Life Cycle Cost Assessment and Retrofit in Community Scale: a Case Study of Jordan

. In the last few years, the renovation and refurbishment of existing buildings have been recognized as one of the main strategies to achieve energy efficiency and sustainability goals. However, the current studies have mainly focused on the retrofit, life cycle assessment (LCA), and Life Cycle Cost (LCC) of buildings in isolation without envisaging the impact of microclimate and the surrounding buildings on the outcome of energy simulation. Specifically, many energy simulation software needs to include the environmental responses when buildings are treated with outdoor conditions based on weather data from the nearest metrological site. Therefore, this study aims to investigate the impact of microclimate on retrofit and LCC of a community of buildings rather than a single isolated building. For this purpose, a coupling method is developed to integrate building energy simulation (BES) and computational fluid dynamics (CFD), which exchange parameters on a dynamic time step basis using Envi-met to create weather files from microclimate parameters and use it on energy simulation through DesignBuilder software. Furthermore, this study interlinks the life cycle cost assessment and retrofit strategies on a community scale. A case study of Amman – Jordan, is selected in this paper by one a residential building with two floors and an area of 450 . (cid:2)(cid:3) (cid:3)ℎ(cid:5) (cid:6)(cid:7)(cid:8)(cid:5) (cid:3)(cid:9)(cid:8)(cid:5), the retrofit strategy is considered as implementing green roofs for community areas, which are implemented in the context around the buildings. In addition, this study calculates the net present value and the pack period regarding the life cycle cost study. The initial result shows that there is an impact for microclimate parameters on the temperatures gained on the building's envelope as a result of the effect of airflow through context parameters, which in turn affect the value of energy consumption used for cooling inside the buildings. Moreover, this paper demonstrates that using green roofs on one of the neighbourhood buildings will decrease energy consumption by 28% in the simulated time while the payback period is 9.5 years.


Introduction
Urban regions continually expand in size and population, creating new challenges in areas from transportation and energy consumption to demographics and climate change [1]. One of the most important aspects is the increase in energy demands, which has resulted in issues such as energy poverty, environmental degradation, and people's increased sensitivity to these phenomena [2], affecting the resilience of urban systems and social well-being [1].
As a result, energy efficiency in the building sector has received considerable attention, as it is responsible for over % of the world's total energy usage. Building retrofitting is the most viable and costeffective means of increasing building energy efficiency [3]. Although existing buildings account for most global energy end-use, many studies concentrate on the energysaving potential of future construction projects accounting for around 40% of global energy consumption [4].
Retrofitting is one of the methods used to reduce cooling and heating loads used to reduce energy consumption resulting from changes in community areas or due to the building's geographical location [5].
Retrofit can be at the macro level of the urban area and its elements as a whole block or at the level of the microscale and its effect on the energy consumption of the building, which is the aspect that is focused on this research by taking into consideration the factors from the external environment of the building.
Moreover, Retrofit strategies are applied based on available energy bills at the building's history or through a simulation of energy consumption inside buildings through some programs such as Green Building Studio, eQUEST, IES +VE, and Energy Plus, which can simulate the building from conceptual through the life cycle of the building [6]. On the other hand, all of these methods mentioned above do not take into account all microclimate variables such as buildings, plants, water bodies, natural surfaces, soils, and pollution particles are not taken into account by the methods given above to simulate the microclimate. Furthermore, they lack sufficient model resolution from the macro to the micro scales to determine the boundary conditions of all surfaces affecting the microclimate.
Studies show that seven urban scenarios investigate various technical options involving buildings and surrounding areas, such as material physical properties (albedo), urban fabric morphology, mitigating elements E3S Web of Conferences 396, 04012 (2023) https://doi.org/10.1051/e3sconf/202339604012 IAQVEC2023 such as water bodies and vegetation, and integrated green systems such as green walls and roofs [7]. Hence, this would significantly contribute to UHI mitigation, reduced indoor air temperature, and reduced cooling energy demand [7,8]. Furthermore, the ability of rainfall storage and retention is one of the essential features of the green roof system, especially in Jordan because it considers one of the poorest countries in terms of water resources, which is regarded as one of the biggest problems within the country in addition to energy [9].
Versini et al. [10] demonstrated that green roofs help minimize the dangers of sewage overflow. They concluded that one hectare of a green roof can retain 4500 ݉ ଷ of water per year (representing 450 ݉݉ of precipitation).
Recent technological advancements have increased the flexibility and speed of construction and green roof, allowing them to be integrated into most projects. Modern green roofs are often made up of multiple layers. Depending on the region and the requirements, they may include vegetation, growth substrate, filter fabric, drainage element, root barrier, insulation, and waterproofing membrane [11]. Green roofs are characterized as intense or extensive based on the thickness of the substrate layer. Green roofs with thin substrate layers (15 ܿ݉) are classified as extensive green roofs. However, if the substrate is thicker(20 − 200 ܿ݉) , they are classified as intense green roofs. These layers improve the insulation capability of a typical roof by reducing heat input into the building [12].
Although green roofs have been shown to provide environmental benefits throughout the lifespan of a building, current studies are mainly focused on potential energy savings. As a result, building owners, managers, and researchers need to consider the materials and construction costs associated with implementing energy upgrades, as these factors can significantly impact the success and efficiency of the process. Scolaro and others [13]state that life cycle cost and life cycle cost-benefit are used to evaluate the economic feasibility of green roofs. The life cycle cost approach addresses total life cycle expenditures. The life cycle cost-benefit mixes the life cycle cost approach with cost-benefit analysis, which compares life cycle positive and negative cashflows. The original investment was included in the analysis in the papers on the economic method assessed. Almost all evaluated maintenance and operating costs (such as watering plants, fertilizing and adding growing medium). According to [14], construction is the most expensive phase, 94%, and maintenance is around 5% of the total cost.
As previously mentioned and supported by previous research, the environmental advantages and energy savings provided by green roofs have been examined [9,11,13,15]. However, more attention should be given to reviewing the financial dimension and effects on microclimate variables such as Urban Heat Islands. Therefore, this study explores community buildings' life cycle costs and cost-benefit, considering microclimate parameters like outdoor air temperature and wind speed. In addition, this research also utilizes cost-benefit and life cycle cost analysis to determine the energy savings achieved and the financial feasibility of implementing green roof systems.
For this purpose, this research establishes a coupling framework for the two most advanced macro and micro level simulation models, ENVI-met as a CFD tool and DesignBuilder, as one of the building energy simulation tools for energy simulation. The study uses ENVI-met v4.0, a predictive 3D climate simulation program used in this case study, to evaluate the accuracy of simulated and observed microclimate data. The integrated CFD approach considers longwave and shortwave radiation fluxes, plant transpiration and evaporation, and air movement within the model [16]. In addition, the study employs DesignBuilder to simulate the whole building's energy usage using the weather data generated by ENVI-met. The Jordan-Amman case study is used in this study for one of the residential buildings in a highdensity neighbourhood where green roofs are used as one retrofit strategy for community buildings. Therefore, the life cycle cost for implementing green roofs on one building within the neighbourhood is applied, collected as the energy bills for this building and compared before and after using the retrofit strategy on the microclimate. Figure 1 shows the workflow of research, which starts from data collection for (neighbourhood plans, weather files, energy bills, green roof resources and prices), and the proposed approach combines weather data exported from Envi-met with DesignBuilder to simulate various scenarios. The coupling method results were then used to compare the energy consumption before and after retrofitting. Finally, the resulting energy savings data was used to calculate the life cycle cost of the strategy employed.

Methodology and Tools
The dynamic coupling strategy shown above was the modelling-based approach combining the microclimate and the building energy model since no holistic model can simulate the abovementioned aspects. Also, the coupling method has been implemented using DesignBuilder as a BES and Envi-met as a CFD simulation program. DesignBuilder and Envi-met were integrated through the Weather Converter EnergyPlus tool. Weather Converter is an EnergyPlus tool to read standard weather service file types such as SAMSON and newer 'typical year' weather files such as TMY2, WYEC2, and IWEC. Moreover, the tool translates and extends specific weather data into different formats. For example, the weather data used in this paper was for a temperate climate zone [17]. Therefore, the form used was Typical Meteorological Year (TMY) 2.
The coupling method for models and tools was done in four stages as follows (see Figure 2): x Stage 1: Running the microclimate simulations using ENVI-met and then averaging the results using LEONARDO (a built-in ENVI-met tool). x Stage 2: Using the averaged output from ENVI-met to create a modified (custom) EPW file using weather converter tools from EnergyPlus. x Stage 3: Run the building energy simulations in the DesignBuilder simulation engine using the custom weather file created in Stage 2. x Stage 4: Calculate the cost-benefit and the life cycle cost by comparing the data for both scenarios (before and after retrofitting.

Cost-benefit and Life Cycle Cost (LCC)
Green roofs are a popular retrofit strategy for buildings in many neighbourhoods, as they can significantly impact outdoor air temperature, energy consumption, and indoor air temperature. This paper evaluates the potential energy bill savings, operation costs, payback period and present worth value of green roofs in Jordan.
The most popular green roof in Jordan is the extensive green roof with a 30-40 cm thickness. The total cost for such a system is approximately 19.5 Jordanian Dinars (28 USD). The greenest layer typically used is Sedum, and the life span of this type of roof is 40 years, according to Wong's publication [24]. However, the studies indicate that clients should expect 20-25 years of life span from their green roofs regarding climate conditions in Amman [15].

Energy saving
Energy savings (ES) were determined as the difference between existing energy consumption (EC Existing) and retrofitted green roof (EC Retrofitting) system. To compute energy savings, the following equation is used:

Bills Saving
Bill savings (BS) were computed by multiplying energy savings by the electricity rate (ET). According to Jordan Petroleum Refinery and Jordanian Electric Power, the electricity tariff is 0.2 JD (0.28$). The following equation may be used to compute this:

Operation Cost
The operating cost (OC) is the cost of a new retrofit system, which is the total roof area (ARoof) multiplied by the price of a green roof per m2 (GRPrice) plus the maintenance cost (MC), which is evaluated as 5% by previous studies that mentioned above in the introduction, and this can be calculated by the following equation:

Payback Period
The payback period (PBP) is the time required to return the increased investment (increment cost or operation cost) in efficiency improvement through decreased operating expenses. The PBP is the ratio of the incremental or operation cost (from the baseline to the more efficient product) to the decrease in the annual bills saving ratio, calculated over 12 months. If PBP exceeds the product's lifetime, the increased purchase price is not recovered in reduced operating cost. To compute PBP, the following equation is employed:   It is the benefit from applying the retrofit system, which is calculated by the ratio of minimising the lifespan of the green roof with the Payback Period (PBP) to the Bills Saving (BS) by the following equation:

Energy Model Performance
Expanding passive houses, NetZero, and retrofit buildings improve the quality of energy models and, as a result, the applicability of energy conservation measures. As a result, assessing the accuracy of building energy models is crucial because the model can only be utilised when confirmed through a calibration technique. ASHRAE Guideline 14-2014, the International Performance Measurement and Verification Protocol (IPMVP), and the Federal Energy Management Program are among the organisations that have produced rules and procedures to establish a measurement of the accuracy of these models (FEMP). Therefore, NMBE and CV(RMSE) are used as a method to validate the energy model regarding the study of Ramos and German [25]: Based on these equations, the calibration criteria for different guidelines should be within the range shown in Table 1; this research considered ASHRAE 14 guidelines as a reference for the validation purpose. In the case of NMBE, positive and negative values mean under or above the prediction of normalization.

Case Study
Jordan combines the climate of the Mediterranean basin and the desert climate, where the climate of the Mediterranean basin prevails in the northern and western parts of the country. In contrast, the desert climate prevails in most of the country. Jordan is a subtropical region with hot and dry summers and mild winters. Throughout the year, clear skies predominate, with light to gloomy skies and soft, gentle rainfall in the winter. The average time spent in direct sunlight each day is roughly eight hours. Jordan's climate is characterized by a contrast between a somewhat wet season from December to April and extended periods of dry weather throughout the rest of the year. The area has an extended summer, with the hottest month being August and the coldest month being January [18].
Jordan needs more indigenous energy resources and must rely on imported gas and oil from other countries. According to the Ministry of Energy and natural resources, Jordan imported over 97% of its energy needs in 2015, causing a financial load on the national economy [19]. In the previous 15 years, energy consumption has quadrupled and is predicted to continue at the same rate [19][20][21]. Furthermore, the residential sector accounts for most energy consumption, accounting for up to 24% of total energy consumed and around 42% of the total electricity used in the country [20].
In Amman, Jordan's capital, a multi-family apartment complex accounts for 73% of the total housing stock and more than 80% of all buildings [22]. As a result, the emphasis of this research is on multifamily structures in Amman, Jordan. The purpose of choosing the neighbourhood of Dahyat al Istiqlal in Amman is to compare the values obtained from the microclimate simulation (Envi-met) with the meteorological station near the area (Amman Airport) which is far away, about 4 km from the location of this region.

Microclimate modelling
ENVI-met simulation is based on spatial and temporal model resolutions. It can simulate spatially between 0.5 ݉ and 10 ݉ per grid resolution with time increments of less than ‫ݏ01‬ [6]. This study adopted a grid of 40 × 40 × 30 cells with a spatial discretization of 2݉ × 2݉ for the horizontal plane and a vertical height of 2݉ for each cell. Open lateral boundary conditions were also applied. The ArabiaWeather database was used to define the statistically significant pattern of climatic variables representative of a typical summer day identified as 26th July for 2016 as available data for Amman Civil Airport [23], while the nonforcing mode option was adopted to determine the hourly profile of outdoor variables. To account for the dynamic effect given by the thermal inertia of the soil and greenery and the effects of buildings and outdoor pavements, simulations were performed over a 25ℎ cycle, starting at 0:00 and ending at 24:00 with the parameters shown in Table 2.
To assess the decrease in cooling energy demand associated with UHI mitigation strategies, an entire oneday climatic file was created using ENVI-met models, including 24 hourly ambient temperature values, wind speed and relative humidity. This climatic file was then used as input for simulating cooling load and energy consumption in a residential building selected as a typical building.  components and the operational schedules were also determined from occupants' interviews.
It is important to note that for both simulation runs (before and after retrofit), the configuration of the building's geometry, along with the modelling parameters and operational schedules, remained the same; the parametric analysis and different simulation results were attributed to the use of additional hourly weather files (before and after green roofs application) as an input parameter in DesignBuilder model. Table 2. Envi-Met input setup parameters. Figure 3 illustrates the two-dimensional and threedimensional views of a selected neighbourhood in Amman-Jordan, and the building to which life cycle cost (LCC) was applied. The model was built with specific heights and as-built neighbourhoods after observing building location, heights, empty lands, urban features, pavements, trees, and street width.

Building energy modelling
Base case residential building was selected from one building in the community area for which information is available. The plan of the generic building unit and the respective 3D model, introduced in the DesignBuilder tool, are shown in Figure 4. The generic unit is designed as a multi-storey building on flat land with two floors and a balcony in the south facade exposed to direct sunlight, while two buildings surround the side facades.
The construction, cooling and heating design were based on observation and interviews with the owners. Finally, the construction period of the building was before 1990. Thus thermal protection was considered for the building components due to needing to implement the national thermal codes at that time. The construction materials, mainly reinforced concrete, brick elements, and stone, along with the dimensions of the building

Retrofit scenarios
The retrofit strategy (green roofs) was applied to all buildings within the neighbourhood to evaluate the impact. The building had a total area of 450 ݉ ଶ which is 227 ݉ ଶ for the ground floor, 205 ݉ ଶ for the first floor, and the rest is Balcony covered. Windows are single-glazed and transparent. The total amount of glass in the North, East and South facades was estimated to be between 25% and 30% of the total wall area. There is no solar protection for the facades. The energy bills were collected, and the activity behaviours were discussed in this case study through the owners' interviews.
To address the different details from the base case, Table 3 lists the general description of the sample building and some properties of the construction material used as well as these details were set on the energy model, which simulated using DesignBuilder v.7. Moreover, details of occupant's behaviour, HVAC (cooling and heating), construction and lighting were set as it is shown in Table 3.  Table 3. Base case criteria applied on energy model.

Effects of green roofs on the Average Daily Air Temperature
This section compares hourly air temperature and wind speed for two scenarios before and after the retrofit. Figure 5 shows the values exported from the meteorological station. As mentioned, the site-specific weather datasets are based on the ENVI-met simulation results. Therefore, they reflect the microclimatic conditions in the two-floor building unit of the case study area before and after the implementation of green roofs on the top of the buildings within the community of the case study building. At the same time, the conductivity and aged U-values are considered.
The model is simulated on the hottest day for 24 hours between the 26th and 27th of July 2016. Figure 5 compares outdoor air temperature data obtained from the weather station and exported air temperature values simulated by Envi-met in the same area, considering the environmental context using Envi-Met Leonardo. In addition, the outdoor air temperature in the same area is compared before and after retrofitting.
In the base scenario, six receptors are simulated using the simple model in Leonardo: air temperature, leaf temperature, relative humidity, pressure, wind speed, and diffuse radiation. After many Envimet simulation outputs, the outdoor air temperature and wind speed significantly impact weather files change.
The highest value for the outdoor temperature is 32.4℃ before the retrofit. At the same time, it became 31.4℃ after the retrofit strategy was applied, and this value was at 16:00. However, there is a clear difference between simulated data and measured data from the weather station. For instance, the highest temperature was 33℃ at 13:00 for the measured data and 31.1℃ for the simulated simultaneously.
Also, the same difference is applied to the lowest temperature as it was 22.8℃ at 6:00 AM for the measured data, while it was 23.7 ℃ at the same time for the simulated data. So that means a difference of around 5% between the weather file measured in the metrological station and simulated data.
The lowest temperature for simulated data before retrofit is 23.7℃, and 23.0℃ after retrofit simultaneously. So that means the percentage of fixing the outdoor temperature is around 3.5%.
Regarding the second affected receptor, there is a vast difference between simulated and measured data on wind speed. The reason is that the measured data is taken in an open space and at 10݉ height. In contrast, the Envi-met export data simulate the natural context with different heights, considering surrounding blocks' orientation and heights.
Among different scenarios for the wind speed factor, it is found that the highest wind speed value before retrofit is ‫ݏ݉85.1‬ and decreases to 1.15 ݉ ‫ݏ‬ ⁄ . After the retrofit is applied to the community area, this gap leads to a change of 27% of the difference in wind speed, which is a significant impact.
In terms of comparing simulated data in the current situation and when green roofs are used as a retrofit strategy, the outdoor air temperature should be examined to explore the impacts of the retrofit strategy in fixing the outdoor temperature. For this purpose, Figure 6 shows the differences between air temperature, wind speed, and wind direction at 2:00 p.m. on a simulated day.
Finally, the retrofit strategy impact results are not constant for both scenarios regarding the terrain (height) of the cutting line. Additionally, the parameters changed regarding the height of the cutting plane on the Envimet. Table 4 illustrates the different wind speed and outdoor air temperature values for the same scenario on a simulated day at 14:00, considering the height or terrain.

The impact of green roofs on energy consumption
The simulation output of the annual energy consumption of the investigated building unit, both for the existing conditions and after the application of the green roof, is depicted in Figure 5. Applying green roofs leads to improvement in the albedo value and the emissivity. The DesignBuilder accounts capacity, which is 1.1 and 1.9, respectively. After conducting energy simulations to assess the effectiveness of implementing green roofs as a retrofitted solution, it was found that annual energy consumption decreased by 11% from 15,587 to 13,942 ܹ݇ℎ. Additionally, on the simulation day (26th July), energy consumption decreased by 28% from 60.4 to 51ܹ݇ℎ, and cooling demand decreased by the same percentage from 43 to 30 ܹ݇ℎ Figure 7. The annual energy consumption for the case study was selected for two scenarios that DesignBuilder simulated.  The LCC is the equipment's total, including operation cost and maintenance. From the results above, the retrofit can achieve its beneficial goals, and the payback period will be after 9.5 years as the cost-benefit during the lifetime of the green roofs will be 150%.

Conclusion
This study employed a coupled simulation methodology by coupling the microclimate CFD model (Envimet), DesignBuilder building energy model, and the climate data to Amman-Jordan case study. This study aims to simulate the microclimate change induced by adding green roofs as a retrofit strategy for community areas and its impact on energy consumption for residential buildings. It also evaluates the costbenefit and the payback period in using this strategy to retrofit the community buildings.
Based on the computational model on the hottest day during the year and after fixing the outdoor air temperature, the result from applying the framework shows how building indoor energy consumption and thermal performance interacts with specific outdoor microclimates. Firstly, the air temperature of outdoor microclimate difference between measured and simulated data by Envimet and its contrast by adding green roofs on the top of the buildings as a retrofit strategy. Second, a selected green roof strategy under the proposed framework reduces 11% of annual energy  https://doi.org/10.1051/e3sconf/202339604012 IAQVEC2023 consumption and 28% of daily cooling demand in the chosen period. Third, the study shows the cost-benefit from applying the strategy under the proposed framework with a contribution to get a worth value one and half times the total cost with consideration for the total cost.
For future studies, the authors intend to conduct further simulation studies into climate change impacts on the community areas and emphasise the carbon dioxide ratio as a parameter to study the life cycle assessment. In addition, further validation studies are required to improve the simulation framework by comparing the simulation results with outdoor and indoor field measurements.