Probabilistic private cost-benefit analysis for green roof installation: A Monte Carlo simulation approach

Accepted Manuscript Title: Probabilistic Private Cost-Benefit Analysis For Green Roof Installation: A Monte Carlo Simulation Approach Author: Amir Mahdiyar PhD Student Sanaz Tabatabaee PhD Student Aidin Nobahar Sadeghifam PhD Student Saeed Reza Mohandes Master of Science Arham Abdullah Assoc. Prof. Dr. Mahdi Moharrami Meynagh PhD Student PII: DOI: Reference:

S1618-8667(16)30270-9 http://dx.doi.org/doi:10.1016/j.ufug.2016.10.001 UFUG 25787

To appear in: Received date: Revised date: Accepted date:

1-7-2016 3-10-2016 4-10-2016

Please cite this article as: Mahdiyar , Amir, Tabatabaee, Sanaz, Sadeghifam, Aidin Nobahar, Mohandes, Saeed Reza, Abdullah, Arham, Meynagh, Mahdi Moharrami, Probabilistic Private Cost-Benefit Analysis For Green Roof Installation: A Monte Carlo Simulation Approach.Urban Forestry and Urban Greening http://dx.doi.org/10.1016/j.ufug.2016.10.001 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Probabilistic Private Cost-Benefit Analysis For Green Roof Installation:

A Monte Carlo

Simulation Approach

Amir Mahdiyar*a, Sanaz Tabatabaeeb, Aidin Nobahar Sadeghifamc, Saeed Reza Mohandesd, Arham Abdullahe, Mahdi Moharrami Meynaghf, ,

a

PhD Student - Department of Structure and Materials, Faculty of Civil Engineering, Universiti Tekknologi Malaysia, 81310, Johor, Malaysia. Email: [email protected] Tel: (+60)11-12132721 b

PhD Student - Department of Structure and Materials, Faculty of Civil Engineering, Universiti Tekknologi Malaysia, 81310, Johor, Malaysia. Email: [email protected] Tel: (+60)11-12134858 c

PhD Student - Department of Structure and Materials, Faculty of Civil Engineering, Universiti Tekknologi Malaysia, 81310, Johor, Malaysia. Email: [email protected] Tel: (+60)1121659626 d

Master of Science - Department of Structure and Materials, Faculty of Civil Engineering, Universiti Tekknologi Malaysia, 81310, Johor, Malaysia. Email: [email protected] Tel: (+60)17-6408939 e

Assoc. Prof. Dr. - Department of Structure and Materials, Faculty of Civil Engineering, Universiti Tekknologi Malaysia, 81310, Johor, Malaysia. Email: [email protected] Tel: (+60)19-7528000 f

PhD Student - Department of Structure and Materials, Faculty of Civil Engineering, Universiti Tekknologi Malaysia, 81310, Johor, Malaysia. Email: Email: [email protected] Tel: (+60)107059897

*Corresponding author: [email protected]



Highlights Noise reduction is an influential factor in cost-benefit analysis of green roof installation.



Monte Carlo simulation is used to consider green roofs’ uncertainties.



Probability of loss in installing intensive green roof is more than extensive one.



Most probable payback period of extensive green roof is more than three years.



NPV of green roof installation in developing and developed countries is very different.

Abstract Green roofs are known as one of the environmentally-friendly applications and also as a sustainable approach in developing countries. Although many researchers have proven the environmental benefits of installing green roofs all around the world, they have not been used widely in many countries due to the lack of knowledge about cost-benefit issues. This paper places an emphasis on all the private factors 1

affecting cost-benefit analysis. Installation, operation and maintenance costs are compared with the benefits such as energy saving, the increase in property value, and the acoustic effect in order to determine two indicators namely “net present value” and “pay-back period,” using the Monte Carlo simulation. Two scenarios are considered in the analyses: using the property, and selling the property after construction. Moreover, correlation and regression sensitivity analyses are also conducted. The capital of Malaysia, Kuala Lumpur, is selected for the case study due to the lack of cost-benefit analysis in developing countries. The results show that there is low probability of loss in the installation of both types of green roofs during their lifespans. Moreover, net present value for intensive green roofs is found to be higher than extensive ones, whereas the payback period for installing extensive green roofs is lower than intensive green roofs. It is concluded that the probability of loss for the owner is higher than that of benefit in the scenario of selling the property after construction resulting from the installation of both types of green roofs.

Key words: green roof; cost-benefit analysis; Monte Carlo simulation, net present value; payback period

1 Introduction Sustainable approaches in construction industry are known to make a significant contribution to the purpose of reusing and recycling materials, energy saving, and reducing emissions in order to alleviate the resultant adverse impacts on the environment created by the construction industry (Akadiri et al., 2013; Alyami et al., 2013; Lundholm and Peck, 2008). Green roofs have been introduced as the environmentally friendly approach to inspire sustainable construction (Bianchini and Hewage, 2012a), and carry a large number of environmental benefits (Berardi et al., 2014; Ondono et al., 2016; Simmons et al., 2008). However, there are still some barriers in many countries like Australia, Hong Kong, and Malaysia in installing green roofs (Rahman et al., 2013; Williams et al., 2010; Zhang et al., 2012). Additional initial costs and the maintenance-related costs are stated as the major barriers for green roof installation (Wong et al., 2003). There are two different classifications for green roofs: a) intensive and extensive green roof, and b) built-in-place versus modular (Morgan et al., 2013). Intensive green roofs show remarkable similarities to roof gardens; they need adequate and reasonable depth of soil and also require a constant maintenance during their entire lifespans. However, extensive green roofs consist of a relatively thin layer of soil in comparison to the other types (Czemiel Berndtsson, 2010). Furthermore, they are designed in such a way 2

to be virtually self-sustaining for which high maintenance is not required (Dvorak, 2009). Private benefits and costs for the installation of green roofs vary along with the types; however, all these types provide positive environmental benefits (Mahdiyar et al., 2015). Storm water retention (Dunnett et al., 2008), mitigation of urban heat island (Susca et al., 2011), increasing the property value (Jim and Peng, 2012), and providing recreational spaces (Ascione et al., 2013) are some of the benefits of green roof installation. Green roofs have been implemented in many countries with different economic and climatic circumstances (La Roche and Berardi, 2014), and a number of studies have been investigated into economic impacts of installing green roofs. Clark et al. (2008) demonstrated that Net Present Value (NPV) for a conventional roof is between 20% and 25% more than the extensive green roof during its lifespan (over 40 years). Carter and Keeler (2008) collected data during an experimental study for green roof in order to conduct a cost-benefit analysis. The NPV of extensive green roofs in their study indicates that this type of green roof is more expensive than the conventional one ranging from 10% to 14%. Consequently, they concluded that a 20% reduction in initial cost is necessary to consider this type of green roof as an economic-feasible construction practice. Bianchini and Hewage (2012b) assessed the costs and benefits involved in personal and social sectors in installing green roofs. The results from their study indicate that by installing any type of green roof, both private and social sectors are at a lower-risk investment, and green roofs are a personal investment. They also found that the social benefits of green roofs play an important role in obtaining the results. Indeed, considering social costs and benefits directly affects the decision making related to this investment. Niu et al. (2010) aimed investigating into the scale of environmental benefits of green roof installation, from the range of private buildings to the city scale using the financial NPV model. Sproul et al. (2014) conducted an economic comparison between white roof and green roofs, and they found that white roofs provide more net savings for the owner; however, green roof might be preferable due to its environmental benefits. Blackhurst et al. (2010) focused on the beneficial aspects of green roofs such as reduction in building cooling load, storm water runoff, carbon dioxide, air pollutants and mitigation of UHI. They found that green roofs are not cost-effective in private sectors; nevertheless, adding the social sector makes it more cost-efficient. Although many studies have focused on green roofs for developed countries, there is a lack of costoriented study as regards the green roof installation for the private sector in developing countries. Regarding the differences in economic factors between developed and developing countries (Di Vita, 2008), remarkable differences are expected to be embedded in the results of cost-benefit analysis for installing green roofs for these types of countries. Additionally, the economic benefit of sound isolation of green roof installation is not being affected in previous cost-benefit studies of installing green roofs. The aim of this paper is to bring out the probabilistic cost-benefit analysis of installing green roofs focusing on the private

3

sector. In this paper, analyses are conducted to calculate NPV and the payback period for installing green roofs in Kuala Lumpur considering two scenarios. The conclusions are based on the assessments of the probability of reaping benefits or incurring losses during the lifespan of each type of green roof. 2 Factors Affecting Cost-Benefit Analysis of Installing Green Roof for Private Sector There are many effective factors in cost-benefit analysis of green roof installation; however, the effectiveness of each factor depends on many criteria such as climate, economic circumstances, government policies and so on (Berardi et al., 2014). Malaysia as a developing country with a high potential of implementing green roof (Rahman et al., 2013), has an equatorial climate with uniformly high temperatures, high humidity, relatively light winds, and abundant rainfall throughout the year (Makaremi et al., 2012). These environmental specifications and governmental policies cause some economic benefits or drawbacks in terms of installing green roofs. For instance, extensive and intensive green roofs are capable of stormwater retention from 30%-60% (Ayub et al., 2015), and 17%-48% (Musa et al., 2008), respectively. This could be regarded as a significant benefit of the rainy weather prolonged in Kuala Lumpur, capital of Malaysia. However, it is a social benefit, and there is an apparent lack of reference to the private benefit of storm water management in Kuala Lumpur. Moreover, green roof installation is considered as an effective application to gain recognition and certification through Green Building Index (GBI) (Fauzi et al., 2013; Zahir et al., 2014). It is worth mentioning that 100% costs of green roofs are claimable, if the building obtain GBI certificate (“Green Building Index,” 2016). However, there are myriad of criteria that should be met to obtain GBI certificate. Finally, all the influential factors in cost-benefit analysis of green roof installation in Kuala Lumpur are discussed in detail in the following sections. Moreover, in order to make it globally understandable, all the cost-related values are converted from Malaysian ringgit (RM) to US Dollar ($), as 1$ ≈ 4RM. 2.1

Initial Cost Installation of each type of green roof needs much more capital than the conventional roof (Carter and

Keeler, 2008). Wong et al. (2003) demonstrated that additional structural support is not required for installing extensive green roofs, and as far as installing intensive green roofs are concerned, required additional costs depend on the type of vegetation and additional roof deck structural support. In a study, Sproul et al. (2014) considered $172/m2 incurred on installing extensive green roofs as the median installation cost of the green roof projects surveyed in their report. In another study, Berardi (2016) distributed a survey to green roof companies in Canada, and stated that the cost of extensive green roof installation is between around $107/m2 and $122/m2 for individual plug plants compared to pre-planted

4

sedum mats for which a bit more amount is required. Moreover, Castleton et al. (2010) reviewed the costs of retrofitting the buildings with green roofs in UK. According to the references that have been reviewed in their study, the costs of green roof installation is between $56/m2 and $202/m2. They also concluded that the reasonable estimate to retrofit a building with an extensive green roof is around $168/m2. Additionally, in retrofitting the buildings with green roofs, the variations of cost of green roof installation highly depend on whether the roof is needed to be re-waterproofed or the waterproofing layer of the old roof is in a good condition (Castleton et al., 2010). On the other hand, the differences between the initial costs of green roof installation incurred by the owners in different countries could be due to the differences in labor cost, the production and manufacturing location of the materials, and the economic incentives. As reported by previous researchers (e.g. Berardi (2016) and Bianchini and Hewage (2012b)), in some cities, economic incentives (tax reduction) reduce the initial costs of green roof installation for the owners. These incentives differ from a city to another one. For instance, in Toronto the incentive is $75/m2, while in New York is $48/m2. In terms of Asian countries, according to Peng and Jim (2015), the cost of extensive green roof installation in Hong Kong is around $64/m2; however, for China, Manila, and Singapore it is around $28/m2, $37/m2, and $80/m2, respectively (Rahman et al., 2013). In Kuala Lumpur, Rahman et al. (2013) demonstrated that the minimum and maximum costs during the installation of extensive green roofs are between $75/m2 and $100/m2. Additionally, in terms of intensive green roof, it is more than $100/m2. It is worth mentioning that incentive to install green roofs has not yet provided by the government of Malaysia. Moreover, a survey among green roof companies in Kuala Lumpur allowed the authors to establish that the maximum amount for installing an intensive green roof could be up to $240/m2. Consequently, as owing to the fact that the minimum cost of intensive green roof installation is $100/m2, this paper considers the cost of intensive green roof installation between $100/m2 and $240/m2. 2.2

Property Value According to Jim and Peng (2012), buildings with maintained green roofs can increase the property

sale value. There are some studies in Canada and the U.S. that considered the benefit of green roof installation between 2% and 20% in terms of increase in property value for both types of green roofs (Bianchini and Hewage, 2012b; Johnston and Newton, 2004). However, there is limited literature concerning the potential benefits involved in the green roof installations in increasing property value on buildings in Kuala Lumpur. With this in mind, it is noteworthy that the results of study conducted by Noor et al. (2015) in Kuala Lumpur showed that, green spaces increase the property value between 3%-12%.

5

They also mentioned that the amount of increase in property value depends on the size and the distance of the green space from the property. According to the greater impact and wider range of intensive green roof in comparison with extensive green roof in terms of increasing the property value, this study assumes that extensive and intensive green roof increases the property value from 3-6% and 6-12%, respectively. Moreover, According to the value of houses in Kuala Lumpur (“House Prices in Malaysia,” 2015), the value of property is between $750/m2 and $1500/m2. As such, in this paper, the increase in property value for extensive green roof is regarded between $22.5/m2 and $90/m2; while for intensive green roof, it is between $45/m2 and $180/m2. 2.3

Energy Saving Green roof is a dynamic system, and the function of green roofs is subject to change according to the

depth of soil (Refahi and Talkhabi, 2015), weather condition (Getter and Rowe, 2008), substrate and vegetation type (Nardini et al., 2011; Yaghoobian and Srebric, 2015) and building characteristics such as roof-to-envelope ratio (Martens et al., 2008). There are many studies conducted in terms of green roof energy saving, and the results show the high potential of green roofs in energy saving in hot and cold climates (Castleton et al., 2010; Jaffal et al., 2012; Karachaliou et al., 2015; Tsang and Jim, 2011; Yang et al., 2015). For instance, it is mentioned that green roofs are capable of saving energy in summer; ranging from 10% to 80% (Jim and Peng, 2012). However, the potential of green roofs in terms of cooling and heating capabilities should be assessed under local climate conditions (Ghaffarianhoseini et al., 2015; La Roche and Berardi, 2014; Yang and Wang, 2014). Investigating into energy saving by installing green roofs in Malaysia is still in its early stage (Ismail et al., 2012). However, there are some references mentioned that green roofs are capable of energy saving in Malaysia (e.g. Rahman et al. (2015, 2013)). The study conducted by Rumana and Mohd Hamdan (2009) in Malaysia, showed that the reduction of indoor ceiling surface temperature of a green roof was up to 3°C compared to the bare roof. Ghaffarianhoseini et al. (2015) discussed the importance of greenery in unshaded courtyards in Kuala Lumpur, Malaysia. They concluded from the results of simulations that the use of vegetation such as grass (similar to extensive type of green roofs) for covering the courtyard provides a limited influence towards improving the thermal comfort; however, the use of trees in courtyards (similar to extensive type of green roofs) can enhance the overall thermal comfort. Moreover, The results of an experimental study in Singapore whose climate is hot and humid show that the thermal performance of extensive green roofs provides a considerable reduction in the energy consumption of air-conditioning (Hien et al., 2011).

6

Wong et al. (2003) conducted an energy-related study on green roofs in Singapore. They analyzed different green roofs in a tropical weather, which is same as Malaysia's climate, with different types of vegetation and with different thickness of soil. They concluded that extensive and intensive green roofs are able to reduce the annual cooling load from 17-47% and 47%-79%, respectively. Furthermore, the energy consumption simulations of flat roofs were conducted by the Building Sector Energy Efficiency Project (BSEEP), based on different hours of air-conditioning in a day, and also most common types of roofs in Malaysia (Tang and Chin, 2013). The results showed that the energy consumption for air-conditioning in Malaysia is between 124 kWh/m² and 552 kWh/m². According to the tariff of the electricity bill in Malaysia, the minimum and maximum prices are $0.054/kWh and $0.142/kWh, respectively (“Pricing & Tariffs,” 2016). Consequently, in terms of the reduction in energy consumption, extensive green roofs can save between $1.13/m2 and $36.84/m2 annually; while these amounts for intensive green roof are between $3.14/m2 and $61.92/m2. 2.4

Lifespan The results of a survey among professionals in Malaysia show the benefit of increase in roof’s lifespan

by green roof installation (Rahman et al., 2013). Although the lifespan of green roofs is expected to be more than 40 years in most of the research carried out, researchers have reported different lifespans for green roofs. For instance, Peri et al. (2012) mentioned that the longevity of a green roof in Europe is twice more than the conventional roof. Kosareo and Ries (2007) reported 45 years as the lifetime of green roofs. Moreover, Clark et al. (2008) stated that the longevity of green roof is up to 50 years, In another study, Bianchini and Hewage (2012b) considered 55 years as the maximum lifetime of green roofs. As the maximum lifetime of green roofs in the most previous studies is considered between 45 and 55 years, the average amount of 50 years is given to the green roofs in this research. Furthermore, the lifespan of conventional green roof is around 20 years for three most commonly practiced roofing systems in Malaysia; concrete flat roofs, light-weight pitched roofs, and lightweight pitched roofs over concrete flat roofs (Tang and Chin, 2013). According to The Government of Malaysia (2015), construction costs of building is between $700/m2 and $880/m2, while the cost of re-roofing in Malaysia is around 3% of the total cost of building construction (“Ideal Roofing,” 2013). As a result, this study considered the benefit of the increase in lifespan of the roof between $21/m2 and $26.4/m2 for every 20 years. 2.5

Operation and Maintenance (O&M) Cost of maintenance highly depends on the type of green roof, the vegetation used (Peri et al., 2012),

and labor costs. Extensive green roofs are well-known for the thin soil layers, small plants and fully covered

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vegetation on the roofs (Cook-Patton and Bauerle, 2012), and designers expect the extensive green roofs to be maintenance free, but some fertilization is regularly recommended for the commercial products (McIntyre and Snodgrass, 2010). Maintenance cost of intensive green roof is considered usually much higher than that of extensive green roof according to the cost-benefit analyses studies. For instance, Townshend (2007) stated that the O&M costs of intensive green roof might be up to 20 times more than extensive green roof. According to the lower labor cost in Malaysia, the O&M cost is also cheaper in comparison with the other countries like Singapore. In accordance with to the survey distributed to green roof companies in Kuala Lumpur, in this paper O&M costs are considered to be annually between $0.1/m2 and $0.25/m2 for installing extensive green roof, and between $0.80/m2 and $6.5/m2 for installing intensive green roofs. 2.6

Acoustic Effect It has been proven by many researchers that installing green roof results in noise reduction (e.g.

(Czemiel Berndtsson, 2010; Seok et al., 2012; Wong et al., 2010)). This benefit of green roof can be fully achieved when the property is exposed to the roads, airports, or noisy areas. There are lots of studies conducted on noise reduction in different cases through analyzing the effect of green roof installation. Van Renterghem and Botteldooren (2009) investigated the effects of green roof on noise reduction in a road on the basis of different vehicles. The results showed that green roofs are able to reduce the noise caused by the road traffic, while the higher the traffic speed of light vehicles is, the better performance of green roof would be. In another study, Van Renterghem and Botteldooren (2011) conducted in-situ measurements of sound propagating over flat extensive green roofs for five cases. From the measurements taken, they stated as a conclusion that green roofs may lead to consistent and significant sound reduction at locations where only diffracted sound waves arrive. Yang et al. (2012) systematically examined the acoustic effects of designable parameters, in particular for diffracted sound waves. The results of the effect of green roof area suggest that generally sound pressure level attenuation is increased gradually in line with the increased number of rows of the green roof trays, although there are variations due to the geometrical effect. Moreover, Connelly and Hodgson (2013) confirmed that the vegetated roofs increase the sound transmission loss of roof systems. In another study, Van Renterghem and Botteldooren (2014) mentioned that similar to any porous material, the acoustic performance of the green roof's substrate could suffer from the presence of water. However, it was concluded that the impact of the water content in an extensive green roof substrate for road traffic noise abatement is expected to be limited. The various layers used for each type of green roofs help in reducing the noise, and needless to say, the capability of noise reduction depends on soil depth and types of vegetation (Van Renterghem and 8

Botteldooren, 2009). Peck and Kuhn (1999) mentioned that the extensive green roofs can reduce the noise around 40 decibels and for intensive green roofs, this amount is between 46 and 50 decibels. Moreover, according to Van Renterghem and Botteldooren (2008), a good overall efficiency for extensive green roofs would be achieved near the maximum layer thickness (15–20 cm), and it is around 10dB. They found that if the thickness of a substrate layer exceeds 20 cm, which is a common practice for an intensive green roof, expected positive results could not come to fruition. Moreover, Lagtrom (2004) conducted a study on noise reduction in extensive green roofs. The results from the study proved that extensive green roofs are able to reduce the noise from 5-20 dB. In another study, Connelly and Hodgson (Connelly and Hodgson, 2008) showed that extensive green roofs are capable of noise reduction from 5-21dB within frequency range of 50-4000 kHz. In order to calculate the benefit of noise reduction in green roof installation in Kuala Lumpur, the performances of three practical acoustic ceiling panels are reviewed; thermalton, polytone, and cineplextone. According to Acoustic Ceiling Panel (2016), the minimum and maximum noise reduction within frequency range of 125-4000 kHz is 4dB and 20dB, respectively. These reductions are very similar to the capability of green roofs in sound reduction. As a result, by installing any type of green roof, the residents of the property can gain the same benefit from installing acoustic panel. Moreover, as stated by the Public Works Department of Malaysia, the price of an acoustic ceiling panel is between $11/m2 and $19/m2 (The Government of Malaysia, 2015). Consequently, in this paper, the minimum and maximum benefit of installing each type of green roof is considered to be between $11/m2 and $19/m2 in turn. 2.7

Inflation and Discount Rates Discount rate is the economic factor that may play a critical role in cost- benefit analysis. There are

many theoretical reasons prove that there is a significant difference between the amount of discount rates in developed and developing countries (Di Vita, 2008). Generally, developed countries apply a lower discount rate between 2% and 8%. For instance, Bianchini and Hewage (2012b) considered the discount rate between 1% and 4% in Canada, while Wong et al. (2003) utilized the value of 5.15 %. According to Hallegatte (2014), the discount rate in developing nations is around 8% to15%, and as a result, these amounts are considered in the cost-benefit analysis of this paper. Moreover, the inflation rate is another effective economic factor in long-term financial analyses and announces from the governments in each country annually. According to similar studies (e.g. (Bianchini and Hewage, 2012b; N. H. Wong et al., 2003)), the minimum and maximum of inflation rates during the previous decade need to be taken into account in determining the inflation rate. In light of this, inflation

9

rates between 0.6% and 5.4%, based on the pertinent data released by the Malaysian government (“Malaysia Inflation,” 2015), are taken into consideration in this paper. Table1-3 show the inputs used in cost-benefit analysis for this paper. 3 Monte Carlo Modelling to Simulate Range of NPV and Payback Period

3.1

Background Monte Carlo (MC) technique, which is known as probability simulation, is a computerized technique

to figure out the effect of uncertainties in estimating models. MC simulation is widely being exploited by researchers in a wide range of areas comprising finance, project management, energy, manufacturing, engineering, research and development, and risk management (Amigun et al., 2011; Armaghani et al., 2016; Gurgur and Jones, 2010; Marmidis et al., 2008). Wang (2014) used Monte Carlo simulation to develop a framework for radiative heat exchange in street canyons with shade trees. Moreover, Bianchini and Hewage (2012b) utilized this technique for social cost-benefit analysis of three types of green roofs. There are two important aims in conducting MC simulation, firstly, to determine the uncertainties and variability quantitatively in simulation exposure of probability, and secondly, to investigate the range of model results, and major agents of the variability and uncertainties considering relative contribution of them to the overall variance (US EPA Technical Panel, 1997). Conventional estimating techniques were capable of estimating fixed values; however, the inputs and the outputs of a MC simulation are ranges of values. In MC simulation, in order to calculate the outputs, a random value is selected based on the range of the value and its distribution functions for each variable. This randomly selection method of variables continues for more than 1000 times (based on the number of sample size that is selected), and the results are recorded. These results are of paramount importance for calculating the probability and ranges of the outputs (Ghasemi et al., 2012).

3.2

Development of MC Simulation MC simulation in this study is used to analyze NPV and Payback period concerning green roof

installation. Net present value is a practical measure and a widely-used indicator for financial feasibility studies. The mathematical formula for NPV is as follow: 𝐶

𝑡 NPV= ∑𝑇𝑡=1 (1+𝑟) 𝑡 − 𝐶0

(Eq. 1)

where, Ct is net cash flow during the period t, C0 is total initial investment costs, r is the real discount rate, and t is the number of time periods. As the inflation rate is effective in cost-benefit analysis, real discount rate is applied in NPV formula. Equation 2 shows how the real discount rate is calculated. 10

Real discount rate =

1+𝑛𝑜𝑚𝑖𝑛𝑎𝑙 𝑑𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑟𝑎𝑡𝑒 1+𝑖𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛 𝑟𝑎𝑡𝑒

–1

(Eq. 2)

Moreover, the cumulative net cash flow for each period is calculated and then the following formula for the payback period is used. 𝐵 𝐶

Payback period = A+

(Eq. 3)

where A is the last period with a negative cumulative cash flow, B is the absolute value of cumulative cash flow at the end of the period A, and C is the total cash flow during the period after A (Jan, 2014). All the factors affecting the calculation vary and depend on many conditions. For instance, the initial cost of installing each type of green roof varies according to the type of vegetation and material used for drainage as quoted by Bianchini and Hewage (2012b); however, the amount of energy savings by each type of green roof highly depend on climatic circumstances, type of vegetation and so on (Berardi et al., 2014). As a result, all these factors must be considered precisely for the specified region. Considering the differences in specifications of each project, a range of amounts has to be allocated to all the factors. These ranges show a minimum and a maximum amount of each variable in order to be considered as the inputs of the simulation. Moreover, in estimating the expected values in predictive models, it’s a common to consider a series of assumptions related to the problem (Armaghani et al., 2016), especially, when the range of data is unknown, considering historical data in a specific field results in an acceptable estimation (Solver, 2015). In the stochastic model, based on the available data, all of the model inputs (initial cost, property value, tax reduction, avoided storm water in drainage system, energy saving, lifespan, maintenance, acoustic effect, inflation and discount rates) are considered as continuous probability distributions (CPD). Table 1 and Table 2 present the probability distribution functions of the input variables used in the MC simulation. Risk Solver Platform is a MS-ExcelTM add-in feature, used in this study as a powerful software for MC simulation. In MC simulation, there are two different sampling schemes: Latin Hypercube sampling and simple random sampling. In Latin Hypercube sampling, the exact lower and upper values in a range are used for distribution. Moreover, fewer simulations are required in Latin Hypercube sampling to generate the same level of precision in comparison with simple random sampling (Liu, 2014). Consequently, as it is stated that Latin Hypercube sampling is a preferred sampling scheme (e.g., U.S. EPA Technical Panel 1997), this study utilized this sampling scheme. Furthermore, to ensure that all the random combinations of the values are selected, 5000 trials are performed. The analysis of this paper as illustrated in Fig. 1, is based on the owner’s decision, and it is conducted through two different scenarios. The first scenario is based on using the property by the first owner, which means the owner decides to install a green roof and uses the property up to the green roof lifespan. In this scenario, all the costs and benefits are considered except the benefit of the increase in property value. In the 11

second scenario, the owner is aimed to sell the property after finishing construction. Consequently, those benefits and costs are applicable to only one-time effect. To estimate NPV and payback period stochastically, the below-mentioned steps are performed: 1.

The financial data were collected from previous related studies.

2.

The distribution functions were assigned to the factors through Risk Solver Platform Software.

3.

Two different models were developed based on the scenarios of the study.

4.

To achieve a statistical representation for NPV and payback period, 5,000 simulations of MC were performed in the spreadsheet model.

4 Result and Discussion 4.1

First Scenario: Using the Property Data analyses indicate the potential economic advantages and drawbacks of installing green roofs (per

m2). As shown in Fig. 2, with 86% probability, the NPV of installing extensive green roof results in benefit; however, the probability of loss for intensive green roof is around 24%. Hence, the results show that the most probable benefit for installing extensive green roof and intensive green roof is $173/m2, and $213/m2, respectively. Furthermore, with 90% confidence, the NPV of extensive green roof installation is less than $374/m2, while this amount for intensive green roof is $562/m2. The minimum NPV for extensive green roof is $-75/m2, even though the maximum NPV could be up to $899/m2. Moreover, the minimum and maximum NPV for intensive green roof is $-265/m2 and $1451/m2, respectively From the financial perspective, installing extensive green roofs is a lower-risk investment in comparison with intensive green roof for the owners based on the differences in the results of the probability of benefit and loss. Furthermore, in order to calculate the payback period, the cash flows are calculated regardless of discount and inflation rates. The most probable amounts of cash flows for extensive green roof at the end of third and fourth year are -$16/m2 and $2/m2, respectively. The negative amount of cash flow in the third year and the positive amount in the fourth year shows that the payback period of installing extensive green roofs is around three years and 11 months. However, for intensive green roof, the most probable cash flow is $-12/m2 in 5th years and $16/m2 in 6th year. Consequently, as the values of the 5th and 6th year are negative and positive in turn, the payback will be between 5th and 6th years; its exact value is around five years and seven months It is noteworthy that the actual payback period will be less than the estimated durations as per the increase in the tariff of electricity in Malaysia. According to the results of this study, there are not significant differences between the results of the payback period of green roof installation in this paper and a similar study conducted in Canada as a

12

developed country. Bianchini and Hewage (2012b) conducted a cost-benefit analysis of green roof installation in Canada, and the payback periods of green roof installation were calculated in their study around 55 and 72 months for extensive and intensive green roofs in Canada respectively. On the other hand, the results of NPV in this study significantly differs from the mentioned study conducted in Canada. The results of their study showed that the maximum NPV for an extensive and intensive green roof in Canada, considering only personal benefits, can be up to $3606/m2 and $5715/m2, respectively (Bianchini and Hewage, 2012b). It is worth mentioning that, although there are many factors influencing the results of NPV, the significant differences in the results of NPV could be due to the differences in the economic factors, particularly, the difference between the discount rate in developed and developing countries. 4.2

Second Scenario: Selling the Property after Finishing Construction As it can be seen from Fig. 3, when the owner decides to sell the property after finishing construction,

the most probable cash flow is -$16.23/m2 for extensive green roofs, and this amount could be up to $11/m2 with 90% confidence. In terms of installing intensive green roof, these amounts are -$41/m2 and up to $39.75/m2 for the most probable amount and with 90% confidence, respectively. Those who want to sell the property after finishing the construction cannot benefit from the annual benefits of green roofs; however, the benefit from the increase in property value can be capitalized in this scenario. Additionally, the probability of loss for extensive and intensive green roof is around 73% and 76%, respectively. As it can be seen in Fig. 3, the probability of loss for both types of green roofs is higher than their potential benefit. Fig. 3 also indicates that the benefit of extensive green roof installation can be up to $31/m2, while the maximum benefit of intensive green roof installation is $94/m2. 4.3

Sensitivity Analysis Regression and correlation sensitivity analyses were conducted for this study. In correlation

sensitivity, Risk Solver Platform calculates the rank-order correlations considering the inputs and the results of simulations. The range of rank order valuation is between -1 and +1. Rank order correlation calculates the relationship between two data sets by comparing the rank of each value in a data set (Armaghani et al., 2016). This type of correlation sensitivity, using Monte Carlo simulation, is preferable compared to the linear correlation, especially when uncertain variables are existed (Wang et al., 2011), and the exact distribution functions of the variables are unknown. The results of correlation sensitivity analysis show the level of effectiveness of the variables on the final output of the simulation where the inputs consist of different ranges of values and different units. However, in regression sensitivity, known as the conventional approach (Song and Wang, 2016), multiple regression analysis was conducted by variation of one input

13

(between -50% and +50%), while all the other inputs kept constant. Same procedure was repeated for all the inputs, and the changes in NPV and payback period were obtained. Consequently, regression sensitivity analysis was conducted to identify the effects of contributing factors on NPV and payback period. Table 4 and Table 5 show the results of correlation analyses for extensive and intensive green roofs, respectively. In the first scenario, the most effective parameters on NPV for both types of green roofs in the first scenario are energy saving, discount rate and inflation rate, while the least effective factors for extensive and intensive green roofs are O&M costs and noise reduction, respectively. On the other hand, energy saving and initial cost are the most influential factors in the results of the payback period for both types of green roofs. In the second scenario, the most influential factors in the results of cash flow for extensive green roof are the increase in property value followed by initial cost. However, the order of these two factors for intensive green roof is exactly opposite to that of extensive green roof. It is noteworthy that the noise reduction is the least effective factor in the second scenario for both types of green roofs. Figs. 4-6 illustrate regression sensitivity analyses. As indicated in Fig. 4, the discount rate and energy saving have the highest negative and positive impacts on the results of NPV for both types of green roofs, respectively. Moreover, Fig. 4 shows that the inflation rate is an effective factor in the results of NPV for both types of green roofs in the first scenario; the lower the inflation rate, the lower the NPV. It can clearly be observed that the economic circumstances of each region have a significant impact on the NPV of green roof installation. As a result, the economic factors should be carefully considered for adopting any type of green roof application. Moreover, O&M costs, lifespan of the roof, and noise reduction for both extensive and intensive green roofs are slightly effective on the results of NPV, while the initial cost has the higher impact. Furthermore, in the payback period for both types of green roof as shown in Fig. 5, energy saving and initial costs have the major impacts on the results, while noise reduction and O&M costs have lower impacts. As it can be seen from the results of sensitivity analyses of NPV and payback period for both types of green roofs, energy saving has the highest impact on the results. This can be due to the high amount of energy consumption in Malaysia for air-conditioning, and the capability of green roofs for energy reduction. In the second scenario, as shown in Fig. 6, initial cost, property value, and noise reduction are the only effective factors, while noise reduction has the lowest impact on the result of cash flow. It is important to note that the positive effect of the increase in property value is higher than the negative effect of initial cost in both types of green roof.

14

5 CONCLUSION Green roofs provide many private benefits. This paper focused on all the private factors affecting probabilistic cost-benefit analysis of installing two types of green roofs in Kuala Lumpur including initial cost, property value, energy saving, O&M costs, lifespan, and the acoustic effect. NPV and payback period of green roof installation were analyzed through Monte Carlo simulation. Two different scenarios were considered for providing the results: using and selling the property after finishing construction. After obtaining the results, it is concluded that installing an extensive green roof is a low-risk investment and owners benefit from installing extensive green roof with a low probability of loss. Furthermore, installing an extensive green roof is a long-term investment with a short-term return in Kuala Lumpur. For intensive green roof, the payback period is longer than extensive green roof in Kuala Lumpur; however, it also can be considered as a short-term investment. The differences between payback periods of both types of green roofs is up to two years, and as the lifespan of green roof is considered up to 50 years, both types of green roofs can be considered as a feasible alternative to conventional roofs in Kuala Lumpur in terms of the payback period. The differences between the results of NPV and payback period between the two types of green roofs in two scenarios are due to the significant impact of the increase in property value and energy saving. Although the benefit from the increase in property value is achieved for only the second scenario, the owner in this scenario cannot benefit from annual energy saving. In the second scenario, when the owner decides to sell the property after finishing construction, most probably, neither extensive green roof nor intensive green roof can result in benefit for the owner. Moreover, all the factors discussed as the costs and benefits of installing green roofs directly affect cost-benefit analysis; however, inflation rate and discount rate are the social rates that cannot be ignored. Further research is required to analyze the economic impact of storm water management for private sector in Malaysia. This major plus point of green roofs can directly affect the results of cost-benefit analysis.

Acknowledgment The authors would like to express their sincere appreciation to the anonymous reviewers for their valuable and constructive suggestions.

15

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Figure1. Architecture of the simulation model

Figure 2. Obtained distribution model of NPV together with summary statistics (first scenario)

Figure 3. Obtained cash flow distribution model together with summary statistics (second scenario)

Figure 4. Sensitivity analysis for the factors affecting the results of NPV

Figure 5. Sensitivity analysis for the factors affecting the results of payback period

Figure 6. Sensitivity analysis for the factors affecting the results in second scenario

23

24

25

26

27

28

29

Table 1. Data input for the extensive green roof

Factor Initial cost

Cost ($/m2) Min Max 75 100

Cost

Time frame One time

Distribution function Uniform

Type

Property value

22.5

90

Benefit

One time

Uniform

Energy saving

1.13

36.84

Benefit

Annual

Uniform

Noise reduction

11

19

Benefit

One time

Uniform

O&M cost

0.1

0.25

Cost

Annual

Uniform

Lifespan

21

26

Benefit

Every 20 years

Constant

30

Reference (Rahman et al., 2013)  (“House Prices in Malaysia,” 2015)  (Noor et al., 2015)  Assumption  (Wong et al., 2003)  (“Pricing & Tariffs,” 2016)  (Tang and Chin, 2013)  (The Government of Malaysia, 2015)  (“Acoustic Ceiling Panel,” 2016)  (Lagström, 2004)  (Connelly and Hodgson, 2008)  The survey distributed to green roof companies  (The Government of Malaysia, 2015)  (“Ideal Roofing,” 2013)

Table 2. Data input for the intensive green roof

Factor

Cost ($/m2) Min Max

Type

Time frame

Distribution function

Initial cost

100

240

Cost

One time

Uniform

Property value

45

180

Benefit

One time

Uniform

Energy saving

3.14

61.92

Benefit

Annual

Uniform

Noise reduction

11

19

Benefit

One time

Uniform

O&M cost

0.80

6.5

Cost

Annual

Uniform

Lifespan

21

26

Benefit

Every 20 years

Constant

31

Reference  (Rahman et al., 2013)  The survey distributed to green roof companies  (“House Prices in Malaysia,” 2015),  (Noor et al., 2015)  Assumption  (Wong et al., 2003)  (“Pricing & Tariffs,” 2016)  (Tang and Chin, 2013)  (The Government of Malaysia, 2015)  (“Acoustic Ceiling Panel,” 2016)  (Lagström, 2004)  (Connelly and Hodgson, 2008) The survey distributed to green roof companies  (The Government of Malaysia, 2015) (“Ideal Roofing,” 2013)

Table 3. Data input for discount and inflation rates

Social Rate

Value

Reference

Minimum

Maximum

Discount Rate (%)

8

15

(Hallegatte 2014)

Inflation Rate (%)

0.6

5.4

(“Malaysia Inflation” 2015)

32

Table 4. Correlation coefficients between input variables foe extensive green roof.

Name of the variable Initial cost Property value Energy saving Noise reduction O&M cost Longevity Inflation rate Discount rate

Correlation coefficient NPV -0.06 +0.89 +0.04 -0.005 +0.02 +0.25 -0.32

Payback period -0.16 +0.88 +0.05 -0.01 -

33

Cash flow -0.35 +0.93 +0.11 -

Table 5. Correlation coefficients between input variables foe intensive green roof.

Name of the variable Initial cost Property value Energy saving Noise reduction O&M cost Longevity Inflation rate Discount rate

Correlation coefficient NPV -0.18 +0.84 +0.01 -0.08 +0.02 +0.26 -0.32

Payback period -0.37 +0.93 +0.04 -0.17 -

34

Cash flow -0.72 +0.69 +0.05 -