy intensity per square metre is widely applied in stock dynamic analysis (Pauliuk, Sjöstrand and Daniel B Müller, 2013; Pauliuk and Müller, 2014;
Sandberg, Sartori, Magnus I. Vestrum, et al., 2016, 2017; Vásquez, Amund N Løvik, et al., 2016); however, intensity per capita is the preferred method in cases where detailed data concerning stock is lacking.
( ) ( ) i( )
W t = A t w t Equation (3.10) All data and assumptions related to the model parameters will be presented in chapter 5 along with the results.
For sub-system (3), this study advances the assessment by introducing in-use intensity parameters (electricity use, transportation energy use, and water) and a life cycle perspective assessment of the model considering both these parameters. The aim is to provide a system-based approach to environmentally evaluate the stock and its development to 2050.
LCA is introduced to assess the impacts associated with the flows provided by the city as a way to maintain a good quality of life. The results related to energy and water flows can be obtained from a stock dynamic model for use as an inventory to carry LCA. Applying LCA only provides an effective evaluation of the system at a given point in time, but it can also serve as a basis for determining different scenarios relating to development, policies, and technology to be assessed. The system’s boundary is based on cradle to gate, and the functional unit is foregone in this part of the study. The selection of not including a functional unit is determined by several factors. As stated by Goldstein and others, defining a functional
49 unit for LCA in urban areas is a complex challenge, because cities have different populations with different lifestyles and provide different service qualities (Goldstein et al., 2013). While the functional unit was defined in section 3.2 as per capita, it was intended to serve the objective of conducting the LCA by assessing how the city is impacted by its inhabitants.
Additionally, LCA was described as being used to assess the current status of the city without predicting figures for future development. However, there have been recent calls to advance the LCA methodologies applied to urban centres to afford better guidance for a functional unit for LCA to be applied to cities (Albertí et al., 2017).
On the issue of the impact assessment, ReCiPe 2016, a hierarchical midpoint was chosen to assess climate change, as reported as GWP. The LCA calculation will be performed using SimaPro software.
A note must be made regarding integrating LCA with the stock dynamic model. Several studies have attempted this approach and the majority also applied LCA to quantify GHG in association with upstream activities (Pauliuk, Sjöstrand and Müller, 2013; Pauliuk and Müller, 2014b). However, there is a limitation imposed on this integration. The assessment of background processes (upstream) was based on datasets that are static in nature. As a consequence, these data do not capture changes in processes over time, such as the energy mix.
In Chapter 6, however, different scenarios will be developed based on this model to capture the impact of these changes. This will provide an answer to the research question (6).
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