Probabilistic Life cycle Cost Model for Sustainable Housing Retrofit Decision-Making

Probabilistic Life cycle Cost Model for Sustainable Housing Retrofit Decision-Making JAFARI 1 Amirhosein, VALENTIN 2 Vanessa, RUSSELL 3 Mark 1 Ph.D. Student, Department of Civil Engineering, University of New Mexico, MSC01 1070, Albuquerque, NM 87131, E-mail: Jafari@unm.edu ABSTRACT Although recent researches show that the need for housing green retrofit is increasing, modern building practices show little regard for sustainable efforts. Sustainable housing retrofitting is an effort to convert a house to a low energy facility, to analyze the deconstruction techniques, and to evaluate the alternatives for installing reused/recycled materials. In order to make the decision of both designing and pursuing a green housing refurbishment approach, there is the need to perform a life cycle cost assessment of the alternatives, in order to evaluate how each alternative influences the cost of building, longterm costs (e.g., maintenance, utilities) and environmental impact among others. This study presents a probabilistic model for estimating life cycle costs (LCC) that considers all cost elements throughout the life of the sustainable and ordinary housing refurbishment alternatives. The developed LCC model employs the use of probability theory and Monte- Carlo simulation to evaluate the competing alternatives. The study identifies key elements for the model such as cost elements and factors related to service life of the green alternatives. In addition, the case study of the green retrofit of a house built in 1960 s is presented in order to demonstrate the use of the developed model to prove the economics of sustainable housing retrofitting. Initial result shows that also cost of building may increase if we select the sustainable retrofit approach, but the long-term costs would decrease. So, green housing retrofit, which contributes toward sustainable management of natural resources, would direct to more cost-effective during the life cycle of a project. Keywords: Life cycle Cost (LCC); Sustainability; House Retrofit; Cost Analysis; Green Building. 1

INTRODUCTION The construction industry has a significant impact on the environment. A brief literature review shows that the building sector, including building construction and operation, consumed almost 50% of the total energy each year (Wang et al. 2010) (Kansal and Kadambari 2010). In addition, most of this amount of energy is consumed by a building during its life cycle period (Menassa 2011). Energy costs play an important role in long-term exploitation costs (Gasic et al. 2012). One way to reduce the adverse impacts of buildings on the environment and target the energy efficiency is green building retrofitting. Menassa states that there is ongoing growth in sustainable retrofit of existing building market that is projected to dramatically increase in the next 20-25 years (Menassa 2011). Sustainable housing retrofitting is an effort to convert a house to a low energy facility, to analyze the deconstruction techniques, and to evaluate the alternatives for installing reused/recycled materials. Wang et al. stated that it is vital for the investors to take into consideration the environmental influences of a building through its whole life, that is, from the initial construction process to the future operation stage of the building (Wang et al. 2010). Actually, the capital costs of a building only represents half the total cost during its whole life, and are only slightly higher than the total costs of cleaning and care taking, replacement and maintenance, and routine servicing (Wang et al. 2012). Unfortunately, most public funding decisions are often made on the basis of initial cost and without any consideration of life cycle costs (Arditi and Messiha 1999; Salem et al. 2003). The investors of durable buildings realized that an increased amount of money spent on initial cost can considerably reduce future costs of a building (Wang et al. 2012). So, the expenditure on the design and construction of a green building is more compared to a conventional building. However, the operation cost of green building is less along with the environmental benefits and a better place for the occupants (Kansal and Kadambari 2010). As value for money was repeatedly recorded as client main interest (Wang et al. 2012), it became essential to consider accurate measures and cost control in green retrofit life cycle cost analysis. Life cycle cost (LCC) is an evaluation technique that takes into consideration all costs that emerge during the life cycle of a project and is usually used for the comparison of competing alternative investment strategies (Ammar et al. 2013). Therefore, in order to make the decision of both designing and pursuing a green housing refurbishment approach, there is the need to perform a life cycle cost assessment of the alternatives, in order to evaluate how each alternative influences the cost of building, long-term costs (e.g., maintenance, utilities) and environmental impact among others. The widely used cost estimate method for life cycle cost estimate is still the deterministic model in practice. However, deterministic models cannot model life cycle costs successfully because the uncertainties of the future events affect the estimate of the life cycle cost of buildings (Wang et al. 2012). This study presents a probabilistic model for estimating life cycle costs that considers all cost elements throughout the life of the sustainable and ordinary housing refurbishment alternatives. The developed life cycle cost model employs the use of probability theory and Monte-Carlo simulation to evaluate the competing alternatives. The study identifies key elements for the model such as cost elements and factors related to 2

service life of the green alternatives. In addition, the case study of the green retrofit of a house built in 1960 s is presented in order to demonstrate the use of the developed model to prove the economics of sustainable housing retrofitting. LITERATURE REVIEW AND BACKGROUND Green Building Retrofit. A green building consumes minimum natural resources for its construction and operation throughout its life, in order to conserve the non-renewable resources. It also emphasizes the reuse, recycling and utilization of renewable resources. A green building focuses on increasing the efficiency of use of the resources (Kansal and Kadambari 2010). There are many benefits of green buildings such as: reduced energy consumption, reduced damage to natural, reduced water consumption, limited waste generation due to recycling and reuse, reduced pollution loads, and enhanced image and marketability. Sustainable retrofit is a capital improvement with an associated cost that resets the building life, improves performance, and makes the building s use more predictable for an extended period of time (Menassa 2011). The decision to retrofit existing buildings still presents a number of challenges to the building stakeholders due to: (1) lack of information and benchmarks about the actual performance of the building and its systems after the design phase, (2) reluctant stakeholder commitment because energy prices and, (3) taxes are not high enough to create a strong incentive for retrofits (Beheiry et al. 2006), and (4) perceptions from early green buildings that significantly higher costs outweigh economic and environmental benefits (Menassa 2011). The main obstacles to sustainable retrofits are high construction costs, long pay back periods, and difficulty in quantifying the benefits of green building. Life Cycle Cost Assessment. Life Cycle Cost Analysis (LCCA) is an analytical method of project evaluation in which all costs of the project (i.e., construction, operation, maintenance and disposal) are considered (Kansal and Kadambari 2010). The life cycle costing process includes breaking down building to a measurable and detailed elemental level, to make life cycle assumptions, to calculate replacement cost of each element at each year according to the life cycle assumptions and finally to summarize and generate a life cycle profile over a long period of time (Wang et al. 2012). In LCC analysis, different options are quantified so as to ensure the adoption of the optimum asset configuration (Ammar et al. 2013). The appropriate time to control the life cycle cost of a building is at the initial design stage. At this stage, options are open for consideration and potential savings can be secured, but little information is available (Ammar et al. 2013; Arditi and Messiha 1999; Wang et al. 2012). The determination of time horizon exists in aspects such as the physical, technological and economic life of projects. It depends on the client s expectations and the characteristics of the project (Wang et al. 2012). A number of factors should be taken into consideration when building life cycle cost model, such as utility costs, maintenance costs, and etc. A classification can be in which case three types can be recognized (Gasic et al. 2012): 3

Regular costs, which are evenly distributed in time; Cyclical costs, or periodic costs, that repeat in regular time spans, and; Extraordinary costs, which can but don't need to occur, depending on the need. Most approaches to modeling life cycle costs for civil infrastructure construction and rehabilitation alternatives assume a deterministic behavior for the service life of alternatives, which is not a valid assumption (Ammar et al. 2013; Salem et al. 2003). Because of the uncertainties in cost elements of buildings, the deterministic models cannot model life cycle costs properly. Either overestimating or underestimating the life cycle assumptions are the risks in life cycle costing which may cause the project to be under funded in future (Wang et al. 2012). To deal with such uncertainty, probabilistic techniques are usually used. Previous Research on LCCA. A literature review shows that most of the research in the field of LCCA utilization is devoted to transportation projects, including highways, bridges, and pavements, among others. It also reveals that some theoretical non-deterministic models have been developed for estimating life cycle cost of buildings. Zayed et al. used an economic analysis, which is a deterministic method, and the Markov decision process, which is a stochastic method, to carry out the life cycle cost analysis (Zayed et al. 2002). They applied the analysis to different rehabilitation scenarios were proposed for steel bridge paint. Salem et al. presented a new approach for estimating life cycle costs and evaluating infrastructure rehabilitation and construction alternatives, derived from probability theory and simulation application (Salem et al. 2003). They used highway pavement data to demonstrate the model concept and development; however, the uncertainty associated with costs was not included. Wang et al. applied Monte Carlo simulation method to the Quantitative Risk Assessment of life cycle costing risk management (Wang et al. 2012). They also chose a school project as a case study to demonstrate a new simulation approach to life cycle cost management. Ammar et al. developed a model utilized fuzzy set theory and interval mathematics (Ammar et al. 2013). The authors also presented an example application in order to demonstrate the use of the developed model and to illustrate its essential features. Kansal and Kadambari performed a deterministic life cycle analysis to prove the economics of green buildings versus ordinary buildings (Kansal and Kadambari 2010). Menassa also presented a quantitative approach to determining the value of the investment in sustainable retrofits for existing buildings by taking into account different uncertainties associated with the life cycle costs and perceived benefits of this investment (Menassa 2011). The first step in life cycle cost analysis is to define the cost elements and structure. Each element correlates to several life cycle assumptions such as the replacement cycle, replacement cost and quantity of the element. Every assumption is a variable in life cycle costing; therefore assumption making is the most difficult step in life cycle costing due to the complex cost breakdown structure and uncertainties in predicting future events in the long period of time (Wang et al. 2012). Table 1 summarizes assumed factors in previous studies on life cycle cost analysis on construction projects. 4

Table 1: Cost elements in previous studies Reference Studied Case Approach Elements Building Projects (Bromilow and Pawsey 1987) (Kansal and Kadambari 2010) (Menassa 2011) (Gasic et al. 2012) (Wang et al. 2012) (Ammar al. 2013) et (Chan et al. 2008) (Zongzhi and Madanu 2009) (Amini et al. 2012) (Santos and Ferreira 2013) (Yi et al. 2013) A university building A green building versus an ordinary building Sustainable retrofits for existing buildings Architectural projects A school rehabilitation project Water mains and sewer infrastructure rehabilitation project Highway pavement Highway project Highways (conventional/perpetual pavements) Pavement project Highway project Deterministic Deterministic Financial option pricing method with considering uncertainty Literature search Mont Carlo Simulation Fuzzy set theory Transportation Projects Deterministic Deterministic, Risk-based, and Uncertainty-based approaches Deterministic Deterministic Deterministic and probabilistic methods in economic analysis 5 Replacements cost, Maintenance cost, Cleaning costs, Energy cost, Other cost Initial Cost of Building, Annual Maintenance Cost, Special Repairs, Annual Operation Cost Energy upgrades investment costs, Annual costs of operating and maintaining Utility costs, Maintenance costs, Administration costs, Periodic costs, Taxation costs, Repair and replacement costs, Renovation, alteration, and addition costs, Miscellaneous costs and expenses Replacement cost rates Initial capital costs, Operating and maintenance costs, Disposal cost, Service life of the asset + Discount rate Construction cost and maintenance cost Construction cost, Rehabilitation costs, Resurfacing cost, Routine maintenance cost, Preventive maintenance cost Initial construction costs, maintenance and rehabilitation costs, User costs (Vehicle operating cost and cost value of time), Salvage value Construction costs, Annual maintenance costs, Annual user costs, Deducting the residual value of pavements at the end of the project analysis period Initial costs, Operation & Maintenance costs, Rehabilitation costs, User benefits + Discount Rate There are a few researches that focus on Mont Carlo simulation for life cycle cost assessment. In 2003, Salem et al. introduced Mont Carlo simulation to determine life cycle costs of civil infrastructure construction and rehabilitation alternatives (Salem et al. 2003). Their developed model utilized a risk-based approach to predict probabilities of occurrence of different life cycle costs of constructing/rehabilitating an infrastructure unit.

Infrastructure service life was modeled by fitting statistical distributions to pavement-failure data within each pavement group, testing the goodness of fit, and determining the distribution parameters. The main critic of their works is that they do not consider uncertainty in cost elements. They just focused on distribution of infrastructure service life, with no concern on distribution of cost elements, or replacement and repairs cycles. In 2012, Wang et al. also applied Mont Carlo simulation to life cycle cost analysis (Wang et al. 2012). They used the triangle distribution for each cost elements and model the life cycle cost of a school building using Mont Carlo simulation, then concluded that the life cycle cost has been under estimated by the deterministic model. They also did a sensitivity analysis to identify the cost significant items for the life cycle cost of the building which provides an efficient way to cost control in life cycle analysis; however they just considered replacement cost rates. This study contributes applying Mont Carlo simulation to model the life cycle cost assessment of a housing retrofit project. The model considers uncertainty in all cost elements and replacement periods. It also attempts to optimize the best time for start retrofitting according to life cycle cost of the house. CASE STUDY The University of New Mexico is working with the Associated General Contractors (AGC) of New Mexico and the Central New Mexico Home Builders Association to remodel an existing home as a demonstration project. The project is intended to gather data and demonstrate the effectiveness of various remodeling techniques. The house being studied was originally constructed in 1964 as a ranch home in Albuquerque, New Mexico. Essentially, all of the repairs on the home were intended only to keep the facility habitable and no major energy conserving features have been added. The home is a 1600 square feet 3 bedroom 2 bath concrete block facility constructed on a crawlspace (Figure 1). There is a relatively flat gable roof with a 1:12 pitch. The current heating is by gas furnace and cooling is provided by a swamp cooler system. Figure 1: House layout 6

METHODOLOGY As shown in Fig. 2, the methodology of this research includes a six-step approach: Step1 Step2 Step3 Step4 Step5 Step6 Identifying Retrofitting Activities Estimating the Initial Costs for Each Activity Estimating the Saving Costs for Each Activity Assuming Needed Factors Developing the LCCA Model Analyzing the Results Figure 2: The research methodology Step1: Identifying Retrofitting Activities. To determine the sequence of areas to be retrofitted, this research starts with the basic least expensive items from the building and works up through more complex items to finish with on-site renewable energy systems. The Build Green New Mexico criteria for a Green Building document is used to evaluate the steps that could be taken to renovate the house case. Figure 3 provides a summary of the planned activities for retrofitting of the house. Low Cost Lighting 01.Programmable Thermostat 02.HVAC tune up 03.Replace all lighting with CFLs 04.Replace with Energy Star for Refrigerator Appliances 05.Replace with Energy Star for Clothes washer Planned Retrofitting Activities Insulation Windows & Doors Heating & Cooling Water Heating Renewable Options 06.Replace with Energy Star for Dishwasher 07.Insulate Ceilings 08.Insulate walls 09.Insulate Attic 10.Replace doors with insulated core 11.Replace windows with energy efficient glass 12.Install ground source heat exchanger 13.Evaporative Cooler 14.Solar Thermal 15.Solar electric Figure 3: Planned retrofitting activities 7

Step2: Estimating the Initial Costs for Each Activity. The initial cost of each activity is estimated by the following sources: RS Means: Green Building Cost Data (RSMeans 2012) Housing and Urban Development Website: Energy Efficient Rehab Advisor (HUD 2013) AGC cost estimator expert For each activity, three point of cost is estimated according to the estimation sources. Step3: Estimating the Saving Costs for Each Activity. The annual saving cost of each activity is estimated by the following source: equest (Quick Energy Simulation Tool) software, version 3.65 (which is a software to perform detailed analysis of building design technologies using building energy use simulation techniques) Housing and Urban Development Website: Energy Efficient Rehab Advisor (HUD 2013) Energy Star website (EnergyStar 2013) Step4: Assuming Needed Factors. As the literature shows, there are two main factors in LCCA include: Discount Rate (r): When analyzing long-term public investments, we must compare costs and benefits that occur in different time periods. Therefore, the LCCA process uses an economic technique known as discounting to convert different costs and benefits occurred at different times at a common point in time (Ferreira and Santos 2013). This technique applies a financial variable called discount rate (r) to represent the time value of the money. This rate is not a constant term and may vary over the service life of the project. A discount rate of 2 or 3% above inflation is considered an appropriate value (Hojjat 2002). In this research, a discount rate of 3% is assumed. Service Life (n): expected service life of a building depends on the client s expectations and the characteristics of the project. It may vary from 25 to 50 years (Wang et al. 2012), and also may be expanded to more than 70 years (Ammar et al. 2013; Kansal and Kadambari 2010). In this research, a service life of 50 years is assumed. Step5: Developing the LCCA Model. This study uses Mont Carlo Simulation for LCCA. Monte Carlo simulation uses computing power to explore all of the possible outcomes to a problem given certain bounds of variability expressed in the model (Wang et al. 2012). The main advantage of this method over the deterministic models is it allows the uncertainty and risks during the long-term operation stage of buildings to be involved in cost analyses. In this study the software @RISK, which can help Monte Carlo simulation to be used in the project with a large number of variables, has been employed to carry out the calculations. Many cost items can be usually incurred in construction and/or rehabilitation of a project over its service life: from initial cost to salvage values. In this study, two main items are used in LCCA: Initial Cost (IC): These costs are estimated in 3 points for each activity. In this study, a Pert Distribution is considered for initial cost of each activity. Initial costs do not need to be discounted. 8

Annual utilities bill (AB): Benefits of each activity is decreasing in annual utilities bill. According to estimated energy saving for each activity, the annual utilities bill of the house effecting by each activity can be calculated. In this study, a Pert Distribution is considered for annual utilities bill. All annual bills have to be discounted first as present value as given by Eq. (1): (1) Where PVAB i is present value of the annual bill in year of i, AB i is the current value of the annual bill in year of i, and r is discount rate. Since it is assumed that the annual bills will be same at the end of each year, the total present values of annual bills can be as given by Eq. (2): (2) Where PVAB is total amount of present value of the annual bills in whole service life, n is service life of the project, and AB is the annual bill for utilities. Therefore the whole life cycle cost of the house retrofitting can be calculated as given by Eq. (3): (3) Where LCC is life cycle cost of the retrofitting project, IC is the total initial cost of implemented retrofitting activities, and AB is annual utilities bill according to the implemented activities. Step6: Analyzing the Results. The analyzing the results include 4 main parts as follow: The results of the equest software for energy saving Calculation of payback period for each activity according to initial cost and annual saving Calculating the life cycle cost of the retrofitted and not retrofitted house for 50 years service life. Selecting the best retrofit activities to minimize the life cycle costs for three scenarios of 30, 50, and 70 years for service life of the project. RESULTS AND DISCUSSION equest Software. equest is a user friendly software that step through the creation of a detailed building model, allow to automatically perform parametric simulations of the design alternatives, and provide graphics that compare the performance of the alternatives. equest calculates hour-by-hour building energy consumption over an entire year using hourly weather data for the location under consideration. Input to the program consists of a detailed description of the building being analyzed, including hourly scheduling of occupants, lighting, equipment, and thermostat settings. In this research, the house case is modeled by equest v3.65. in the first step, the house is modeled as it is, without consideration of any retrofitting activities. Figure 4 shows the output of the case house in terms of electricity and natural gas consumption of the building. 9

Figure 4: energy consumption of the house as it is The building has been occupied by a family of three for the last two years. During that time, the annual utility usage has been 9,000 kwh of electricity and 70 MBtu of gas. As the results show, the annual electric and gas consumption of the case house is simulated to be 9,550 KWh and 73.42 MBtu, respectively. Therefore the simulated energy consumption for the home is directly in line with the average actual usage of utilities. On the other hand, figure 5 compare the percentage of energy usage in homes by end users between U.S. average home and the outputs for the case home. A. US Energy Information Admisintration B. Outbut of the equest for Case House 2009 A. US Energy Information Admisintration 2009 2013 17.0% 34.6% 34.6% 41.5% 41.5% 19.4% 49.9% 17.7% 6.2% 17.7% 6.2% 13.7% Space Heating Air Conditioning Water Heating Appliance, Electronics, and Lighting Figure 5: Comparison of energy consumption by end users Considering the unit price of 0.113 $/KWh and 10.6 $/MBtu for the electricity and natural gas, respectively, the annual bill of the case house would be $1857.2. In the next step, each retrofitting activity is implemented into the model and the result on annual bills is 10

evaluated. Payback Periods. After estimating the initial cost and annual saving for each retrofitting activity, the payback period for each activity is calculated. The payback period is the length of time required for an activity to recover its initial costs in terms of saving. Using the Mont Carlo Simulation and @Risk software, the payback period of each activity is calculated, as a distribution. Table 2 summarizes the results of each distribution in terms of the mean value and the interval that payback will occur in probability of 90%. For example by having the activity 7: Insulate Ceiling, the initial investment with 90% probability will come back between 9.2 to 12 years; in average in 10.2 years. Table 2: Summary of the payback periods for each retrofitting activity No. Activity Payback Period (Year) Percentile5 Percentile95 Mean Rank 1 Programmable Thermostat 0.4 1.0 0.6 2 2 HVAC tune up 0.6 0.8 0.7 3 3 Replace all lighting with CFLs 0.2 0.3 0.3 1 4 Replace with Energy Star for Refrigerator 14.8 103.2 42.9 11 5 Replace with Energy Star for Clothes washer 7.9 23.5 14.5 7 6 Replace with Energy Star for Dishwasher 27.9 133.7 64.5 14 7 Insulate Ceilings 9.2 12.0 10.2 5 8 Insulate walls 7.9 33.0 17.4 8 9 Insulate Attic 8.8 13.5 11.1 6 10 Replace doors with insulated core 31.5 145.6 74.7 15 11 Replace windows with energy efficient glass 43.0 97.7 63.7 13 12 Install ground source heat exchanger 30.3 41.6 35.4 10 13 Evaporative Cooler 4.9 8.6 6.7 4 14 Solar Thermal 21.5 50.1 32.4 9 15 Solar electric 47.6 62.4 54.4 12 Calculating the life cycle costs. In this section, the life cycle cost of the project (including initial retrofitting costs and utilities costs) for 50 years of service life of the building is evaluated. To compare the two alternatives, the evaluation options include: The case house as it is (the initial cost is zero but the utilities bill is in maximum amount) The case house with implementing all retrofitting activities (the initial cost is in maximum but the utilities bill is zero) The results are shown in Figure 6. As the result shows, the mean of LCC of the case house for not having the retrofitting and having the retrofitting is equal to $46,988 and $63,852, respectively. Therefore having the all retrofit activities is not cost effective at all. Furthermore, as the result of the energy consumption shows, the retrofit house can make more energy than it needs buy implementing all retrofit activities. Although the main purpose of retrofit a house is decreasing the LCC, the results show that having too much investment for retrofitting can increase the LCC. Therefore it is the responsibility of a decision maker to select the best combination of the retrofit activities to minimize the LCC of a project. In the next section, an approach to select the best combination of activities for retrofitting is presented. 11

Figure 6: The LCC for the case house (left) as it is (right) with having the retrofitting Decision Making for Retrofitting. In the section, an approach to minimizing the life cycle cost base on the selection of the best combination of the retrofit option is presented. Three scenarios include service life of 30, 50, and 70 years applied to the model to determine the best options for retrofitting the case house. The optimization approach includes the following steps: A binary variable is defined for each activity (called decision variable) If the decision variable is 0 for an activity that means there is no initial cost and no effect on energy consumption of the house. If the decision variable is 0 for an activity that means the initial cost of the activity and its effect on annual utilities bill are considered in LCC. The objective function for the optimization is total LCC that has to be Minimum. Using @Risk optimizer, the minimum value for LCC can be reached by considering the decision variables as adjustable cells. After finding the minimum value for the LCC, the activities with decision variables of 1 would be our best combinations of retrofitting the case house. Figure 7 shows the results of optimizing the LCC for service life of 50 years. The result illustrates that the average LCC for retrofit cost would be $27,134, if the best combination of retrofit activities are selected. The optimum LCC for retrofit house is too less than having all retrofit activities ($63,852) and having no retrofit activities ($46,988). Table 3 summarizes the results of LCC optimization for different scenarios. The final results show that by having the best combination of activities to retrofit the house, the LCC would decrease to more than 40%. In other words, by selecting the best retrofit options, not only the amount of emissions and energy consumption decrease, but also the LCC of the building decreases significantly. 12

3.5 25.04 29.29 5.0% 90.0% 5.0% 3.0 2.5 2.0 1.5 1.0 Total Cost Minimum 22800.3754 Maximum 31626.3178 Mean 27134.2868 Std Dev 1289.3570 Values 5000 0.5 0.0 Service Life (year) Values in Thousands Figure 7: The optimum LCC for service life of 50 years Table 3: The summary of the LCC optimization for different scenarios Number of LCC / Average Scenario Retrofit Selected Activities Numbers Service Life LCC ($) Activities ($/Year) 30 Optimum 6 1,2,3,7,9,13 22,914 763 no Retrofit 0-46,988 940 50 Optimum 9 1,2,3,5,7,8,9,13,14 27,134 543 All Retrofit 15 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15 63,852 1277 70 Optimum 9 1,2,3,5,6,7,8,13,14 29218 417 RESEARCH LIMITATION This research tries to introduce an approach to determine and optimize the LCC for retrofit houses and how the best alternative for the retrofitting a house can be selected. This research may have some limitation as follow: The cost elements for house retrofitting could be more than initial cost and annual bill savings. For example maintenance costs, applicable rebates, and tax incentives could be considered as another cost items. The annual utility bill also has to include the water consumption. Discount rates may not be a constant value during the service life of the project. All in all, the purpose of this research was only the introduction of the LCC approach. The justification of the approach with a complete cost element could be studied in later research by the author. CONCLUSION This study presented a probabilistic approach to estimate life cycle costs (LCC) that of the sustainable and ordinary housing retrofitting alternatives. The developed LCC model employed the use of probability theory and Monte-Carlo simulation to evaluate the 13

competing alternatives. A case study of the green retrofit of a house built in 1960 s is presented in order to demonstrate the use of the developed model to prove the economics of sustainable housing retrofitting. This study used two softwares: equest and @Risk to direct its purpose. The results showed that the three best activities to retrofit that have the less payback periods are: Replace all lighting with CFLs, Programmable Thermostat, and HVAC tune up. The results also shows that although the main purpose of retrofit a house is decreasing the LCC, having too much investment for retrofitting can increase the LCC. Therefore it is the responsibility of a decision maker to select the best combination of the retrofit activities to minimize the LCC of a project. The results also showed that by having the best combination of activities to retrofit the house, the LCC would decrease to more than 40%. In other words, by selecting the best retrofit options, not only the amount of emissions and energy consumption decrease, but also the LCC of the building decreases significantly. REFERENCES Amini, A. A., Mashayekhi, M., Ziari, H., and Nobakht, S. (2012). "Life cycle cost comparison of highways with perpetual and conventional pavements." International Journal of Pavement Engineering, 13(6), 553-568. Ammar, M., Zayed, T., and Moselhi, O. (2013). "Fuzzy-Based Life-Cycle Cost Model for Decision Making under Subjectivity." Journal of Construction Engineering and Management, 139(5), 556-563. Arditi, D., and Messiha, H. M. (1999). "Life Cycle Cost Analysis (LCCA) in Municipal Organizations." Journal of Infrastructure Systems, 5(1), 1. Beheiry, S. M. A., Wai Kiong, C., and Haas, C. T. (2006). "Examining the Business Impact of Owner Commitment to Sustainability." Journal of Construction Engineering & Management, 132(4), 384-392. Bromilow, F. J., and Pawsey, M. R. (1987). "Life cycle cost of university buildings." Construction Management & Economics, 5(4), S3. Chan, A., Keoleian, G., and Gabler, E. (2008). "Evaluation of Life-Cycle Cost Analysis Practices Used by the Michigan Department of Transportation." Journal of Transportation Engineering, 134(6), 236-245. EnergyStar (2013). "Home Improvement: Improve Your Home's Energy Efficiency with ENERGY STAR." <http://www.energystar.gov/index.cfm?c=home_improvement.hm_improvement_in dex&s=mega>. Ferreira, A., and Santos, J. (2013). "Life-cycle cost analysis system for pavement management at project level: sensitivity analysis to the discount rate." International Journal of Pavement Engineering, 14(7), 655-673. Gasic, M., Pejanovic, M., and Jurenic, T. (2012). "Life cycle cost elements of the architectural projects." TTEM- Technics Technologies Education Management, 7(1), 227-236. Hojjat, A. (2002). "Life-cycle cost optimization of steel structures." International Journal 14

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