Life Cycle Costing (Environmental Footprints and Eco-design of Products and Processes) 3031409922, 9783031409929

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Environmental Footprints and Eco-design of Products and Processes

Subramanian Senthilkannan Muthu   Editor

Life Cycle Costing

Environmental Footprints and Eco-design of Products and Processes Series Editor Subramanian Senthilkannan Muthu, Head of Sustainability - SgT Group and API, Hong Kong, Kowloon, Hong Kong

Indexed by Scopus This series aims to broadly cover all the aspects related to environmental assessment of products, development of environmental and ecological indicators and eco-design of various products and processes. Below are the areas fall under the aims and scope of this series, but not limited to: Environmental Life Cycle Assessment; Social Life Cycle Assessment; Organizational and Product Carbon Footprints; Ecological, Energy and Water Footprints; Life cycle costing; Environmental and sustainable indicators; Environmental impact assessment methods and tools; Eco-design (sustainable design) aspects and tools; Biodegradation studies; Recycling; Solid waste management; Environmental and social audits; Green Purchasing and tools; Product environmental footprints; Environmental management standards and regulations; Eco-labels; Green Claims and green washing; Assessment of sustainability aspects.

Subramanian Senthilkannan Muthu Editor

Life Cycle Costing

Editor Subramanian Senthilkannan Muthu Chief Sustainability Officer Green Story Inc. Kowloon, Hong Kong

ISSN 2345-7651 ISSN 2345-766X (electronic) Environmental Footprints and Eco-design of Products and Processes ISBN 978-3-031-40992-9 ISBN 978-3-031-40993-6 (eBook) https://doi.org/10.1007/978-3-031-40993-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023, Corrected Publication 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Paper in this product is recyclable.

Contents

Application of Life Cycle Costing in Building Energy Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohammad Hossein Jahangir and Reza Alimohamadi

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Life Cycle Cost Analysis of Nearly-Zero Energy Buildings: An Introduction to the Methodologies . . . . . . . . . . . . . . . . . . . . . . . . . . Hashem Amini Toosi, Jacopo Famiglietti, and Mario Motta

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Analysis of Life Cycle Cost, Barriers and Strategies Towards Enhancing Its Application: Case Study of Commercial Buildings . . . . . . . . . . . . . . . Achini Shanika Weerasinghe and Thanuja Ramachandra

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Applications of Life Cycle Costing in Waste-to-Energy Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Abdulrahman Abdeljaber, Omar Mostafa, and Mohamed Abdallah

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Life-Cycle Cost Assessment of Various Wave Energy Converters (Based on Energy and Carbon Intensity) . . . . . . . . . . . . . . . . . . . . . . . . 117 Mohammad Hossein Jahangir and Shahrzad Bahrizadeh Towards Optimising the Life Cycle Costs of High-Rise Residential Apartments: A Case of Sri Lanka . . . . . . . . . . . . . . . . . . . . 153 Kajavathani Panchalingam and Thanuja Ramachandra Correction to: Life-Cycle Cost Assessment of Various Wave Energy Converters (Based on Energy and Carbon Intensity) . . . . . . . . . . . . . . . Mohammad Hossein Jahangir and Shahrzad Bahrizadeh

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Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183

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Application of Life Cycle Costing in Building Energy Performance Mohammad Hossein Jahangir and Reza Alimohamadi

1 Basic Concepts Due to the problems of excessive energy consumption and the growth of greenhouse gas emissions, as well as the need for sustainable development in the construction industry, employers and designers in this field are forced to follow specific tips and requirements to achieve the desired goals (Lu et al., 2021; Whole Life-Cycle Costing: Risk and Risk Responses, 2004). But compliance with these requirements is still not mandatory in many countries of the world and is not properly implemented (De Boeck et al., 2015). Considering this issue and due to the higher initial costs of these requirements, investors are not eager to fulfill them voluntarily. In such cases, an economic analysis of the projects and, as a result, economic justification of the requirements, can be very helpful. One of the most widely used and powerful economic analyses is the life cycle costing (LCC) analysis. Life cycle cost analysis is a method that is used for the economic evaluation of different proposed options according to their cost in a given period of time (Dwaikat & Ali, 2018a). For example, in the building sector, it can be related to the economic evaluation of the use of the heating system or the use of different materials in the external walls of the building. The LCC calculation method is completely in accordance with the principles mentioned in engineering economics, and in order to understand its concept correctly, one must master some of the basic concepts of this branch (Kinch, 2003). The life cycle cost can be used at any stage of the service life of the building. The LCC method includes all costs and revenues of the project, from the stages of feasibility, engineering, and implementation to the construction

M. H. Jahangir (✉) · R. Alimohamadi Faculty of New Sciences and Technologies, Renewable Energies and Environmental Department, University of Tehran, Tehran, Iran e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. S. Muthu (ed.), Life Cycle Costing, Environmental Footprints and Eco-design of Products and Processes, https://doi.org/10.1007/978-3-031-40993-6_1

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and operation of the building and, finally, their disposal to the environment. Some of the things wherein LCC analysis can be used for evaluation in the building sector are given below: • • • •

Individual building systems. Newly built buildings. Plans for a partial or major renovation of a building. Comprehensive plans for the development and renovation of facilities.

In choosing the proposed options, the extent of their impact on the various goals of the building, such as reducing water and energy consumption, thermal comfort, system reliability, flexibility against possible changes and risks, compliance with building standards and rules, and even the appearance of the building, should be considered and should lead to their improvement (Kneifel & Webb, 2020). In order to prioritize options, in addition to technical issues, economic issues should also be given sufficient attention because, in many cases, choosing these options may lead to an increase in the initial or operational costs of a project, which discourages the investor from implementing them. In such cases, life cycle cost analysis can be useful for ranking different options and determining whether or not they are economical. For example, the life cycle cost analysis can suggest the option with a high initial cost as the optimal option due to the lower operating and maintenance costs during the building’s operation period. For this reason, using this analysis is very useful to determine the optimal options for long periods of time, whereas many economic methods are only focused on initial costs or operational costs at the beginning of the project (Zhao & Li, 2022). In this regard, energy-saving projects provide excellent examples for the application of LCC. For example, it is possible to reduce energy consumption by making changes in the external envelope of buildings (such as walls, ceilings, and windows); replacing old heating, ventilation, and air conditioning (HVAC) systems with more modern systems; using renewable technologies in buildings, etc.; and maintaining thermal comfort. When such actions increase the initial cost of the building, with the help of LCC, it can be determined whether such actions are economically justified or not. There are usually several optimal solutions in an energy retrofit project (Dwaikat & Ali, 2018b). For example, to reduce heat transfer from the walls around the building, thermal insulation can be used in a wide range of thermal resistance values. Or in the case of windows, windows with heat resistance and different glasses can be used. Or using air conditioning systems with higher efficiency instead of the current systems is an example of these cases. Many of these solutions are also cost-effective, but usually, an LCC analysis can help select the most optimal alternative. Also, LCC can be used to prioritize the allocation of funds to a specific option in case of budget constraints. In order to rank the available options and choose the best of them, a number of influential indicators such as saving-to-investment ratio (SIR) or adjusted rate of return (AIRR) are explained below (Kneifel & Webb, 2020).

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Fig. 1 Road map of the chapter

Life cycle cost analysis is one of the most powerful economic analyses. For this reason, it requires more economic knowledge than other economic analyses. To perform life cycle cost calculations, the analyst must have a sufficient understanding of economic concepts such as investment and operating costs, discounts, inflation, interest, and similar issues (Kim et al., 2020). In order to see the overall outline of the chapter, the road map of this chapter is shown in Fig. 1.

2 History of LCC It is not possible to talk about the history of life cycle cost analysis in detail, but according to articles, it was probably first used in the 1960s by the United States in the military industry. Then, in the following years, this concept was developed in various industrial and social fields. In the early 1990s and with the emergence of a concept called green buildings, the concept of LCC entered a new phase, and many researchers and experts have been trying to interpret this issue and provide methods and tools to facilitate the calculations of this analysis. At the end of the period, they revolutionized life cycle costing by performing a life cycle costing analysis for the entire duration of a project, from the extraction and manufacturing of raw materials to their disposal in the environment (Cole & Sterner, 2010; Asiedu & Gu, 1998). In this regard, in 1987, Promilo and Pawsey used mathematical models in the LCC analysis of Australian university buildings. They were able to predict the life cycle pattern of the investigated buildings using these models (Bromilow & Pawsey, 2013). They did this by identifying and costing the activities that need to be done to maintain the building. At the same time, other organizations and researchers around the world also developed LCC methods in building design. For example, many US

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government agencies, during their research on the LCC of buildings, have developed guidelines to examine investments in the different aspects of the building (Kneifel & Webb, 2020).

3 Applications of LCC Today, according to the progress made in the field of life cycle costing, it is used in a wide range of industrial and nonindustrial applications, and as mentioned earlier, one of these applications is related to the use of LCC in buildings, which are analyzed in order to improve energy performance. Due to this issue and the increasing growth of research on buildings with high-energy performance, there are a large number of articles and projects that were devoted to the application of LCC analysis as an economic analysis tool in relevant projects. Considering the high initial cost of materials, equipment and systems used in residential buildings, the use of life cycle costing analysis can show the economic benefits of retrofit options and justify building optimization projects (Goh & Sun, 2016). In the following, some studies conducted regarding the use of LCC tools in improving the energy performance of buildings will be reviewed. In a study, Aye et al. analyzed their proposed options for the construction of an office building using the traditional methods of life cycle cost analysis (Aye et al., 2010). In another research, Ellingham et al., after conducting a life cycle cost analysis for the building studied in their research, concluded that the use of LCC analysis, in the context of project uncertainties, can help the owner in choosing a suggested option (New Generation Whole-Life Costing, 2006). Also, Cole et al. used several different LCC methods in order to justify the economic performance of green buildings. In the end, they stated as a result that the use of LCC analysis to examine the existing solutions in a project will lead to a definite benefit (Cole & Sterner, 2010). In a number of articles, considering the high initial costs of green buildings compared to traditional buildings, using life cycle costing analysis, it has been proven that despite the higher initial costsof these buildings, the operation costs in the years then it will decrease (Weerasinghe et al., 2021). In s similar study, Kats et al. calculated the life cycle cost of 30 schools located in ten different states. They stated that the initial cost of such buildings will be only 2% more than that of traditional buildings, while the optimization of energy performance and the reduction of thermal losses, in a period of 20 years, is 20 times its traditional examples (Teachers et al., 2006). The economic usefulness and added value of these types of buildings should be clarified for investors through life cycle cost analysis. This analysis helps project owners invest in green buildings and, thus, reduce the emission of environmental pollution. In this regard and in order to investigate this issue, Zuo et al. indicated that paying attention to the economic aspects of green buildings is very important, and for the growth of this industry, the use of such analyses is necessary and vital (Zuo &

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Zhao, 2014). In addition, they reviewed the literature related to the evaluation of green buildings with a focus on life cycle costs. As a result of their review, they found that the use of LCC analysis in this industry is relatively slow (Zuo et al., 2017). Other authors also investigated various simplified methods of life cycle cost analysis in the early stages of green building design to identify more effective options. LCC studies have continued in the field of construction, and many people have chosen the optimal solution from among the proposed options using it. For example, Tam et al. used this analysis to investigate wooden options for saving building energy. In another work, Ilankon et al., by means of life cycle cost analysis, investigated the use of cementitious supplementary materials in building envelopes and announced the proposed options (Tam et al., 2017).

4 Barriers to LCC In the previous sections, it was stated that the use of life cycle cost analysis for the economic analysis of the energy performance of a building can be very helpful in many areas, including choosing the optimal option from among a number of proposed options. But it should also be noted that the use of LCC, due to possible complications and the lack of familiarity of building owners and designers with its process, can put double pressure on construction projects (Kirk & Dell’Isola, 1995; ISO, 2017). For this reason, the use of this technique is limited, and there are still many problems with its widespread use. Other reasons that can be mentioned are the lack of explanation and justification of the economic benefits of this method during the project period and after, the lack of complete and reliable financial and nonfinancial data of the buildings to perform the LCC process, uncertainties that may affect various parameters of the LCC analysis during the existence of the project, incomplete or wrong implementation of the LCC analysis process, and so on (Dwaikat & Ali, 2018b). Therefore, more efforts should be made in the area of the development of life cycle cost calculation methods according to the type of project under study, as well as the proper training of existing methods by experts so that it can be expected that the use of this technique in the construction industry becomes widespread.

5 LCC Method As mentioned before, for the purpose of the economic analysis of proposed solutions to optimize different objectives in a building, the LCC method is used. In fact, life cycle costing analysis includes all costs related to the discovery, extraction and preparation of building materials, engineering and construction of buildings and equipment used (such as HVAC equipment), operation and costs related to building maintenance and related equipment and finally, the costs related to the disposal of construction materials and equipment that have reached the end of their life (Dwaikat

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Fig. 2 LCC process

& Ali, 2018b). Figure 2 shows the different parts of the LCC process for the economic analysis of the energy performance of a building. One of the sources that can be used to obtain the data required for the LCC process is the international standard ISO 15686-5:2017. In the project cost breakdown structure that this standard provides, an LCC analysis is divided into four main components: (1) costs related to engineering and construction, (2) costs related to project execution processes, (3) costs related to maintenance, and (4) costs related to the disposal of materials and equipment used during the project to the environment. Of course, each of these categories includes smaller components that can be used to cover all project costs (ISO, 2017). By adding the stage of project concept and definition to the stages mentioned above, it is possible to see the whole life cycle costing process in a project (Dwaikat & Ali, 2018b). It is important to mention that the failure structure provision contained in the ISO 15686-5:2017 standard is general, and the process of calculating the life cycle cost of the building has complications, which will be mentioned below. One of the main advantages of using this analysis is that when several options have the same performance, it is possible to determine which option will be more economical than others with the help of LCC calculation. Considering that only one alternative can be chosen for each item of the project, these options are called mutually exclusive (Schmidt & Crawford, 2017). As mentioned, based on many articles, to know the details of the LCC process and how it works, the ISO 15686-5 standard, which is a reference standard, can provide useful information. According to this standard, the different parts of the life cycle cost analysis that should be adjusted for LCC calculation are the life span of the

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building, the analysis period, the future inflation rate, the discount rate of future prices to the current price, engineering and construction costs, repair costs and maintenance, and the costs of material disposal into the environment, each of which will be described below (Dwaikat & Ali, 2018b; ISO, 2017).

5.1

Life Span of the Building

First of all, it should be said that the service life of a building means its useful life. The useful life of a building is the period when the building’s equipment and materials can provide the minimum required performance level (ISO, 2008). The correct identification of the useful life of a building is very important and necessary to determine the parameters related to the duration of the project that will be used in the existing relationships to calculate the LCC. However, determining the overall life span of a building, considering the type of equipment in it, is a challenging issue and requires sufficient attention because, for example, doing and not doing the correct maintenance of the building facilities can greatly affect the life of the building (Whole Life-Cycle Costing: Risk and Risk Responses, 2004; Kirk & Dell’Isola, 1995). In this regard, solutions have been proposed, among which a number of authorities, including ISO 15686-5, have stated that the estimated life of a building is at least equal to its design life (ISO, 2017).

5.2

Project Analysis Course

The next phase of life cycle analysis refers to the project analysis period. In fact, this period means the duration of the project. The basis of this concept is also the fact that the life cycle cost is not supposed to cover the entire life of a system or product; rather, it includes a period when devices and materials can meet the minimum requirements set in a project (Kirk & Dell’Isola, 1995; Schmidt & Crawford, 2017). In this regard, many economic experts have suggested that the time horizon of the project should be during the useful life of the project (the time period when the project is economically justified) (Whole Life-Cycle Costing: Risk and Risk Responses, 2004; Kirk & Dell’Isola, 1995; 135, S.F.-N. Handbook, 1996). For example, Kirk and Delizola have concluded in their research that this numerical period is between 25 and 40 years, which varies according to the building and its cultural and geographical conditions. In their view, the reason for determining the maximum period for analysis is that considering an analysis period of more than 40 years can lead to the devaluing of the current value of future cash flows (Kirk & Dell’Isola, 1995). The ISO 15686-5:2017 standard also recommended that in LCC calculations, the analysis period should not exceed 100 years. At the same time, to comply with the requirements of sustainable development, it is suggested that the

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LCC analysis includes the entire service life of the building or the system on which this analysis is performed (ISO, 2017).

5.3

Discounting in LCC Analysis

One of the most important parameters in LCC analysis is the discount rate. The correct determination of this rate plays a very important role in the correctness of the performed analysis. The discount rate is actually an expression of the present value of money that will be earned in the future (Kirk & Dell’Isola, 1995; ISO, 2017; Junkes et al., 2012). This rate basically refers to the time value of money and determines the equivalent value of money that is supposed to be used in the future (Bull, 2003). In expressing the concept of the discount rate, interest rate should also be mentioned. The interest rate works the opposite of discount rate; that is, if the value of the money that is currently used is calculated in the future, it is called interest rate. It should be noted that in order to calculate the life cycle cost of a project in order to compare the proposed options and choose the optimal option among them, the discount rate used should be calculated for all options at a common base time (Whole Life-Cycle Costing: Risk and Risk Responses, 2004; Kirk & Dell’Isola, 1995; Bull, 2003). Correctly calculating the discount rate of a project is an important and challenging issue. There are different ways to calculate this rate, such as using mathematical relationships in engineering economics or using methods recommended by international handbooks and standards (Kneifel & Webb, 2020). In this regard, the ISO 15686-5:2017 standard has recommended that the appropriate discount rate for privately owned projects should be determined in such a way as to cover the opportunity cost of the investment (ISO, 2017). For this purpose, the interest cost of a loan taken to invest in a project, the lost profit due to the cash reduction of deposits, the profit rate of other projects that were lost due to lack of investment, the actual profit rate, or the forecast profit rate resulting from the new project can be used as the discount rate for the project. This standard declares the appropriate discount rate for government projects to be the discount rate announced by the central bank of that country or the International Monetary Fund (Dwaikat & Ali, 2018a). Due to the importance of the correct calculation of the discount rate, below is a brief description of the method of calculating the discount rate of a construction project from the NIST 150 manual (Kneifel & Webb, 2020).

5.4

Discounting Future Amounts to Present Value

As mentioned earlier, in an LCC analysis, all cash flows in a project (both positive and negative), from the time of their discovery and extraction to their engineering and implementation, as well as after their construction and use until their disposal

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into the environment, should be discounted to the present value. This rate is usually determined based on the employer’s capital. This rate, for projects with personal ownership, is determined by a parameter, such as the minimum acceptable rate of return of the investor (MARR) for investments with equivalent risk and duration. According to the description and variety of investment opportunities, the exact amount of the discount rate is different for each project. In many countries, the value of this rate is published by related institutions for different applications and uses. An important point that should be taken into account in the calculation of the cash flows of the life cycle cost analysis is the payback time of an investment project. To explain further, it is very important for investors (both public and private) when they receive their capital. For example, an investor would rather earn $500 in five $100 installments over 5 years than earn the same amount at the end of the fifth year. The reason for this is a problem called inflation. In fact, the value of the cash flow decreases over time due to the effect of inflation. Also, another reason for this issue is that if the return on investment occurs in a shorter period of time, the investor can use it to invest in other projects (Kneifel & Webb, 2020).

5.5

Inflation

Another influential parameter in calculating the life cycle cost is the inflation rate. This rate directly indicates the decrease or increase in the price of goods and services (Kirk & Dell’Isola, 1995; ISO, 2008). Nominal or real costs can be used to determine the inflation rate in the calculation of life cycle cost analysis. To correctly identify the inflation rate, the concept of nominal and real costs should be well understood. Nominal costs are costs whose estimated price is estimated according to the effect of inflation. On the other hand, real costs are those costs that are not considered in the estimation of costs and the effects of increasing or decreasing inflation, and in a way, they represent the current value of goods or services (Kirk & Dell’Isola, 1995; ISO, 2017; Junkes et al., 2012). According to the literature, it is recommended to use the actual costs when analyzing life cycle costs in order to reduce the effects of possible uncertainties due to inflation. On the other hand, when the future value of money is evaluated, it is recommended to use nominal costs for the life cycle cost analysis (Kirk & Dell’Isola, 1995; ISO, 2017; Junkes et al., 2012; Bull, 2003). To know the inflation rate at different times, you can use the statistics published by the central bank or statistics office of each country or the International Monetary Fund (IMF). In these reports, information on a parameter called the consumer price index (CPI) is usually discussed for various types of goods and services. The consumer price index is the rate of change in the price of a fixed amount of goods or services over time (Dwaikat & Ali, 2018b). The World Bank also announces the inflation rate of different countries in its annual reports (World Bank, n.d.). Another important point that should be noted is that it is better to consider the price inflation rate of the energy group separately from other groups of goods and services. The reason for this is that the increase or decrease in the price of energy is different from

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other goods and services. In fact, the price of energy is sensitive to many political, social, environmental, and other events, and with any change in them, it undergoes severe fluctuations. This is the reason why many countries, in presenting their inflation report, divide different groups and services and report the corresponding inflation rate for each group (Dwaikat & Ali, 2018b).

5.6

Design and Construction Cost

Costs related to engineering, construction, and implementation are one of the most important and influential parts in calculating the life cycle cost of construction projects (Whole Life-Cycle Costing: Risk and Risk Responses, 2004; Bull, 2003). These steps usually take a lot of time from the project. The actual cash flows that are spent during this stage are considered sunk costs. In economic applications of life cycle analysis of buildings, these costs should be ignored. But considering that one of the goals of the life cycle cost analysis is to determine the total life cycle cost of the building, engineering and implementation costs are inevitably included in the LCC (Whole Life-Cycle Costing: Risk and Risk Responses, 2004; ISO, 2017).

5.7

Operating Cost of the Building

The operating costs of a building are mainly related to the costs that must be incurred during the construction and operation of the building. There is a wide range of costs, and based on the international standard ISO 15686-5:2017, they can include things such as rent, water and electricity costs, taxes, insurance, etc. Each of these costs alone may not affect the total budget, but their sum will definitely include significant amounts (ISO, 2017).

5.8

Building Maintenance Cost

Every building, after its construction and operation, during its useful service life, will need repair as well as the maintenance of the materials and equipment used in it. In fact, building maintenance costs refer to those expenses. that are necessary to maintain the function of the building and the equipment used in it. These include expenses for the restoration and protection of the body of the building; the replacement or repair of the building's mechanical and electrical facilities; compensation for damages caused by accidents, such as earthquakes; and similar costs (Olanrewaju & Abdul-Aziz, 2015). In other words, the cost of maintenance is the sum of the cash flows spent for this purpose. As with other phases of the life cycle cost analysis, the ISO 15686-5:2017 standard categorizes maintenance costs, as shown below (ISO, 2017):

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1. 2. 3. 4. 5. 6. 7.

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Costs related to maintenance. Building renovation and retrofitting. Partial repair and replacement cost. Cost of replacing systems or main parts. Cleaning. Ground conservation. Equipment renovation and maintenance tax.

5.9

End-of-Life Cost

The last part of the calculation steps of the life cycle cost analysis is the cost of the disposal of construction components and materials to the environment (Whole LifeCycle Costing: Risk and Risk Responses, 2004; Kirk & Dell’Isola, 1995). These costs include things such as the costs related to equipment inspection at the end of its life, destruction and disposal of hazardous materials, or other such costs that should be taken into account in the LCC analysis (Whole Life-Cycle Costing: Risk and Risk Responses, 2004; Kirk & Dell’Isola, 1995; ISO, 2017). Regarding the destruction of building materials and equipment, it should be said that there are methods of destruction that are usually used. These methods include mechanical destruction, destructive destruction, and a combination of these two methods (Pun et al., 2007). In the mechanical destruction method, construction equipment and materials are destroyed directly. In this method, due to the destruction of all the components that have reached the end of their life, it takes more time, but it does not require much precision (Kibert & Hazardouse, 2000). On the contrary, according to the goals of sustainable development and greater compatibility with the environment, there is a deconstructive method. In this method, all building components and materials are checked very carefully so that they are not destroyed if it is possible to recycle and reuse them. In this method, due to the reduction of the amount of waste generated after the end of the building’s life, much less damage is done to the environment (Pun et al., 2007; Kibert & Hazardouse, 2000). In the combined method, both mentioned destruction methods are put together to increase both the speed and accuracy of destruction (Pun et al., 2007). Nowadays, due to the need to pay enough attention to sustainable development in all sectors, including the construction sector, it is necessary to use appropriate techniques for the destruction and reuse of construction equipment (Sustainable Construction: Green Building Design and Delivery, n.d.). Considering that in many countries no law has been established to oblige the owners to comply with the principles of destruction, there is no good information in this field. However, it is recommended to use these techniques in the destruction process, considering the advantages and income that material recycling can have (Pun et al., 2007; Kibert & Hazardouse, 2000). As mentioned earlier, in the process of calculating the LCC of a project, all project cash flows must be discounted to the net present value. For this, indicators

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and parameters can be used. Next, based on NIST Manual 135, which is a very reliable calculation basis around the world, the method of using the discount rate to discount future cash flows to their present value is described. First, it should be said that parameters have been introduced that, although they are not part of the main process of LCC calculations, are compatible with this method. These parameters include net savings, the amount of savings to the initial cost, and also the adjusted internal efficiency, which will be described in the following sections. It is also important to mention that all the stated parameters are based on the same cost rate during the service life of the project, which is completely consistent with the life cycle cost analysis (Kneifel & Webb, 2020). Each of the criteria mentioned above can help in different parts of the LCC analysis. For example, to determine the lowest cost of an option among other available options, the net savings criterion can be used. In this way, the proposed solutions with lower LCC lead to higher net savings. Alternatively, savings-toinvestment ratios and adjusted internal rates of return can be used for ranking (e.g., using materials with different heat transfer coefficients for the wall) (Fregonara et al., 2017). Before entering the cost estimation part in the LCC calculation process, it should be said that life cycle cost analysis can also be calculated annually. In this case, the annual value of the depreciation cost of all project components is considered in the same way and according to the value of money at a certain time.

6 Estimated Costs for LCC In the previous sections, the basic concepts and main stages of the life cycle cost analysis were discussed. As mentioned earlier, LCC analysis is an economic analysis, and to calculate its different parts, all costs and revenues of a project must be determined. Some of these financial flows can be based on official statistics, and others should be estimated. In the following, we will try to explain the cash flows of a project and the topics related to it.

6.1

Related Effects

In every construction project, there are various costs, including the costs of engineering and construction, operation and repairs, and their destruction as well as returning them to the environment (Hong et al., 2021). The first decision that must be made at the beginning of the process of calculating the life cycle cost of a building is to identify the factors affecting the predetermined goals, such as optimizing building consumption, using renewable energy technologies, or choosing different options for building envelope so that they can be used in the calculation of the life cycle cost of the building. To answer this question, the financial effects that each of the

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proposed options imposes on the project should be examined. When considering these impacts, impacts should be considered in terms of monetary quantities (e.g., in dollars) whenever possible. For other effects that cannot be expressed in monetary terms, alternative methods such as the quantification of values should be used so that it is possible to use them in the life cycle cost calculation process. It should be noted that these items are effective as a result of the analysis due to their extensiveness and many interactions with other components and the direct and indirect effects they may leave behind (Kneifel & Webb, 2020). Considering that construction projects consist of many parts and components with different effects, including all costs in an LCC analysis can impose a large computational cost (Morrissey & Horne, 2011). Therefore, usually in an LCC analysis, those costs are considered which are significant in terms of quantity and are necessary for the analysis to be done correctly. In general, cost is considered relevant to a decision when it changes from one option to another proposed option. In this case, the common costs between all options cannot have a significant effect on the choice between them, and they can be removed from the calculation process in order to avoid increasing the computational burden. It should be noted that if these costs are not avoided in the implementation of an LCC, it does not mean that a wrong answer will be produced; it will only lead to added computational load and increased costs related to data analysis and collection. In an LCC analysis, costs will be influential when they can make a significant difference between the proposed options (Kneifel & Webb, 2020). For example, energy costs for replacing building air conditioning systems can be very effective and significant, while the energy cost for replacing light bulbs in a part of a house can be less effective. Unfortunately, there is no specific method for determining the importance of the costs of a construction project, and a large part of it depends on experts. But despite this, there are guides in articles and books around the world that can help people with this. In examining the economic effects of decisions, attention should be paid to the elimination of sunk costs. Sunk costs are costs that have been incurred in the past and cannot be recovered. In effect, sunk costs are excluded from future economic decisions because they remain constant regardless of the outcome of a decision. For example, the cost of providing a cooling tower for an air conditioning system that has just been replaced with an air-cooled air conditioning system is considered a sunk cost. These costs should be avoided in calculating the LCC analysis (Kneifel & Webb, 2022). Project cost estimation can be done in different ways (Almeida et al., 2015). They can be obtained directly from existing prices or from methods such as the interpolation of data available in industrial cost collection databases or even the extrapolation of current costs according to their current and future prices. The use of a specific method or a combination of them depends on the available information and the economic conditions. In the following, explanations are given about the sources and methods of cost estimation, but it should be noted that the expert should identify the best method of cost estimation according to his knowledge and experience and use them in his analysis (Kneifel & Webb, 2022).

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6.2

M. H. Jahangir and R. Alimohamadi

Classification of Expenses

The different parts of the cost of an LCC can be classified in different ways. In most of these categories, cash flows related to investment and project implementation costs are considered separately. Some of these categories include initial and future cash flows, annual recurring cash flows, and recurring expenses, which will be described below (Kneifel & Webb, 2020).

6.2.1

Investment Costs Versus Operating Costs

Usually, the financial flows of the life cycle cost analysis are divided into two main groups of cash flows related to investment and operation. Understanding the concept of these costs and how they differ from each other is helpful when calculating supplementary measures of the life cycle cost (Kneifel & Webb, 2022). It should be noted that in the case of the growth of project investment costs, employers try to save on possible costs that may occur after operation. In fact, the differences between these two costs do not affect the final result of the life cycle cost so much and do not make a proposed option out of priority. It is only possible that the priority of choosing the desired option will change according to the amount of available capital (Kneifel & Webb, 2020). It should be noted that the initial cash flows include costs such as project feasibility, engineering and implementation, and the preparation of the required materials, all of which are related to the preconstruction and initial stages of construction. On the other hand, the future cash flows of a project include costs such as income from material recycling, positive cash flows from the production of building components, costs related to the disposal of building materials that have expired, and initial costs for replacing defective parts. It becomes healthy with parts (Biolek & Hanák, 2019). On the other hand, the costs of water and electricity consumption, the costs of cleaning the building, and subscription are related to the operating costs of the building. It should be noted that the costs of repairing parts (for example, repairing the rotor of the engine room electric pump or repairing the windows of the building) are considered part of the operational costs (OM&R). These costs include various examples of building components, which are usually related to the postoperational period of the building (Biolek & Hanák, 2019).

6.2.2

Initial and Future Cash Flows

The importance of separately considering the initial and future cash flows of the project is necessary for calculating some supplementary criteria of LCC analysis. Amounts spent in the initial stages of construction, such as feasibility studies, engineering, and project implementation, belong to the first category, i.e., initial

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costs. These costs, as their name suggests, are related to the initial stages of construction. Other costs of a project, such as maintenance costs, replacement costs and costs that will exist after the operation of the building and the launch of the existing systems, are related to the future costs of the project. Also, the cash flows that will remain after the building’s lifetime are likewise considered future cash flows (Kneifel & Webb, 2020).

6.2.3

Annual Recurring and Nonrecurring Amounts

This category is related to the number of occurrences of a cost during the study period of the project. Also, this category is used to identify the type of present value factor for discounting future amounts: – Nonrecurring costs occur without a specific pattern and at unknown times during the project period. Initial cash flows, replacement costs, revenues from a project, and costs resulting from the repair of defective equipment are included in this category. Also, to discount the future amounts to the present value, the single present value (SPV) factor is the appropriate factor for this purpose. Due to the lack of a specific model, this category of expenses is usually not predictable, and forecasts should be made for them from the total budget in the early stages of the project. – This category of expenses has a specific pattern and will exist every year. The important thing about these costs is that either the amount of these costs is constant during the life of the building or the amount of their increase can be predicted every year. Costs caused by water and electricity consumption, costs caused by cleaning the building, routine maintenance costs, and such things are included in this group. Also, coefficients such as UPV or UPV* are suitable present value factors for this type of expense. The difference between these two coefficients is that if the amount of costs is a constant value every year, the UPV coefficient is used to discount the future values, and if the costs have a certain increase every year, the UPV* coefficient will be used (Kneifel & Webb, 2020, 2022).

6.3

Cash Flow Schedule

Life cycle cost analysis, like many other processes, requires schedules. For this reason, the cash flows of the project (both positive and negative) should be collected according to the time of occurrence. Preparing a detailed schedule may be a bit timeconsuming; for this reason, it is quite common to produce simplified schedules instead of detailed models. Using simpler time schedule models means that cash flows can occur every year and at certain times. It should be noted that building life cycle cost calculation software can be very helpful in preparing these schedules (Kneifel & Webb, 2020).

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Fig. 3 Cash flow diagram (Kneifel & Webb, 2020)

6.3.1

Cash Flow Charts

Usually, to better understand the timing of costs and revenues of a project, cash flow diagrams are used, as shown in Fig. 3. The diagrams are prepared for each of the alternative projects. In the graphs, the horizontal axis corresponds to the studied years. The numbers on the horizontal axis can correspond to any year of the building’s life, specific dates, or any arbitrary time division. It is even possible to consider the horizontal axis based on the year the building service started and add one unit to it every year. The vertical axis also shows the numerical value of costs. There is no specific requirement to show positive and negative cash flows, but typically, positive cash flows are shown at the top of the chart, and negative cash flows are shown at the bottom of the chart (Kneifel & Webb, 2020). Figure 3 shows the cash flow diagram of a construction project that has a service life of 15 years. As can be seen from the figure, in the year of starting the project, an amount of 5000 dollars has been invested. At the end of the service period of the building, an amount of $200 has been created as a residual. From this chart, there is other information about project costs, which can be referred to as recurring and nonrecurring costs (Kneifel & Webb, 2022).

6.4

Estimating Future Cash Flows Using Base Year Prices

Considering that the calculation of the life cycle cost of construction projects is usually done before the construction of a building, many values and parameters considered in the analysis should be used as estimates. For this purpose, future costs that are discounted based on the year of the start of building services are usually used for estimation. Despite its simplicity, this method usually has high accuracy and can be used in life cycle cost analysis projects. In estimating costs, if the price increase of a commodity or option is consistent with general inflation, the analysis can be considered a constant dollar. In constantdollar analysis, the inflation index is removed from the calculation process, which

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means that the future value of the commodity will be equal to its current value. In many articles and LCC projects, the prices of all goods and services, except for water, electricity, and gas, are considered fixed in dollars. But if the rate of increase in the price of goods or services is clear, that rate should be used in the analysis (Kneifel & Webb, 2022).

7 Calculation of Life Cycle Costs The classical LCC method is the simplest method for the economic analysis of a project during its lifetime. In general, life cycle cost analysis is used when there are several proposed options to achieve a specific goal (for example, using windows with different heat transfer coefficients or using boilers with different efficiencies to provide thermal comfort). In this method, the LCC value (as described below) should be calculated separately for all proposed options and should be compared with the LCC value calculated to the base building. In this case, if the LCC of the proposed option was lower than the LCC of the base case, it can be said that the proposed option has economic justification. It should be noted that in performing the life cycle cost analysis, one case should be considered as the base case. Also, the basis of LCC analysis is comparison (either with the base case or with other proposed options). It should be noted that for a correct comparison between the options, all common parameters of the LCC process should be considered the same for all options. Finally, the condition of meeting the minimum requirements of the project for choosing the proposed option must be met so that only the options that lead to the improvement of the considered goal (goals) are selected (Kneifel & Webb, 2020). In the previous parts, we tried to describe the different parts of the life cycle costing process and how it works. In the following, the basic equations used to calculate the life cycle cost of the project are described. These equations are expressed in a basic way, and the overall equations may undergo changes in each project according to the conditions of that project. The different terms needed to calculate LCC are the same costs described in the previous sections, such as maintenance costs, initial costs, or even project revenues. Also, after presenting the main relationships of LCC calculation, a number of supplementary parameters in LCC calculation are likewise described, and their main relationships are stated (Kneifel & Webb, 2022). The basic equation for calculating the life cycle cost of a project is shown below: N

LCC = t=0

Ct ð1 þ d Þt

ð1Þ

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M. H. Jahangir and R. Alimohamadi

which in the above equation, LCC, represents the current value of an alternative solution in terms of dollars, Ct, represents the difference in project costs, including initial costs, maintenance, operation, etc., with all financial flows entering the project, such as income from the sale of electricity produced from renewable energies to the grid in year t, N is the duration of the project and finally d is the discount rate to convert the units to the present value (Kneifel & Webb, 2020). The above formula, despite its simplicity, may require a lot of computational costs, especially when the study time is very long. Therefore, for building-related projects, the above relationship is rewritten as follows: LCC = I þ Repl - Res þ E þ W þ OMR þ X

ð2Þ

In the above equation, I is investment costs in dollars, Repl is replacement costs in dollars, Res is the remaining amount related to inputs in dollars, E is the cost of electricity and gas, W is the cost of water consumption, OMR is the cost of repairs and maintenance, and finally, X is related to other costs that may be incurred during the life of the project (Kneifel & Webb, 2020). After stating the main relationships in LCC calculation, it is time to explain additional concepts in life cycle cost calculation. These indicators can be calculated by values such as current and future net costs, discount and inflation rates, and project duration. In fact, a number of these criteria are used at certain times in the calculation of the life cycle cost. For example, we use indices such as SIR and AIRR when we want to rank the most optimal among several alternative solutions from a financial point of view. – Net savings (NS): one of the most important indicators related to LCC analysis is net savings. In fact, NS is a modified version of the net profit (NB) measure. For the purpose of further explaining, the NB criterion is used in case incoming financial flows are also considered in the investment analysis. On the other hand, the net savings criterion is used when reducing operating costs is considered in the future. As stated earlier, the NS method discounts the amount of cash flow that the proposed solution is expected to save over the useful life of the project to its present value, and other quantities that can be expressed in monetary terms and are among the advantages The proposed solution is considered as cost reduction. To calculate NS, the difference between the LCC of the proposed solution and the base case can be considered, so that NS = LCC Base Case - LCC Alternative

ð3Þ

If the answer to the above equation is greater than zero, it means that the proposed option is financially optimal. This concept can be expressed in another way; thus, when considering multiple alternatives, the solution with a higher net savings rate will always have a lower LCC. This case shows that the LCC method and the NS criterion are completely compatible with each other.

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NS also represents the reduction or increase in the cost of different parts of the life cycle costing process between the proposed options and the base case. Although this method creates more computational load than the previous one, these steps are necessary to calculate the SIR and AIRR indices. In this case, by calculating NS, calculations related to other supplementary parameters compatible with LCC calculation are checked. Also, if the NS is calculated correctly, the answer obtained from both methods is the same. In the following, the NS calculation method using individual differences is briefly described (Kneifel & Webb, 2020). N

NSA:BC = t=0

St ð1 þ d Þt

N t=0

Δlt ð1 þ d Þt

ð4Þ

which in the above equation, NSA:BC, represents the net savings amount of the proposed solution (A) compared to the base solution (BC), St is the amount of reduction in year t in operating costs associated with the proposed solution, ΔIt, other initial costs of the proposed solution in the year t, t is the year of applying the proposed solution, d is the discount rate, and finally, N is the number of years under study. Considering that the above relationship can lead to a high calculation load, for building-related projects, the following formula can be used to calculate NS: NSA:BC = ½ΔE þ ΔW þ ΔOMR þ ΔX  - ½ΔI 0 þ ΔRepl - ΔRV 

ð5Þ

In the above equation, NSA:BC is the amount of cash flow saved by deducting miscellaneous initial costs for option (A) compared to the base case (BC), ΔE is the amount of reduction in the energy costs of the proposed option and the base case, ΔW is the amount of reduction in the water cost of the option proposed and the base case, ΔOMR refers to the amount of reduction in the OM&R costs of the proposed option and the base case, ΔX is the amount of reduction in the miscellaneous costs of the proposed option and the base case, ΔI0 refers to the amount of initial miscellaneous cost of the proposed option, ΔRepl is the amount of cost required to replace the proposed option, and ΔRV is the difference between the residual cash flow value of the proposed option and the base option (Kneifel & Webb, 2020). – Savings-investment Ratio (SIR): SIR is the ratio between the savings of a proposed solution and its initial cost. This criterion is used when reduced project operating costs are considered an advantage. This criterion, like the NS criterion, is relative and should be calculated according to a base case. In this criterion, if the SIR is greater than 1, the proposed option will be economically justified. If this case is expressed in proportional form, it will be in such a way that the savings of this option are higher than the initial costs and its investment, and as a result, its net savings rate will be positive. Unlike the previous criterion, in the evaluation of several proposed options, the option with the lowest LCC does not necessarily

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have the highest SIR and should be checked on other aspects as well. Therefore, this index should not be used in choosing unique options in a project. To calculate this index, it is possible to act like the NS calculation method as follows: SIRA:BC =

N t=0

S ð1þdÞt

N t=0

Δlt ð1þd Þt

ð6Þ

In this regard, SIRA:BC, Savings-to-investment ratio of the proposed solution (A) to the base solution (BC), St, the amount of reduction in operating costs of the proposed solution in year t, ΔIt, other initial costs of the proposed solution in year t, t years of applying the solution, d is the discount rate of the project, and N is the service life of the project (Kneifel & Webb, 2020). Considering that in the above formula the incoming and outgoing cash flows are calculated every year, the following formula is suggested for construction projects: SIRA:BC =

ΔE þ ΔW þ ΔOMR þ ΔX ΔI 0 þ ΔRepl - ΔRV

ð7Þ

The important point in calculating the values of the formula above is that all values must be discounted to the present value (Kneifel & Webb, 2020). – Adjusted internal rate of return (AIRR): the AIRR measure determines the annual return on investment in a project according to the duration of the study. This criterion is also a relative criterion, like the other criteria mentioned so far. This concept means that parameters such as base year, discount rate, and inflation are considered the same for the proposed solution and the base case. In general, AIRR is equal to the discount rate used in calculating the LCC of a project. The way to analyze this index is that if the calculated AIRR is greater than the project’s MARR, the investment will be profitable. Also, if the AIRR number is equal to the MARR of the project, it means that the amount of savings of the project is equal to its costs, and as a result, the project is neutral. It should be noted that the requirements for using this index are the same as those for the SIR index, and it is generally used to rank the proposed solutions. To calculate AIRR, SIR must be calculated first; then the following relation can be used: 1

AIRR = ð1 þ rÞ:ðSIRÞN - 1

ð8Þ

In this formula, r is equal to the reinvestment rate and N is the longevity of project services (Kneifel & Webb, 2020).

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– Discounted payback (DPB): this measure and SPB are the payback measures. This criterion calculates the time required to recover the investment costs in the initial stages of the project. DPB calculates the payback period of the project by means of the amounts spent or obtained from the project each year. As stated, the output of this index will be in the form of time; as a result, if the DPB of the project is less than the study period, the project will have economic justification (Kneifel & Webb, 2020; Kneifel & Webb, 2022). – Simple payback (SPB): unlike the DPB criterion, which used discounted inflows and outflows to calculate the project’s payback period, the SPB criterion does not account for price changes during the payback period. This criterion is usually set in a certain period of time, which is much less than the study period. Assuming the discount rate is greater than 0 because the undiscounted costs and revenues are more than the discounted financial flows, the SPB index is shorter than the DPB of that project (Kneifel & Webb, 2020; Kneifel & Webb, 2022). Among the mentioned criteria, the first three are compatible with the LCC analysis. That is, they will reach the same results when determining the costeffectiveness of an alternative project solution. However, in calculating the LCC of the unique proposed options to select the optimal solution, the only criterion compatible with the life cycle costing process is the net savings rate criterion. The important thing about the explained criteria is that all of them express the relative economic performance of the options. It means that these indicators are used for a proposed solution in comparison with the base case. In the meantime, the correct diagnosis of the basic case is very important. In this choice, it should be noted that, usually, the basic model has a low investment cost while investment costs are higher than other options. To clarify, in projects that are not mandatory and are carried out due to the improvement of the overall efficiency of the building (such as the use of intelligent systems of building facilities), the initial state is the same as the existing state. But in projects that are not optional (such as the breakdown of a piece of equipment and replacing it with a new piece of equipment), it is probably the base case that is the project’s proposed replacement (Kneifel & Webb, 2020, 2022).

7.1

Some Software to Perform LCC Analysis

Since the formation of life cycle costing analysis, many researchers have been trying to provide a tool to facilitate the calculation of this economic process. These tools can include relationships and analytical methods, as well as the use of computer programs. The advantage of using a computer to calculate LCC is that long calculations can be done in a shorter time and with less error. Today, these tools are so widespread that many people use this software in their industrial and scientific projects. In the following, a number of famous and widely used software in this field will be described.

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M. H. Jahangir and R. Alimohamadi

BLCC

Building life cycle cost analysis software is a tool to facilitate life cycle cost analysis calculations provided by the National Institute of Standards and Technology (NIST). This software is provided based on ASTM standards and the NIST 135 manual. Based on its description and according to some articles, with the help of this software, various alternative solutions can be checked simultaneously, and the most suitable proposed solution can be identified based on the relationships in the LCC analysis. It should be noted that this software is capable of calculating supplementary LCC analysis criteria, such as the criteria described above, and as a result, the accuracy of the calculations is also increased (Office of Energy Efficiency & Renewable Energy, Department of Energy, n.d.).

7.1.2

EERC

Another life cycle cost analysis software is the EERC software. Considering that the energy price forecasts by the International Energy Organization are different every year, this software, for the sake of simplicity, includes a fixed price increase rate in its calculations. Then each of the proposed solutions is weighted and included in the calculations. The rate of increase according to different energy price scenarios can be determined based on information from the International Energy Agency or NIST forecasts. The important point of this software is to predict energy prices based on carbon-based policies. For more information, you can refer to this software’s website (Office of Energy Efficiency & Renewable Energy, Department of Energy, n.d.).

7.1.3

BEopt

This software, in fact, is a building energy consumption optimization software, which can also check different proposed designs and identify the lowest ones in terms of LCC. This software simulates building energy performance using the Energy Plus and Building America DOE calculation engine. These calculation tools that BEopt software uses to simulate the energy of buildings are based on different characteristics of houses, such as the type of building cover, people, HVAC systems, etc. Also, this software uses the sequential search optimization technique to find multiple optimal solutions according to the owner’s opinion (Reopt software, n.d.).

7.1.4

REopt

REopt software is a specialized software for optimizing energy systems, such as photovoltaic panels, wind turbines, grid electricity, wave converters, geothermal systems, etc., for buildings with various uses. This software identifies available strategies to reduce life cycle costs for a given location. The interesting thing

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about this software is that over the past 10 years, this software has been used to measure the potential of building more than 10,000 power plant projects, among which power plants with a total capacity of 260 megawatts have been created so far (Reopt software, n.d.).

8 Conclusion Life cycle cost analysis provides a powerful tool for the financial management of all costs related to different parts of a building, including materials, energy systems, operation, etc., in a specific study period. In fact, in order to identify financially suitable options in a construction project at all stages, this analysis is required. This analysis can show whether or not the costs caused by the changes are recoverable during the life of the project compared to the base case. Meanwhile, the following points are very important when using LCC: • It helps find the most financially optimal option among several proposed solutions. • In performing LCC analysis, the condition of estimating the minimum requirements for the project, such as financial, technical, and other requirements, must be met. • All the parameters mentioned in the previous sections, such as the discount rate, inflation, etc., should be assumed to be the same for all solutions. • In order to improve the results and make the analysis more realistic, all input costs should be correctly identified and included according to formula 2. • Parts that are not quantitative and cannot be measured with monetary units should be somehow included in the analysis. • In order to perform some LCC calculations, some supplementary features described in Sect. 7 should be calculated. • A wide range of software programs have been developed to facilitate the calculation of the LCC of a construction project, which can be used according to the type of project and design requirements. • Determining some influential parameters in the LCC process should be done based on expert judgment and experience. Therefore, in order to correctly perform life cycle cost analysis, one should have a relative mastery of the economic issues.

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Kirk, S. J., & Dell’Isola, A. J. (1995). Life cycle costing for design professionals (2nd revised ed., p. 262). Kneifel, J., & Webb, D. (2020). Life cycle cost manual for the federal energy management program. NIST Handbook, 135, 1–295. Kneifel, J., & Webb, D. (2022). The Federal Energy Management Program. Lu, K., Jiang, X., Yu, J., Tam, V. W. Y., & Skitmore, M. (2021). Integration of life cycle assessment and life cycle cost using building information modeling: A critical review. Journal of Cleaner Production, 285, 125438. https://doi.org/10.1016/J.JCLEPRO.2020.125438 Morrissey, J., & Horne, R. E. (2011). Life cycle cost implications of energy efficiency measures in new residential buildings. Energy and Buildings, 43, 915–924. https://doi.org/10.1016/J. ENBUILD.2010.12.013 New Generation Whole-Life Costing. (2006). Property and construction decision-making .... Ian Ellingham, William Fawcett. Google Books. https://books.google.com/books?hl=en&lr=& id=vWl_AgAAQBAJ&oi=fnd&pg=PP1&dq=Ellingham,+I.,+%26+Fawcett,+W.+(2006). +New+generation+whole-life+costing.+New+York:+Taylor+%26+Francis.&ots= fFY8XEZaXn&sig=WUOhs_T3FrBWAzRO-uHYfLUK-90#v=onepage&q=Ellingham%2C I.%2C%26 Fawcett%2CW.(2006).New generation whole-life costing.New York%3A Taylor% 26Francis.&f=false Office of Energy Efficiency & Renewable Energy, Department of Energy. (n.d.). https://www. energy.gov/eere/office-energy-efficiency-renewable-energy Olanrewaju, A. L., & Abdul-Aziz, A. R. (2015). Building maintenance processes and practices: The case of a fast developing country. In Building maintenance processes and practices. The case of a fast developing country (pp. 1–331). Springer. https://doi.org/10.1007/978-981-287-263-0/ COVER Pun, S. K., Liu, C., & Langston, C. (2007). Case study of demolition costs of residential buildings, 24, 967–976. https://doi.org/10.1080/01446190500512024 Reopt software. (n.d.). Google Search. https://www.google.com/search?q=reopt+software&oq= REop&aqs=chrome.1.69i59l2j0i67j0i512l7.1585j0j7&sourceid=chrome&ie=UTF-8 Schmidt, M., & Crawford, R. H. (2017). Developing an integrated framework for assessing the life cycle greenhouse gas emissions and life cycle cost of buildings. Procedia Engineering, 196, 988–995. https://doi.org/10.1016/J.PROENG.2017.08.040 Sustainable Construction: Green Building Design and Delivery. (n.d.). Charles J. Kibert. Google Books. https://books.google.com/books?hl=en&lr=&id=2xgWCgAAQBAJ&oi=fnd&pg= PR15&dq=C.+j+Kibert,+Sustainable+Construction:+Green+Building+Design+and+Delivery, +3rd+ed.,+John+Wiley+%26+Sons+Inc,+Hoboken,+New+Jersey,+USA,+2012.&ots=G9_ ucI83ou&sig=BJF_IdAiEpEwKMJopwPDnmLwXjY#v=onepage&q&f=false Tam, V. W. Y., Senaratne, S., Le, K. N., Shen, L. Y., Perica, J., & Illankoon, I. M. C. S. (2017). Life-cycle cost analysis of green-building implementation using timber applications. Journal of Cleaner Production, 147, 458–469. https://doi.org/10.1016/J.JCLEPRO.2017.01.128 Teachers, G. K. -A. F. et al. (2006). Undefined: Greening America’s Schools. usd116.org. Weerasinghe, A. S., Ramachandra, T., & Rotimi, J. O. B. (2021). Comparative life-cycle cost (LCC) study of green and traditional industrial buildings in Sri Lanka. Energy and Buildings, 234, 110732. https://doi.org/10.1016/j.enbuild.2021.110732 Whole Life-Cycle Costing: Risk and Risk Responses. (2004). Abdelhalim Boussabaine, Richard Kirkham. Google Books. https://books.google.com/books?hl=en&lr=&id=1aJehlxozw4C& oi=fnd&pg=PR5&dq=A.+Boussabaine,+R.+Kirkham,+Whole+life-cycle+costing:+risk+and +risk+responses,+Blackwell+Publishing+Ltd,+Oxford,+UK,+2004.&ots=ZQ1IhwR1r_& sig=HFS9xYyelfV8S14cwAjL7IyxH9s#v=onepage&q&f=false World Bank. (n.d.). Google Search. https://www.google.com/search?gs_ssp=eJzj4tDP1TewNM1 LUWA0YHRg8OIqzy_KSVFISszLBgBWMwcY&q=world+bank&oq=word+bank&aqs= chrome.1.69i57j46i10i199i465i512j0i10i512l7.7066j0j7&sourceid=chrome&ie=UTF-8

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Zhao, J., & Li, S. (2022). Life cycle cost assessment and multi-criteria decision analysis of environment-friendly building insulation materials – A review. Energy and Buildings, 254, 111582. https://doi.org/10.1016/J.ENBUILD.2021.111582 Zuo, J., & Zhao, Z. Y. (2014). Green building research–current status and future agenda: A review. Renewable and Sustainable Energy Reviews, 30, 271–281. https://doi.org/10.1016/J.RSER. 2013.10.021 Zuo, J., Pullen, S., Rameezdeen, R., Bennetts, H., Wang, Y., Mao, G., Zhou, Z., Du, H., & Duan, H. (2017). Green building evaluation from a life-cycle perspective in Australia: A critical review. Renewable and Sustainable Energy Reviews, 70, 358–368. https://doi.org/10.1016/J. RSER.2016.11.251

Life Cycle Cost Analysis of Nearly-Zero Energy Buildings: An Introduction to the Methodologies Hashem Amini Toosi, Jacopo Famiglietti, and Mario Motta

1 Introduction The building sector is responsible for around 40% of the final energy use and has a 6.5% share of the world economy (Elkhayat et al., 2020). The necessity of reducing energy consumption in the building sector to achieve the Sustainable Development Goals (SDG) became a consensus and has been reflected in national and international programs these days. The economic dimension is also known as a critical aspect of the sustainability concept, where the nearly-zero energy buildings (nZEBs) can contribute significantly due to their high relevancy to the country’s economic programs (Amini Toosi et al., 2020). Therefore, nZEBs are considered promising solutions to improve the performance of the building sector, and they are basically defined as buildings with extremely high energy efficiency, and the very low amount of energy required should be provided to a significant extent by renewable energy, including energy generated on-site or nearby (The European Parliament, 2018; Huang et al., 2018). In such a context, the recast Energy Performance of Buildings Directive (EPBD) requires all new buildings to reach the nZEB targets from 2021 and reach the costoptimal level in Europe (The European Parliament and Council of the European Union, 2010; The European Commission, 2016; Pernetti et al., 2021). However, the economic feasibility of such requirements for the stakeholders is still a barrier to such transitions (Pernetti et al., 2021). Performing an economic performance analysis over a building’s life cycle, called life cycle costing (LCC), is a recommended approach to verify and ensure the economic feasibility of nZEBs (Kolokotsa et al., 2009; Alsayed & Tayeh, 2019). LCC analysis can compare the

H. Amini Toosi (✉) · J. Famiglietti · M. Motta Department of Energy, Politecnico di Milano, Milan, Italy e-mail: [email protected]; [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. S. Muthu (ed.), Life Cycle Costing, Environmental Footprints and Eco-design of Products and Processes, https://doi.org/10.1007/978-3-031-40993-6_2

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costs of various investment options and make it feasible to find the most costeffective, energy-efficient design options (Liu et al., 2018). This chapter aims to review the current standards and frameworks for the life cycle costing of buildings. It discusses the different steps and requirements for performing an economic assessment of nZEBs with a life cycle approach. Therefore, several frameworks, including the relevant standards from the International Organization for Standardization (ISO) and Comité Européen de Normalisation (CEN), will be considered. A literature review of the published research works on the application of LCC methods will be elaborated. The chapter aims to clearly understand how an LCC study should be carried out for nZEBs and highlights the main methodological aspects. This study guides stakeholders to carry out a life cycle cost analysis of nZEBs concerning the relevant standards.

2 Life Cycle Costing in the Building Sector: Standards and Frameworks The cost assessment of an n-ZEB over its life cycle is a crucial step for the feasibility evaluation of new buildings and the energy refurbishment of existing ones to achieve n-ZEB targets. It is usually performed along with the life cycle cost (LCC) analysis method (Bragolusi & D’Alpaos, 2022). Considering the cost-influence curve described by Griffith and Sidwell (Griffith & Sidwell, 1995) for building and construction projects, the LCC approach becomes even more critical and relevant. A cost-influence curve indicates that as the design process proceeds to the later phases, the cost of construction and intervention increases while the influence of design scenarios to reduce the overall life cycle costs declines (Fig. 1). Therefore,

Fig. 1 The cost-influence curve in building design processes

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this means the initial phases of design (i.e., conceptual design, schematic design, etc.) have the greatest influence on reducing the life cycle cost of buildings through planning and design strategies. After the completion of the design phase and the beginning of the construction phase, any change in the building’s design will result in a higher cost and a lower potential for reducing them. Such a concept as the correlation between design phases and the cost-saving potential in buildings highlights the necessity of conducting a comprehensive life cycle cost analysis during the initial stages of developing the design scenarios of buildings, although the initial phases of the design process contain a high uncertainty since the details of the design scenarios will be developed as the design steps proceed. Furthermore, providing a commonly accepted standard for performing LCC in buildings is of paramount importance, by which the LCC methodologies and the results in different studies can be verified, replicated, and compared. Several frameworks and guidelines have been developed and published to standardize LCC in the building sector at national and international levels. Although most of the existing frameworks suggest similar procedures to carry out an LCC, there are minor variations that are worthy of investigation. This section briefly reviews the most important framework as the first step to introducing LCC methodologies in the building sector.

2.1

ISO Standards

ISO 15686-5: 2017 (2017) provides the guidelines for the life cycle cost analysis of new or existing buildings, constructed assets, and subcomponents. It also aims to standardize the relevant terminologies and elements of an LCC analysis. The main goal of the LCC, according to this standard, is for the life cycle cost of an asset (i.e., buildings, constructed assets of their subsystems) to be integrated into an evaluation and decision-making process, alongside other types of assessments, such as environmental and safety, functionality assessment, etc. According to ISO 15686-5: 2017, LCC analyses include a list of costs over a constructed asset’s physical, technical, economic, or functional life within an agreed analysis period. However, a broader set of costs, including nonconstruction costs, externalities, and income, will be referred to as the whole life cost of the constructed asset (ISO 15686-5:2017, 2017). ISO 15686-5: 2017 requires the maximum possible accuracy of the cost data, particularly emphasizing the most significant cost variables. Such cost data can be achieved through (a) direct estimation from known costs, (b) historical data analysis, (c) models based on expected performance, and (d) best guesses of future trends. The definition of the estimated service life and design life is delegated to ISO 15686-1 and ISO 15686-2. For those cases with a life span longer than 100 years, the standards suggest considering 100 years for the study. However, the definition of the service life is subject to the agreement and requirements of each project (ISO 15686-5:2017, 2017).

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ISO 15686-5: 2017 employs net present value (NPV) and net present cost (NPC) as the LCC indicator for the analysis. It also introduces other indicators and techniques for measuring life cycle costs and whole life cycle costs, such as payback period (PP), net saving (NS), saving-to-investment ratio (SIR), (adjusted) internal rate of return (AIRR), annual cost, and annual equivalent value. As the discount rate is an essential factor in this method, this standard requires performing a sensitivity analysis using a range of rates to check the validity of the conclusion under various input conditions unless the rate is a fixed requirement. The main factors that have the most significant effect on uncertainties to be checked are the discount rate, the analysis period, and the assumption related to service life, maintenance, repair/ replacement, and cost data. This standard indicates that a discount rate between 0% and 4% is usually used, and it highlights that selecting a higher discount rate discourages long-term investments (ISO 15686-5:2017, 2017).

2.2

CEN Standards

EN 15643–4:2012 (2012) provides a framework for assessing the economic performance of buildings as an integral part of the sustainability assessment of construction work. The main objectives of an economic evaluation in this framework are: (i) to identify the economic aspects and impacts of a building and its site and (ii) to enable the user and designer to make informed decisions toward building sustainability (EN 15643-4:2012, 2012). This standard includes two types of indicators for economic performance in terms of the cost and financial value of the building over its life cycle. The framework provides a list of potential indicators that could be proposed as a basis for future standard development, although they are neither definitive nor completed yet (EN 15643-4:2012, 2012). The standard requires defining the system boundary in the assessment in accordance with the scope of the evaluation. It also emphasizes the requirement of defining the functional equivalent of the study for the basis of the comparability of different assessments. According to this standard, the functional equivalent of the building or the subsystems should include but not be limited to information about the building type, the pattern of use, the relevant technical and functional requirements, and the required service life (EN 15643-4:2012, 2012). This standard provides a list of different cost types and information required in each life cycle module (illustrated in Fig. 2), including the pre-constrcution, production, and construction phases (modules A0–A5); use stage (modules B1–B5); operational use stage (modules B6 and B7); end-of-life stage (modules C1-C); and beyond the system boundary (module D) (EN 15643-4:2012, 2012). Table 1 represents the list of cost data to be included in each life cycle module (EN 15643-4:2012, 2012).

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Fig. 2 Building life cycle modules and information. (Adopted from EN 16627)

This standard also demands specific data quality requirements, verification, transparency of the methods, and reporting and communication with external references. EN 16627:2015 (2015) provides the calculation methods for assessing the economic performance of buildings with a life cycle approach. This standard describes two approaches to economic performance. The first approach is life cycle costing (LCC) for evaluating the costs over the life cycle, also considering the negative cost of energy exports, reuse, and the recycling of building components through the whole life cycle stages as a mandatory indicator. The second approach is the life cycle balance, which considers the first approach, in addition to the incomes over the whole life cycle stages, as an optional indicator (EN 16627:2015, 2015). The standards provide guidelines for defining the system boundary of an LCC analysis for both new and existing buildings and the requirements for scenarios in which the required service life and study period of the LCC analysis do not match. It also provides a method for defining the number of necessary replacements for the building components. EN 16627:2015 requires considering the building-related energy flows aligned with the Energy Performance Buildings Directive (EPBD) in the B6 module, including heating, domestic hot water supply, air conditioning, mechanical ventilation, artificial lighting, auxiliary energy used for pumps, control, and automation. This standard also demands considering the building-related energy data not covered in the EPBD (e.g., safety installation, etc.) in module B6 and reporting them separately. Also, in case non-building-related energy (i.e., computers, washing machines, etc.) are considered, their related results should be reported separately (EN 16627:2015, 2015). To align with EPBD, EN 16627:2015 assumes that on-site energy generation should first be considered to satisfy the building-related energy demand and then non-building-related energy. The standards require not to deduct the exported energy from the required imported energy to operate the building but to report the income of

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Table 1 The cost data in each life cycle module Modules Modules A0, A1– A5

Modules B1–B5

Modules B6 and B7

Modules C1–C4 and D

Cost data to be included in each life cycle module Costs directly related to the purchase or rental of the site Costs directly related to the purchase or rental of the site Costs incurred between factory and site Professional fees Temporary and enabling works Construction of asset Initial adaptation or fit out of asset Landscaping, external works on the curtilage Taxes and other costs related to permission to build Subsidies and incentives Building-related insurance costs Leases and rentals payable to third parties Cyclical regulatory costs Taxes Subsidies and incentives Revenue from the sale of assets or elements but not part of a final disposal Third-party income during operation Repairs and replacement of minor components/small areas Replacement or refurbishment of major systems and components Adaptation or subsequent fit out of asset Cleaning Ground maintenance Redecoration Disposal inspections at the end of the lease period (excluding end-of-life final disposal); End of lease Planned adaptation or planned refurbishment of assets in use Building-related facility management costs Operational energy costs (as defined by EPBD-related standards) Operational water costs Taxes Subsidies and incentives Deconstruction/dismantling, demolition Transport costs associated with the process of deconstruction and disposal Fees and taxes Costs and/or revenues from reuse, recycling, and energy recovery at the end of life Revenue from sale land

Adopted from EN 15643

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the energy export and any subsidy or incentives in module B6. It also does not make a distinction between the energy generation systems that are a part of the building fabric and those that are not (EN 16627:2015, 2015). EN 16627:2015 includes some indications for selecting the macroeconomic parameters. It requires using a real discount rate of 3% for the sake of the comparability of the net present value (cost) of studies, although it allows performing additional calculations using other values for the discount rates. This standard does not suggest a specific value for escalation rate, indicating that different rates may be used building components or services (EN 16627:2015, 2015). The standard uses net present value (NPV), net present cost (NPC), annual cost (AC), and annual equivalent cost (AEC) to measure the economic performance of buildings. It also provides a list of other possible indicators that are not in the scope of the standard but can be used for other aspects of the economic assessment (EN 16627:2015, 2015). EN 15459:2007 (2007) provides detailed guidelines on economic evaluation procedures for energy systems in buildings. It aims to standardize evaluation methods and practices to be fully or partially applied in the economic feasibility assessment of energy-saving options. It also permits a comparison of the different energy-saving solutions, the evaluation of the overall economic performance, and the assessment of the possible energy-saving measures on energy systems in buildings. The main structure of the relevant costs in EN 15459: 2007 is shown in Fig. 3 (EN 15459:2007, 2007). EN 15459: 2007 takes into account two main categories of costs covering the initial investment and replacement costs, including building construction related to energy savings (e.g., construction materials, insulation, etc.) and the cost associated with the installation of energy systems. The second cost category must include all running costs related to the operation of the energy system, maintenance, and metering (EN 15459:2007, 2007). EN 15459: 2007 uses global cost and annuity cost indicators to evaluate the life cycle cost of the energy systems in buildings. It provides guidelines for the calculation steps of the main indicators and other economic parameters, such as discount and annuity factors. It also presents a list of the standard technical life span of a variety of energy systems in buildings in terms of the years to be considered in developing replacement scenarios, alongside the value of yearly maintenance and end-of-life disposal costs in terms of the percentage of the initially required investment of each system (EN 15459:2007, 2007).

2.3

Level(S)

Level(s) is a common framework of the Joint Research Centre-European Commission for the sustainability assessment of buildings with a life cycle perspective. It includes six macroobjectives, including (1) greenhouse gas emission over the building’s life cycle, (2) resource-efficient and circular material life cycle, (3) efficient use of water resources, (4) healthy and comfortable spaces, (5) adaption and

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Fig. 3 The structure of the main relevant cost in building and energy systems. (Adopted from EN 15459)

resilience to climate change, and (6) optimized life cycle cost and values. Level (s) framework considers three levels of the building life cycle, including Level 1 – conceptual design, Level 2 – detailed design and construction, and Level 3 – as-built and in-use stage (Dodd & Donatello, 2021). Regarding Life cycle costing, at the first level, it can be applied to calculate and understand the life cycle cost and long-term perspective on the cost of buildings, alongside the incorporation of main LCC concepts into conceptual and subsequent design phases. At the second level, it can be applied to calculate the life cycle costs of buildings, select tools and databases, and understand the calculation steps based on EN 15459 and ISO 15686-5, including the assumption and the parameters to be used. At the third level, it can be applied to revise the life cycle costs and projections based on monitoring data and to report the life cycle costs for the building. The life cycle cost objective in Level(s) includes construction, operation, maintenance, refurbishment, and disposal. It encourages integrating the sustainability aspects into the risk rating process and market value assessment of the buildings, emphasizing the transparency of the provided information. The LCC indicators proposed by Level(s) include the life cycle cost of euro / (m2 * year), value creation, and risk factors (Dodd & Donatello, 2021).

Life Cycle Cost Analysis of Nearly-Zero Energy Buildings. . .

2.4

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Other Frameworks

Apart from the standards and framework described previously, there are several guidelines for conducting an LCC study developed by public, private, or academic entities. Most of these guidelines follow one or more of the standards in general; however, they might have specific requirements related to the goals of their guidelines. The framework published by the US National Institute of Standards and Technologies (NIST), known as Building for Environmental and Economic Sustainability (BEES 4.0) (Lippiatt, 2007), aims to provide a guideline for measuring the environmental and economic performance of buildings. The study period proposed by these guidelines covers 50 years. The system boundary of the LCC study in this framework starts with the purchase and installation of the products and ends at the end of the study period. It does not consider the end-of-life phase as a requirement, which means all life cycle phases related to raw material acquisition, production, and endof-life processing are omitted from the study (Lippiatt, 2007). The limited system boundary, as described, is one of the main differences between economic and environmental performance assessments highlighted in this framework. BEES uses net present value (NPV) to measure the project’s life cycle costs over the study period and requires using a real discount rate equivalent to 3.0%, as mandated by the US Office of Management and Budget for most federal projects (Lippiatt, 2007). Stanford University provided LCC procedure guidelines to be applied to different phases of design and building ownership, including scoping, feasibility and programming, schematic design, design development, construction documents/permitting, construction, closeout, and ownership. The different goals and tasks of LCC in each phase are outlined. The overall LCC process in this guideline includes establishing the objectives of the analysis, determining the criteria for evaluating alternatives, identifying and developing design alternatives, gathering cost information, and developing the life cycle cost for each alternative. This guideline also provides reference values for each building subsystem’s useful technical life span, e.g., envelope, heating, ventilation and air conditioning (HVAC) systems, electrical systems, etc.). These values can be used to define the maintenance and replacement schedules as they affect the life cycle cost of buildings (Stanford University, 2005). This guideline also uses NPV as the method and indicator for measuring life cycle costs and provides a procedure for calculating payback time in building projects. However, residual value (the estimated value of the building components at the end of service life) is included in formulating LCC, considering it equal to 0. This guideline also provides reference values for the study period of different buildings equivalent to 30, 15, and 10 years for new construction projects, retrofitting/renovation projects, and labs/high-tech buildings, respectively (Stanford University, 2005).

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3 Life Cycle Costing Methodology: Indicator Selection and Calculation As elaborated in the previous sections, multiple institutes and organizations have published several standards and guidelines for conducting the life cycle cost analysis of the building sector energy systems. Many similarities are found among the reviewed guidelines; however, this section aims to discuss the main steps in performing an LCC study in energy-efficient buildings and to review the state-ofthe-art for the assumption and application of these guidelines. Through such a discussion, the required main consideration of life cycle costing in nZEBs will be clarified, and different techniques implemented in the literature will be highlighted. The main steps are described as follows: the definition of the goals, the definition of the scope, i.e., defining the system boundary and the life cycle modules to be included, the assumption regarding the macroeconomic parameters, selection of the economic performance indicator, etc., and finally reporting the results and discussion.

3.1

Definition of the Goals

The main goals of a life cycle cost analysis at the building level are as follows: (i) providing support for the decision-making process by comparing the economic performance of design options, (ii) identifying the potential of improving building performance (refurbishment scenarios), (iii) determining the required budgets, documenting the economic performance of buildings, and (iv) providing support for the development of policies (EN 16627:2015, 2015). According to EN 16627:2015, the scope of life cycle costing should include all building components and connections to the utilities between the building and the site boundary that affect the relevant costs of the building; however, construction works beyond the boundary of the building site shall not be included (EN 16627: 2015, 2015). Since the functional unit of the assessment has a significant impact on the comparability of the results, the functional unit should be the same if the life cycle costing and the evaluation of other sustainability dimensions (i.e., environmental and social) are considered to be combined. In any case, the standard requires taking into account the building type, the relevant technical and functional requirements, the pattern of use, and the required service life. EN 16627 also provides the indications for considering the difference between the required service life and the reference study period. The decision to define the reference study period might be indicated by national regulations or the purpose of the assessment. Therefore, several different assumptions regarding the reference study period are observed among published studies (EN 16627:2015, 2015).

Life Cycle Cost Analysis of Nearly-Zero Energy Buildings. . .

3.2

37

Definition of the Scope and the System Boundary

The system boundary of the life cycle cost analysis of new buildings includes the life-cycled modules illustrated in Fig. 3. While assessing the existing building works (e.g., retrofitting), the evaluation will consist of all the related costs of the interventions, along with the expenses within the later stages of the building life cycle. All the costs related to the operational energy use of the building should be calculated in compliance with the Energy Performance of Buildings Directive (EPBD). The building-related energy services that should be included in the assessment, according to EPBD, are space heating, domestic hot water supply, space cooling, ventilation, artificial lighting, and auxiliary energy consumption (i.e., pumps, control, and automation). The building-related energy consumption that EPBD does not cover should be included in the assessment and reported separately. Likewise, if non-building-related energy is included in the energy consumption calculation, it should be reported separately. Moreover, supposing the building is equipped with on-site energy generation systems (i.e., photovoltaics, etc.), in that case, the generated energy should be assumed first to satisfy the building-related energy demand and then supply non-building-related energy. The exported energy from on-site energy generation systems cannot be considered as compensation for the required imported energy, but the economic profits (revenue), subsidies, and incentives should be included in the operational energy use (module B6). Likewise, all operational energy costs described above must be included in module B6 (EN 16627:2015, 2015; EN 15459:2007, 2007).

3.3

Macro- and Microeconomic Parameters

Life cycle costing highly depends on the choice of macro- and microeconomic parameters applied in calculating economic costs and values (Baldoni et al., 2019). Determining such parameters, however, depends on many factors, including the following: • internal factors of the building projects for which the assessment should be carried out; • external factors related to the project’s economic context, such as the market, alongside fluctuations over the time horizon of the assessment (Amini Toosi et al., 2020). Those complexities indeed can affect the results highly and therefore are among the main parameters that should be defined reasonably with respect to the economic context and the study period of the assessment (Amini Toosi et al., 2021). Some guidelines designed for a specific type of construction activities in certain economic contexts may propose values for macroeconomic parameters, such as inflation rate,

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Fig. 4 Discount rate implemented in the reviewed research studies

interest rate, and discount rate, to be included in the life cycle cost analysis. However, the review of the published studies highlights that different researchers might consider a wide range of values for such parameters (Figs. 4 and 5 and Table A1). EN 16627 proposes using the real discount rate of 3% for comparability among different assessments. However, the assessor can also adopt other values for additional analyses based on consultations with the client and justified by commercial, political, regulatory, and sustainability-related objectives or requirements. The selection of a lower discount rate encourages higher initial investment, which can yield a lower operating cost for the buildings in the future since the future cost and benefits of the building will get a lower discount factor, and therefore, it will result in higher values in terms of net present value at the time of life cycle costing. A research study (Copiello et al., 2017) demonstrated that the impact of the discount rate on life cycle cost analysis is approximately four times greater than the impact of the price of electricity. This result reaffirms the importance of choosing macroeconomic parameters in an LCC analysis. It is advised to select the macroeconomic parameters in accordance with each project’s economic context to have a robust LCC analysis; nonetheless, theoretical studies and sensitivity analysis to assess the cost-effectiveness of different design options under various economic outlooks are deserving of investigation (Amini Toosi et al., 2020).

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Fig. 5 Energy price inflation rate (EnPIR) implemented in the reviewed research studies

3.4

Indicator Selection and Calculation

Several indicators to assess the economic performance of buildings, including nZEBs design options and building refurbishment projects, have been proposed by standards and guidelines and implemented in different studies. However, the choice of the LCC indicator may depend on how the design scenarios should be compared, and the results need to be reported. According to EN 16627, net present value (NPV) is a standard indicator for life cycle costing, which takes into account all the discounted future cash flows, including the cost and revenues across the building life cycle and study period. It can be used to determine and compare the economic performance of different design options and show the design scenarios’ overall life cycle cost and benefit (EN 16627:2015, 2015). The standards also suggest other LCC indicators and can be found in the literature, such as net saving or net benefit, saving-to-investment ratio (SIR), payback period, and adjusted internal rate of return (AIRR), as described in ISO 156865 (2017). Net present value (NPV) is the most used LCC indicator within the literature, which helps investors compare the present value of the economic performance of the design options in terms of total cost or values (Table A1). Also, the payback period provides a clear vision of the time horizon when the initial cost and anticipated

2014

2015

2015 2015 2016

2016

2016 2016

Koo et al. (2014)

Jafari and Valentin (2015) Brás et al. (2015) Koo et al. (2015) Jafari et al. (2016)

Lohse et al. (2016)

Zhivov et al. (2016) Jafari and Valentin (2016) Liu et al. (2016) Copiello et al. (2017)

2017

2017 2017

Krarti and Dubey (2017) Fregonara et al. (2017)

2017

Jafari and Valentin (2017) Lucchi et al. (2017)

2016 2017

Year 2011

References Ouyang et al. (2011)

Oman Italy

United States Italy

United States Portugal South Korea United States Austria, Germany EU, US, CN United States Sweden Italy

South Korea

Country China

Table A1 Summary of the reviewed articles

20 30

50

15

N.S. 30

25 30

33

5, 10, 15 40 5–25

50

40

Study period 40

Present value NPV, initial cost (purchasing material, the energy cost of the operation phase, excluding the repair and maintenance phase) NPV, initial investments costs, energy consumption costs, maintenance and replacement costs, resale benefits and property tax, rebound effect NPV, global costs, payback period time, initial investment and energy consumption costs Energy productivity (EP) Net present costs (NPCs), net saving (NS), saving-to-investment ratio (SIR), adjusted internal rate of return (AIRR), simply payback period (SPB), initial costs, construction and nonconstruction costs, operation costs, excluding the residual value of components

NPV Investment costs

NPV, payback time NPV and SIR NPV, optimization and sensitivity analysis on service life span, available budget and discount rate Net present value (NPV)

Indicator/notes NPV and IRR Different internal rate of return (IRR) is considered. Initial investment cost, NPV, saving-to-investment ratio, compound annual growth rate (CAGR), and improved CAGR NPV, initial investment and energy consumption costs

NS NS

NS

0–3 3 2.5

5

2.6

NS 0–4.5

0–3 NS

0–7 NS 0–10 0–15

0–4

4 NS NS

5

NS

En-PIR Ns

2.5

6 N.S 0–6

2.6

Ns

DR 6.6

40 H. Amini Toosi et al.

Sweden Sweden Sweden Belgium

2019

2019 2019 2019 2019

Seghezzi Re-Cecconi (2019) Nägeli et al. (2019) La et al. (2019) La Fleur et al. (2019) Van De Moortel et al. (2019)

Morocco Iran Italy Morocco China

Italy

2018 2019 2019 2019 2019

Robati et al. (2018) Zheng et al. (2019) Song et al. (2019) Bleyl et al. (2019) Yuan et al. (2019)

2019 2019 2019 2019 2019

Sweden South Korea United States Australia China South Korea Austria Singapore

2018 2018 2018

Dlimi et al. (2019a) Kavian et al. (2019) Baldoni et al. (2019) (Dlimi et al., 2019b) Duan et al. (2019)

Turkiye

2018

Yılmaz and Koçlar Oral (2018) Mili et al. (2018) Oh et al. (2018) Kneifel et al. (2018)

Canada Italy

2017 2018

Ruparathna et al. (2017) D’Orazio et al. (2018)

20 20 30 20 40

35 40 40 60

NS

50 NS 40 25 NS

50 25 30

30

15–100 NS

NPV Present value Present value Sum of the present value (SPV), sensitivity analyses on building service life span, energy price, discount rate, insulation level of the baseline building NPV NPV NPV, PP NPV NPV

NPV NPV and value at risk (VaR) NPV NPV NPV for different electricity price and discount rate scenarios Results are provided and ranked both according to energy-saving and cost-effectiveness potential, NPV

NPV NPV, SIR, and break-even point NPV

NPV NPV, inflation rates, discount rates, price escalation rates, and different tax deduction scenarios NPV

2.25 13 3 2–10 2.7

4 0–10 5 2

NS

7 8 2.54 4.5 4–12

5 3.3 3

NS

3.5–8 1.9–6

(continued)

1.8 7 2.8 0.1–2.5 0.9

2 0.5–2 NS 3

NS

3 NS NS NS NS

NS 3.3 8

NS

NS NS

Life Cycle Cost Analysis of Nearly-Zero Energy Buildings. . . 41

US Iran China China Morocco India Turkiye Italy

Italy

2021 2021 2022 2022 2022 2022 2022 2022

2023

India China Nigeria

2021 2021 2021

Malaysia

Switzerland UK Sweden

2020 2020 2021

2021

Country Cyprus Canada

Year 2020 2020

En-PIR energy price inflation rate

Balasbaneh and Sher (2021) Padovani et al. (2021) Hadi and Heidari (2021) Jiang et al. (2022) Chen et al. (2022) Kharbouch et al. (2022) Satola et al. (2022) Acar and Kaska (2022) Amini Toosi et al. (2022) Amini Toosi et al. (2023)

References Rezaei et al. (2020) Alshamrani and Alshibani (2020) Galimshina et al. (2020) Cui et al. (2020) Gremmelspacher et al. (2021) Saboor et al. (2021) Huang et al. (2021) Akinsipe et al. (2021)

Table A1 (continued)

30

20 30 50 20 30 50 30 30

50

20 20 20

60 25 25

Study period 20 20

NPV

Total life cycle cost, NPV NPV, IRR NPC Life cycle cost saving rate NPV, PP NPV NPV, NS, PP NPV

LCS, PP NPV, PP Levelized cost (LLC), annualized levelized cost (ALLC), and the cost of energy (COE) NPV

NPV NPV NPV

Indicator/notes NPV, NPS, NPI, and SIR NPV

3.68

0–8 5.6 8.5 3.85 2 6 17.75 3.68

3.2

2.5 8.75 -0.4 to 6.4 6.25 5 10

DR 2 5

2.1

2 2 2 1 0.7 3 12.28 2.1

3

0.5–2 6 -0.5 to 2.4 3.4 5–8 8.1

En-PIR 2–5 2

42 H. Amini Toosi et al.

Life Cycle Cost Analysis of Nearly-Zero Energy Buildings. . .

43

benefit will be equal, showing the year when the investment is compensated and returned through the economic benefits of a design option. The payback period and SIR indicator can also provide clear indices for the comparability of economic benefits in energy retrofitting design options.

3.5

Reporting and Communication of Results

The results of life cycle costing shall be reported in a document, which can be supported visually. The report should be transparent and contain traceable information used in the assessment process. The reporting must provide information such as: (i) the purpose of the assessment, including the intended use and scope; (ii) identification of the building, (iii) the life cycle phase, where evaluation is carried out; (iv) the date and temporal validity of the assessment, alongside the verification and identification of the clients, assessor, and verifier (EN 16627:2015, 2015). Moreover, any assumptions, such as the reasons for including and excluding building services or life cycle stages, along with data source and quality, shall be reported clearly. EN 16627 provided the minimum level of disaggregation of information required to be reported in an LCC documentation (EN 16627: 2015, 2015).

4 LCC Implementation: Barriers and Uncertainties Several barriers and uncertainties may exist in the performance of an LCC study, mainly attributing to data accessibility, data quality, and uncertainty levels associated with input data (Amini Toosi et al., 2020). Predicting macroeconomic parameters, such as the inflation rate of the prices and discount rate required for life cycle costing over the study period, is a critical challenge that can affect the reliability of the results. Furthermore, a reasonable choice for the cost of construction activities, building systems, energy prices, etc. requires a deep and updated understanding of the construction market and its fluctuations. Such data can be challenging to estimate due to their dependency on the socioeconomic and political realities of the construction market. Moreover, the cost prediction of future maintenance work over the building’s lifespan is challenging. Such data, however, are crucial for the performance of the life cycle cost analysis of buildings and should be gathered from reliable sources, justified, and reported reasonably and clearly to guarantee the traceability, replicability, and verification of the assessment (Amini Toosi et al., 2020). Future studies should examine additional factors, including the performance degradation of building components, the residual value of building components at the end of their useful lives, and economic policies, like tax incentives. These factors have not received as much attention as they should. Another factor that is rarely

44

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discussed is the salvage value of building components. Koo et al. (2014) removed this component by assuming that salvage value and disposal costs would balance one another out. Other characteristics, such as the rebound impact and resale advantages, are taken into account in a few articles (Amini Toosi et al., 2021).

5 Conclusion The development of the built environment due to escalating population growth and urbanization makes the building sector a significant driver of economies worldwide. In this context, a comprehensive economic assessment of the building sector is crucial to designing buildings and policies. This chapter aims to review and discuss the existing frameworks and methodologies for the life cycle cost analysis of buildings and clarify the advances and barriers to implementing life cycle cost in designing nZEBs. The chapter highlighted the main methodological steps and requirements for conducting the life cycle costing of the buildings and energy systems. Different aspects, such as the definition of the goals and scope of the study, the macroeconomic parameters, and life cycle cost indicators, alongside the requirement of reporting, were elaborated and discussed. The chapter also detailed the several life cycle cost indicators proposed and implemented in different frameworks and studies, including net present value, payback period, saving-to-investment ratio, etc. Still, the net present value was found to be the most popular and recommended indicator in the current standards and the reviewed studies to evaluate the economic performance of nZEBs. It supports the decision-making process by comparing the economic performance of design options and by identifying the potential of improving building performance (refurbishment scenarios). Moreover, a wide range of macroeconomic parameters, such as the discount rate and energy price inflation rate, were found in the literature to analyze the impact of the uncertainty level of macroeconomic parameters on the final life cycle cost results, as recommended by standards. The main standards require opting for a similar discount rate for the comparability of the results. Nonetheless, the different macroeconomic variables employed in the literature offer insights into the diversity of Life cycle costing (LCC) results across various economic settings, while rendering the comparison of results a challenging endeavor. Therefore, for the comparability of life cycle cost results, different studies should follow relevant standards in: (i) defining the scope and (ii) selecting macroeconomic parameters, as well as provide a comprehensive and transparent report that includes all assumptions applied in the calculation process. This chapter highlighted the main requirements for conducting a life cycle cost study of buildings aiming at clarifying the life-cycle-based economic performance analysis steps for the stakeholders. It also encourages the assessment of the life cycle cost performance of buildings according to existing standards and innovative approaches for the sake of comparability and reliability of results, alongside the

Life Cycle Cost Analysis of Nearly-Zero Energy Buildings. . .

45

possibility of improving the methods established by the current frameworks within future scientific research studies.

Appendix Table A1 summarizes the methodological aspects of the reviewed papers, including the selected study period, the macroeconomic parameters, and the LCC indicators used for the economic assessment of the performance of buildings and energy systems.

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Analysis of Life Cycle Cost, Barriers and Strategies Towards Enhancing Its Application: Case Study of Commercial Buildings Achini Shanika Weerasinghe and Thanuja Ramachandra

1 Introduction The concept of life cycle costing (LCC) originated several decades ago and was initially applied by the United States (US) Department of Defence in the mid-1960s for military equipment procurement (Cole & Sterner, 2000). Later, it was introduced in the building sector, which was primarily used for comparing different energy design options (Korpi & Ala-Risku, 2008). LCC is a systematic technique for evaluating costs over a defined analysis period, as outlined by the British Standard Institution (BSI, 2017). LCC plays a crucial role in investment decision-making in the building industry, ensuring that choices are not solely based on initial capital costs (Al-Hajj & Horner, 1998). By considering both initial and operating costs, LCC can result in more cost-effective buildings in maintenance, operation, occupancy, and disposal (Kehily, 2010). Given the significant impact of running costs on building occupiers’ budgets, using LCC in buildings is essential (Goh & Sun, 2015). However, its adoption and application in the sector remain limited, primarily due to the lack of reliable and consistent data and the practitioners’ incomplete understanding of its benefits (Al-Hajj & Horner, 1998; Joseph, 2010; Kishk & Al-Hajj, 1999). Cross-case studies in the field of LCC are relatively scarce, often focusing on a single industry and addressing only superficial aspects of the life cycle cost analysis. For example, a Finnish study found that only 5% of large industrial companies utilised LCC, while a Swedish study in the building industry reported 66% of companies using LCC for decision-making support. In a US study, 40% of city A. S. Weerasinghe (✉) School of Built Environment, Massey University, Auckland, New Zealand e-mail: [email protected] T. Ramachandra Department of Building Economics, Faculty of Architecture, University of Moratuwa, Bandaranayake Mawatha, Moratuwa, Sri Lanka © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. S. Muthu (ed.), Life Cycle Costing, Environmental Footprints and Eco-design of Products and Processes, https://doi.org/10.1007/978-3-031-40993-6_3

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administrations used life cycle cost analysis to assess building projects (Korpi & Ala-Risku, 2008). Similarly, decision-making support tools in buildings are often deemed inadequate due to challenges in implementing LCC and the methods devised to overcome these challenges (Kishk & Al-Hajj, 1999). Collecting reliable and consistent data on running costs, benchmarking operation and maintenance expenses, and analysing their behaviour can facilitate LCC implementation in buildings (Kehily, 2010). Building-specific models are necessary to address actual practices and requirements, and LCC models would gain wider acceptance if they offer speed, ease of use, and estimates derived from various levels of available data and information (Al-Hajj & Horner, 1998). Thus, this study aims to investigate the application of LCC, identify barriers, and propose strategies through a case study of commercial buildings in Sri Lanka.

2 Literature Review 2.1

The Concept of LCC

The concept of LCC was first developed in the US Department of Defence in the mid-1960s and later adapted for building investments (Gluch & Baumann, 2004). LCC involves comparing the cost-effectiveness of different investment options, considering initial and future operational costs over a specified period (Norris, 2001). It is a decision-making tool, particularly for financial considerations (Sterner, 2000). The LCC approach encompasses not only investment costs but also operating and maintenance costs throughout the estimated lifespan of a product (Gluch & Baumann, 2004; Norris, 2001). There is a defined process for calculating and analysing life cycle costs. LCC can be categorised into two main stages: LCC during the construction phase and LCC during the asset utilisation phase. The former entails initial capital costs, while the latter includes costs related to occupancy, operation, and end of life (Dell’Isola & Kirk, 2003). Capital costs refer to the expenses associated with acquiring or constructing an asset (Griffin, 1993; Woodward, 1997), operating costs encompass the ongoing expenses of running a building, and maintenance costs involve the expenditures to keep it in good condition (BSI, 2017; RICS, 1986). End-of-life costs are associated with asset disposal (Langston, 2011). LCC can be further divided into elements, such as life cycle cost analysis (LCCA), life cycle cost planning (LCCP), and life cycle cost management (LCCM) (Kehily, 2010).

2.2

Methods for Calculating and Evaluating LCC

Specific data on investment costs, operation and maintenance costs, and projectrelated variables are required to calculate LCC, as specified in Table 1. These variables include service life, life cycle and design life, period of analysis, discount

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Table 1 Examples of input data needed to calculate LCC for a building Investment cost data Building costs Site costs Design fees Salvage value Demolition costs Others

Operation and maintenance data Administration Energy Water Wastewater Material Cleaning Maintenance Others

Project-specific data Type of building Type of design Type of building material Location Lifetime periods Other specific data

Adapted from Gluch and Baumann (2004), Schade (2014)

rate, inflation, taxes and subsidies, residual value, and utility costs, including energy expenses (BMCIS, 1984; BSI, 2017). The discount rate is a crucial factor that significantly impacts the result as it accounts for the time value of money and the effects of inflation (Dell’Isola & Kirk, 2003). Selecting an appropriate discount rate is crucial because using a too-high or too-low rate can introduce bias into decisionmaking (Flanagan et al., 1987). Accurate LCC calculations necessitate reliable data and methods to reduce uncertainty in cost variables. Various methods can be employed to evaluate LCC, including payback, net present value (NPV), equivalent annual cost, internal rate of return (IRR), and net saving (Schade, 2014). Among these methods, the NPV approach is considered the most suitable for evaluating LCC.

2.2.1

NPV Method

The NPV method involves discounting cash flows to their present value using a required rate of return, and a positive NPV indicates that the investment is worthwhile. In the context of LCC analysis, costs are treated as positive and income as negative, and typically, the alternative with the highest NPV is considered the optimal choice (Law & Smullen, 2008). The advantages and disadvantages of using the NPV method are extensively discussed in the literature and can be conveniently summarised in Table 2. When calculating LCC, it is essential to discount all future intervention costs to a typical time base using NPV formulas. These calculations incorporate escalation and discount rates to account for variations in operations over different periods, enabling the evaluation of diverse systems, solutions, and building options within a specified study duration (Kehily, 2010). LCC calculations rely on predicting the deterioration of building elements and services and estimating the discounted cost of each required intervention, which introduces uncertainty due to numerous assumptions (Kehily, 2010).

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Table 2 Uses, advantages, and disadvantages of NPV Advantage Considers the time value of money Produces a return equal to the market interest rate

Disadvantage Cannot be used when the comparing alternatives have different lifetimes Difficult to interpret

Usability Used by most LCC models Used as a decision-making tool

Uses all available data Easy to carry out Much understandable among all stakeholders Adapted from Schade (2014)

2.2.2

Sensitivity Analysis

Sensitivity analysis is a technique employed to examine how uncertainties in model inputs contribute to uncertainties in the model’s output (Saltelli, 2002). In the context of LCC, sensitivity analysis is used to assess how variations in specific independent variables impact the values of the dependent variable (Law & Smullen, 2008). By conducting sensitivity analysis, the aim is to enhance the accuracy of LCC calculations by evaluating the influence of different variables on the overall results (Gluch & Baumann, 2004). Combining the results of LCC with sensitivity analysis provides a more comprehensive understanding of the potential effects of various variables on the overall economic viability of a building or construction project. The degree of uncertainty associated with LCC depends on the quality of available data, the reliability of pricing assumptions, and the calculation methods (Flanagan et al., 1987). Sensitivity analysis is valuable for assessing how uncertainties across different factors can affect the options under consideration and for identifying the input data that significantly impact the LCC result (Kehily, 2010). It is a crucial guide for identifying additional information requirements and the most critical assumptions made (Lockie, 2008). Sensitivity analysis reveals the vulnerability of LCC to variations, and if alternative variables have minimal influence on recommendations, the decision remains unaffected (BSI, 2017). Despite the inherent uncertainty in cost variables, sensitivity analysis can mitigate uncertainties in the final LCC result.

2.3

Barriers and Strategies to LCC

The difficulties and constraints related to LCC have gained significant acknowledgement, prompting researchers to suggest several enhancements. Making informed decisions during the initial stages of design and procurement is crucial to

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Table 3 Barriers and strategies in LCC Barriers to LCC applications in buildings Practitioners’ imperfect understanding of LCC’s merits Lack of reliable and consistent data Lack of incentives to use LCC

Limited experience in using LCC calculations Lack of accepted industry standards for describing life-cycle behaviour Inconveniencing in collecting the historical data of the building and lack of conformity base calculations and discount rate Lack of hard and soft data (universal methods, standard formats and useful software) Lack of attitude and perceptions of the particular occupancy in the building

Strategies to improve LCC applications in buildings Educate practitioners concerning budget parameters, including the life cycle cost aspects of operating and maintaining it Maintain in-house databases and introduce cost data on similar buildings Provide strong financial support, such as alternative contract procurement arrangements, public-private partnerships (PPPs) and the private finance initiative (PFIs) from investors and developers in the lifetime performance of buildings and infrastructures Conduct a whole life cost forum for the exchange of data and feedback Apply sensitivity analysis, expected value analysis, and risk analysis using simulation models for the handling of uncertainty LCC data should be made available prior to the design stage, encompassing both cost and performance data. Introduce databases that are compatible with LCC calculation models Provide adequate data regarding supply management and link them with business strategies

Adapted from Al-Hajj and Horner (1998), Cole and Sterner (2000), Gluch and Baumann (2004), Goh and Sun (2015), Joseph (2010), Sterner (2000)

ensure that the building’s costs over its entire life cycle are considered (Ashworth, 1993). Additionally, it is essential to continuously monitor and optimise LCC throughout the facility’s service life as assessing every aspect of LCC is impracticable (Cole & Sterner, 2000). Furthermore, having access to relevant cost data and performance standards is vital for enhancing the accuracy and dependability of LCC calculations. These barriers and strategies are given in Table 3.

3 Methods The study adopted a case study approach, conducting eight semi-structured interviews with LCC practitioners and utilising document analysis to examine the application of LCC, barriers, and strategies.

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Case Study

A case study was deemed suitable for exploring less-explored research areas, and the selection of cases could vary depending on the research objectives. This study focused on commercial buildings in Sri Lanka, aiming to investigate the current LCC approach and to facilitate LCC practitioners in the country. Therefore, a multiple-case design was chosen. The unit of analysis, number of cases, and case selection were considered following Yin’s (2009) framework. The number of cases selected: the research design determined the required number of cases, ranging from one to eight, depending on the research objectives and data saturation point. Criteria for case selection: cases were selected based on convenience, accessibility, discretion, and relevance to the research objectives. In this study, high-rise buildings utilised for commercial purposes were chosen, with high-rise buildings in Sri Lanka defined as those with 13 floors or more, including the ground floor. Unit of analysis: the “case” directly relates to the research problems, and the scope and limitations of data collection are determined by defining the unit of analysis. This research focused on analysing LCC components, contributions, barriers, and strategies in commercial buildings in Sri Lanka. The unit of analysis was the commercial buildings that practice the LCC concept in a business nature. Sampling technique: the researchers employed judgment sampling, selecting respondents, such as engineers and facility managers, with the necessary knowledge and experience related to the selected cases.

3.2

Data Collection Techniques

Data for the research were collected through document analysis and semi-structured interviews.

3.2.1

Document Analysis

Documents such as annual organisational reports, administrative expenditure budget records, and operating expense reports were reviewed to gather information on the running costs and the contribution of each element. The interview guideline included a detailed framework of operation and maintenance cost elements based on the respondents’ experience, allowing for data triangulation.

Analysis of Life Cycle Cost, Barriers and Strategies Towards Enhancing. . .

3.2.2

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Semi-structured Interviews

Semi-structured interviews were chosen as the most suitable method for data collection, enabling both structured and unstructured questions and providing flexibility (Cachia & Millward, 2011). The interviews aimed to collect cost data for each case study and gather information on achieving sustainable features and the reasons behind their success or failure. An interview guideline with structured and unstructured questions was developed for the eight LCC practitioners.

3.3

Data Analysis Techniques

After data collection, the study proceeded with data analysis to add value to the raw data and convert them into analytical information (Wahyuni, 2012).

3.3.1

Descriptive Analysis of LCC

Descriptive statistics are used to summarise information obtained from a sample, condensing it and presenting it in a tabular format (Nolan & Heinzen, 2008). This includes techniques such as frequency distributions and graphical methods like histograms, pie charts, bar charts, and scatter plots, which help summarise the data visually. Numerical values such as the mean or median can be used to describe the centre of a dataset, while measures like variance, standard deviation, or interquartile range provide information about variability. Each numerical value represents a specific sample characteristic and is computed from the data. Descriptive statistics analysis can be applied to secondary data from selected cases (Nolan & Heinzen, 2008). This research used Microsoft Excel software to conduct the descriptive analysis.

3.3.2

Content Analysis

Content analysis is a methodology used to uncover patterns and themes in qualitative data, which can offer valuable insights into the topic being studied. This process consists of multiple steps, including identifying the main themes, assigning codes to these themes, categorising responses accordingly, and integrating the themes and responses into the report’s text. Content analysis can be conducted manually or with computer software such as QSR NVivo (Kumar, 2011). This study employed content analysis to identify barriers to LCC, an essential aspect for enhancing future improvements in LCC.

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4 Analysis and Findings 4.1

Profile of Selected Cases

Depending on the data saturation point, the multiple-case study selected eight cases for the data analysis. Selected cases belong to different sectors of industries, such as the property development sector, commercial banking, mixed development, financial sector, customs administration, and commercial office. Those cases involve different fields of work and practice the LCC approach in their organisation. It’s important to note that the selection of cases and data analysis is not dependent on whether the organisation is in the private or public sector. The summary profile of the selected cases explaining organisation type, the sector, and the nature of work or field is given in Table 4. Table 4 Summary profile of selected cases Cases C1

Type of the organisation/sector of the industry Property development and management

C2

Banking industry

C3

Banking industry – public sector

C4

Mixed development

C5

An institution in the financial sector – public sector Customs administration – public sector

C6

C7

Commercial office for tourism, cargo logistics, strategic investments and services sectors

C8

Property development, management and sales services

Adapted from Weerasinghe (2016)

Nature of work/field of involvement Provide rentable office and business space with modern infrastructure facilities, office support and retail services, including any use of retail lobbies for product promotions, launches, exhibitions and sales Commercial banking, project lending and development banking, corporate finance, leasing, pawning, international trade finance, offshore banking Merchant banking, property development and management renting of office space, management and support services, hotel services, hydropower generation Hospitality for business travellers, tourists and local customers, provide apartments, provide space for events and functions Provide intermediation to distribute financial resources in a planned way to potential users Controlling and monitoring imports and exports of goods, especially those that pose environmental, health, social, and security risks, including restricted and prohibited items Management and operation of hotels and resorts, courier services, integrated logistics services and maritime transport, power generation, printing and packaging, apparel manufacturing, inward money transfer, real estate management, insurance and the OTIS elevator agency Leasing of office space and real estate, housing and land, shopping complexes and luxury apartments

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Table 5 Profile of participants Cases C1

Interviewee I01

Position Senior manager – facilities management Senior manager – operation and maintenance Maintenance manager

C2

I02

C3

I03

C4

I04

C5

I05

C6

I06

Energy manager and FM executive Maintenance and project engineer Maintenance engineer

C7

I07

Chief engineer

C8

I08

Maintenance manager

Background Electrical engineering Electrical engineering Electrical engineering Facilities management Mechanical engineering Mechanical engineering Electrical engineering Mechanical engineering

Work experience (years) 21–30 11–20 11–20 1–10 11–20 1–10 11–20 21–30

Adapted from Weerasinghe (2016)

4.2

Profile of Semi-structured Interviewees

Table 5 illustrates the profile of participants, which is prepared using the background information and some specific project information of the cases. The interviewees or participants are limited to eight, and only one professional engaged in the operation and maintenance activities of the organisation and who practised the LCC approach within the scope of their work was interviewed from each case. Interviewees represent different professional backgrounds, such as electrical engineering, mechanical engineering, and facilities management. Respectively, 50% of participants are from the professional field of electrical engineering, 37.5% are from mechanical engineering, and only 12.5% or one member is from the field of facilities management. Participants for the semi-structured interviews are held in four different years of experience categories, such as 1–10, 11–20, 21–30 and more than 30; among those categories, 25% of the participants are in the 1–10 category, 50% are in the 11–20 category, and the other 25% are in the 21–30 category.

4.3

Analysis of LCC

The significance of running costs as an integral part of the LCC of commercial buildings or any other building may contain certain cost elements. When reviewing the literature, two classifications of building running costs are identified. For this research, a framework of operation and maintenance cost elements is prepared based on the identified two classifications.

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One classification belongs to BS ISO 15686-5:2017 standard, and BMCIS prepares another. The amended framework using those two classifications was given to the respondents, and cost data were collected through the respondents and refer to the relevant documents in the organisations. Hence, the elements constituting the running costs of commercial buildings in Sri Lanka were identified according to the prepared framework for operation and maintenance cost elements. The following indicate the elements in those two categories.

4.3.1

The Constitution of Operation Costs

The framework includes operation cost elements, such as rent, insurance, utility, administrative costs, and taxes. However, when considering the selected cases, all the selected commercial buildings are owned by the respective organisations that operate and occupy the premises. Most buildings own rentable spaces, making renting an income for the organisation. Hence, rent cost was not identified as an element of running costs in commercial buildings in Sri Lanka. The insurance cost is mainly determined through a specific insurance package, which may depend on the reinstatement value of the property, including plant and machinery. Under utility costs, electricity, water, fuel, and effluent and drainage costs are mainly identified, and only one case (C4) also contains the cost for gas. Those costs are separately maintained in the selected cases. Hence, even in commercial buildings, there are slight differences in the constitution of utility costs, considering the type of the organisation. The administrative costs of commercial buildings mainly consist of staff costs, waste disposal, security costs, sundries, and porterage. However, an additional cost may be incurred for property management if the buildings are managed by outsourced companies such as property management companies or facility management firms. Other than that, taxes and subsidies are associated with any commercial building.

4.3.2

The Constitution of Maintenance Cost

Maintenance cost elements included in the prepared framework are cost for the decoration, fabric maintenance, building service maintenance, external works, cleaning, and the replacement of major systems and components. Decoration includes cyclical and non-programmed internal and external decorations. On the other hand, fabric maintenance and building services are considered a combination of planned maintenance, corrective maintenance, deferred maintenance (emergency) and repairs, and replacement of the minor components of those elements. As identified in the data collection, the decoration and fabric maintenance cost has not been maintained separately in commercial buildings in Sri Lanka but is added as a cost for general building maintenance. Maintenance costs for building services are separately maintained in commercial buildings in Sri Lanka, according to the types of building services the commercial buildings are capable of. In addition,

Analysis of Life Cycle Cost, Barriers and Strategies Towards Enhancing. . . Fig. 1 The elements that constitute the running costs in commercial buildings in Sri Lanka. (Adapted from Weerasinghe, 2016)

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Running Cost Operation Cost Insurance Utilities Administrative Taxes and Subsidies Maintenance Cost General Building Maintenance Building Services Maintenance Cleaning External Works

Table 6 Median running cost to % deviation in the running costs of each case

Element Running Cost

Median Running Cost to % Deviation in Running Costs C1 C2 C3 C4 C5 C6 C7 C8 -120% 7% -7% -15% -91% 69% 75% 68%

Source: Weerasinghe (2016)

external works in commercial buildings encompass expenses related to pest control and landscaping, and these costs are managed separately. The cost for cleaning is mainly considered as the cost for housekeeping. When considering replacing significant systems and components, this comes again under building service maintenance. In summary, the elements that constitute the running cost of commercial buildings in Sri Lanka can be summarised as in Fig. 1.

4.3.3

The Contribution of Running Costs in Commercial Buildings in Sri Lanka

The cost amount in LKR for all the identified running cost elements was collected. There is a significant variation between the maximum and minimum running cost values. Hence, it is not accurate to consider the mean value of each cost element to map the contribution of running costs in commercial buildings in Sri Lanka. Thus, the study first identified the total running cost variation across the cases to select the best sample of cases among the selected cases to compute the contribution. As a result, Table 6 presents the percentage deviation of median running cost for the selected cases, calculated using Eq. (1), with values represented in LKR for the running cost. Median cost to%deviation in running cost =

ðMedian running cost - Running cost of C n Þ % Median running cost

ð1Þ

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Table 7 Scope of data used for the descriptive analysis Categories of commercial buildings Category 1 Category 2

Description Commercial high-rise buildings that have 23 to 34 floors Commercial high-rise buildings that have 13 to 17 floors

Case selected C2, C3, and C4 C6, C7, and C8

Source: Weerasinghe (2016)

Cost in LKR

Millions

Maintenance Cost, 25%

350 300 250 200 150 100 50 Category 1

Operation Cost, 75%

Operation Cost

Category 2

Maintenance Cost

Fig. 2 The contribution of operation cost and maintenance cost to running cost in commercial buildings in Sri Lanka. (Source Weerasinghe, 2016)

The above table indicates two different ranges of cost deviations within the selected eight cases. As highlighted in light blue, cases C2, C3, and C4 indicate one range, which deviates slightly; as highlighted in dark blue, cases C6, C7, and C8 indicate another range, which deviates considerably. However, C1 and C5 do not fall into any of those ranges. Hence, the contribution of each element to the running cost in commercial buildings is separately illustrated considering the mean value for commercial buildings, which have floors 23–34 approximately and floors 13–17 approximately in the Sri Lankan context. However, cases C1 and C5 are eliminated from the descriptive statistics analysis due to the significant deviations from the selected ranges. This section of descriptive statistical analysis may proceed under the following scope of data given in Table 7. Since running cost mainly consists of operation and maintenance costs, Fig. 2 illustrates those two main elements for the running cost and the contribution in categories 1 and 2. The % contribution of operation and maintenance costs to the running cost in both categories may, respectively, be equal to 75% and 25% approximately.

Analysis of Life Cycle Cost, Barriers and Strategies Towards Enhancing. . .

Cleaning, 10.75%

Building Services, 8.19%

External Works, 0.21%

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Insurance, 1.07% Utilities, 28.97%

General Building Maintenance, 6.14% Taxes, 15.80% Administrative Cost, 28.86% Fig. 3 The contribution of identified sub-elements to the running cost in Category 1. (Source: Weerasinghe, 2016)

Building Cleaning, Services, 9.81% 6.10%

External Works, 0.56%

General Building Maintenance, 8.91%

Insurance, 1.50% Utilities, 38.18%

Taxes, 5.60% Administrative Cost, 34.43% Fig. 4 The contribution of identified sub-elements to the running cost in Category 2. (Source: Weerasinghe, 2016)

Even though the contribution of operation cost and maintenance cost elements are proportionately equal in both categories, the mean value indicates a higher deviation – approximately 200 million LKR for the operation cost and 70 million LKR for the maintenance cost. Hence, the operation cost for the running cost in commercial buildings in Sri Lanka is greater than the contribution of maintenance cost. However, the contribution of sub-cost elements within the operation and maintenance costs to the running costs of commercial buildings in Sri Lanka may differ even proportionately within the two categories. Figures 3 and 4 illustrate the contribution to the running cost in categories 1 and 2.

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The highest contribution is from the utility and administrative costs of the commercial buildings, which provide approximately 29% each to the running cost. Within this contribution of utility cost, the electricity cost consumes a higher proportion than the other energy costs, such as fuel, gas, and water. On the other hand, the administrative cost includes a higher proportion of staff costs than the other administrative costs, such as property management, sundries, porterage, and waste disposal costs. The second highest contribution is from taxes and subsidies, which contribute approximately 16% of the running cost of the commercial buildings and include income taxes, national building tax (NBT), value added tax (VAT), etc. Then the costs for cleaning, building service maintenance, and general building maintenance contribute 11%, 8%, and 6% to the running cost of commercial buildings in Sri Lanka. Insurance costs and costs for external works contribute slightly and considerably lower than the other elements. In Category 2, the highest contributor to running costs is the utility cost of the commercial buildings, which contributes approximately 38%, and the second highest contribution is from the administrative cost, which provides approximately 34%. Then the cost for cleaning, general building maintenance, building service maintenance, and taxes and subsidies contribute, respectively, 10%, 9%, 6%, and 6% for the running cost. Similarly, insurance costs and external costs contribute slightly to the running cost. Both cases highlight the significance of utility and administrative costs for the running cost of commercial buildings in Sri Lanka as the highest contributors and the other contributors have slight deviations in the ranking when the building characteristics are increasing or decreasing. Moreover, based on the cost data on building utilities, one can introduce the contribution of the energy cost and different energy costs towards the building’s total energy cost. Table 8 indicates the cost data under the utility cost. The table indicates that in both categories, the higher percentage of contribution is from the electricity cost of commercial buildings, and the least cost contribution comes from fuel consumption.

Table 8 The contribution of each element to the utility cost

Element Utility cost Electricity Fuel Water

Category 1 Mean value LKR 107,218,073.33 93,751,154.00 1,231,920.67 12,234,998.67

Source: Weerasinghe (2016)

Contribution to utility cost %

Category 2 Mean value LKR 41,853,008

Contribution to utility cost %

87.44% 1.15% 11.41%

39,279,812 1,203,258 1,369,938

93.85% 2.87% 3.27%

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Table 9 LCC usage at the building use phase LCC usage at building use phase Never use Almost never Sometimes Almost every time Frequently use

Selected cases C2 C3 C1

C4

✓

✓

✓

C5

C6

C7

✓

C8

✓

✓ ✓

✓

Source: Weerasinghe (2016)

4.4

Application of LCC

The interviews were conducted using closed-ended questions and a Likert scale to rate the frequency of LCC usage in the organisation in the building use stage. The interviews focus on the significant building stages, specifically the building design and use stages. Overall, the interviews appear to be geared towards a better understanding of how LCC is being used in commercial buildings in Sri Lanka. The findings are summarised in Table 9, indicating the LCC usage at the building use stage. Based on the interviewees’ responses, it appears that the usage of LCC in commercial buildings in Sri Lanka is not very frequent, particularly at the building design stage. Most interviewees (I01, I03, I04, I05, I06, I07, and I08) suggested that LCC is not conducted at the design stage and is not a familiar concept or practice for organisations. Additionally, the interviewees (I01, I02, I04, I06, and I08) indicated that the payback period is often considered instead of the LCC approach and that LCC may not be selected if the payback period is very short. However, some interviewees (I03 and I07) noted that LCC is frequently used by organisations that operate in complex business environments or involve large capital expenditures, such as those in hydropower generation projects (C3 and C7). Despite the lack of usage at the design stage, the interviewees suggested that LCC is satisfactorily conducted by practitioners in the building use stage, although it may not be used frequently. Overall, the responses suggest that there is room for the greater adoption of LCC in commercial building projects in Sri Lanka, particularly at the design stage, which could lead to better decision-making and more efficient use of resources over the life cycle of the building.

4.4.1

Purposes of LCC Use

Overall, it can be observed that the usage of LCC in selecting the best alternative among competing systems or suppliers varies among organisations, depending on their scope and limitations. Some organisations mainly use LCC for energy conservation projects (I01, I02, and I04) or procurement related to building services and

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Table 10 Summary of the satisfactory level of organisations towards the decisions informed by LCC Satisfactory level of the organisation Very dissatisfied Dissatisfied Unsure Satisfied Very satisfied

Selected cases C1 C2 C3

C4

✓ ✓

✓

C5

C6

✓ ✓

C7

C8

✓ ✓

✓

Source: Weerasinghe (2016)

Fig. 5 Coding structure of the purposes and benefits of LCC. (Source: Weerasinghe, 2016)

electro-mechanical equipment (I03 and I05). Others use LCC for building revaluation (I08) or to evaluate the impact of a system or project’s LCC on long-term budgets (I06 and I07). However, LCC is essential in the decision-making process for selecting the best alternative among competing systems or suppliers for most of the organisations interviewed. Table 10 presents the satisfaction levels of organisations with decisions informed by LCC, as determined from the responses of interviewees through closed-ended questions. Even though all the interviewees gave their opinion on the benefits of LCC, which determine the satisfaction level, the perception of the interviewees on the LCC results may determine the difference between “satisfied” and “very satisfied” levels. However, as previously mentioned, since the organisations (C1, C2, C4, C6, and C8) use a payback period to analyse the potential cost benefits and then go for the LCC to select the best alternative among the selected competing alternatives, this may direct an attitude towards a “very satisfied” level. The outcomes from the interviews imply the following purposes of LCC use and the benefits the organisations gain through the practice of LCC for the underlined purposes. Figure 5 indicates the coding structure of the purposes and benefits of LCC. Considering the above views of the interviewees, the purposes of LCC in commercial buildings in Sri Lanka are often laid on source selection studies that choose the most suitable alternative as well as affordability studies that measure the

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Table 11 Types of information maintained by the cases Types of information Construction Operation Maintenance End of life

Maintain in C1 C2 ✓ ✓ ✓ ✓ ✓

C3 ✓ ✓ ✓ ✓

C4 ✓ ✓ ✓ ✓

C5 ✓ ✓ ✓

C6 ✓ ✓ ✓ ✓

C7 ✓ ✓

C8 ✓ ✓ ✓ ✓

Source: Weerasinghe (2016)

impact on anticipated budgets as well as building valuation and revaluation. Moreover, it indicates that the practitioners are satisfied with the decisions informed by the LCC, and the benefits can be gained through the practice of LCC in commercial buildings in Sri Lanka.

4.4.2

Data for LCC

LCC requires accurate data to be available to users and make use of these data where necessary. The interviewees were asked to comment on the organisations’ information related to the LCC elements. Table 11 presents the interviewees’ responses on the types of LCC information. All interviewees stated that the organisations maintain data on their buildings’ operation and maintenance costs. However, two interviewees (I01 and I07) indicated that the organisations also maintain information related to construction stage activities, while the other interviewees (I03, I04, I06, and I08) stated that their organisations also maintain data related to end life. The existence of these data sets highlights the potential for LCC databases within organisations and the contribution that commercial buildings can make to accessing data at the local level. The literature indicates that operation and maintenance costs are significant and integral components of any LCC exercise, and the interviewees’ responses suggest that the organisations understand the importance of LCC in building construction and use stages. They are, therefore, adequately managing building cost data for future use. However, acquiring accurate and reliable data remains a challenge for practitioners, which is further affirmed by the interviewees’ views on the barriers they face when undertaking LCC. It is worth noting that the extent of maintenance of operational data may vary depending on how the data are used. Table 12 indicates the extent of maintaining operation and maintenance cost information. In summary, it appears that the organisations in question maintain data related to building costs, but there is still room for improvement in data accuracy and reliability. Based on the responses of the interviewees, it appears that there is variability in the extent to which commercial building organisations in Sri Lanka maintain databases related to LCC. Some organisations solely identify and collect operation and maintenance cost data, primarily promoting the use of LCCA and LCCP (I02,

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Table 12 Extent of maintaining operation and maintenance cost information The extent of maintaining operation and maintenance cost information Only identifying Identifying and collecting Identifying, collecting, and analysing Identifying, collecting, analysing, and reviewing

Extent involved in C1 C2 C3 C4 ✓ ✓

C5

C6

✓

C7

C8

✓ ✓

✓

✓

✓

Source: Weerasinghe (2016)

Fig. 6 Coding structure for data and databases of LCC. (Source: Weerasinghe, 2016)

I03, and I07). However, other organisations practice identifying, collecting, analysing, and reviewing operation and maintenance data, which promote all three elements of LCC, including LCCM (I01, I04, I05, I06, and I08). Regarding databases, some interviewees (I01 and I06) reported that their respective organisations maintain integrated LCC databases that include information on asset registry, costs, physical attributes, qualitative information, and performance data. One of the interviewees (I04) opined that the organisation maintains a capital asset management system consisting of all information from the procurement of capital assets to the disposal of those assets, including the running costs of the assets. In contrast, other interviewees (I02, I03, I05, and I07) indicated that their organisations only maintain operation and maintenance cost databases. Based on these responses, it can be concluded that the extent to which commercial building organisations in Sri Lanka maintain integrated LCC databases varies. Some organisations seem more proactive in maintaining these databases, which can provide more comprehensive information to support LCC decision-making. A summary of LCC data and databases used in the selected cases is given in Fig. 6.

4.4.3

Barriers to LCC

Interviewees were asked to comment on the barriers to calculating LCC for a particular purpose and the strategies to overcome those barriers. Table 13 summarises the respondents’ barriers when using the LCC approach with the most prominent reasons.

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Table 13 Summary of barriers to LCC Barriers Establish the operating profile of the asset Establish the utilisation factors Identify the total costs associated with a particular asset Escalate current costs at assumed inflation rates

Reasons Not accurate data for estimation

Interviewee I03

Not accurate data for estimation Estimating maintenance costs is a challenge It may not be very accurate for long-term projects due to economic changes

I03 I01, I02, I03, I04, I05, I06, I07, I08 I01, I03, I07

Source: Weerasinghe (2016)

Most interviewees responded that identifying the total costs associated with a particular asset or project is difficult because estimating operation and maintenance costs is challenging when calculating LCC. Furthermore, the interviewee (I05) clarified that there is no comprehensive maintenance for most of the assets. Only the labour costs will be added as the maintenance cost. On a similar view, the interviewees (I03 and I04) opined that there is no accurate data to determine the total costs, paving the way for unreliable costing. Similarly, interviewee I07 further explained: “Estimating becomes critical because those are subject to change according to the market conditions.” Only interviewee I03 opined that there is no accurate data to estimate the assets’ operating profile and utilisation factor. The interviewees (I01, I03, and I07) opined that escalating costs at an assumed inflation rate is also tricky for longer-term projects due to economic changes. These views indicate the barriers faced by the LCC practitioners in Sri Lanka due to the lack of accurate data and the unpredictability of the cost variables, like the inflation rate.

Critical Cost Variables in LCC Identifying the critical cost variables for predicting life cycle costs is essential. The accuracy of these cost variables depends on several factors, including the availability of robust benchmark data sets and the complexity of the information needed for conducting LCC evaluations. According to the interviewees’ opinions, the critical cost variables are summarised in Table 14. It is encouraging to hear that the interviewees had a positive opinion on handling the uncertainty associated with the critical cost variables to make better-informed decisions. These different ways of handling uncertainty in cost variables are given in Fig. 7. Based on the statements from the interviewees (I01, I02, I04, and I05), the acquisition cost, as a critical cost variable, form a considerable part of the LCC for new construction. All the interviewees expressed their opinion that operation and maintenance costs are considered critical cost variables. This is due to the fact that various types of operation and maintenance activities with varying frequencies are possible, and these activities can have variable impacts on the life cycle costs of a

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Table 14 Critical cost variables as identified by the interviewees Generic cost variables Acquisition costs Operation and maintenance costs End-of-life costs and residual value Service life, life cycle, and design life Period of analysis Discount rate Inflation Taxes and subsidies

Critical cost variables according to C1 C2 C3 C4 C5 C6 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

✓ ✓

✓ ✓

C7

C8

✓

✓

✓

✓

✓ ✓

✓

✓

Source: Weerasinghe (2016)

Fig. 7 Coding structure for handling uncertainty in cost variables. (Source: Weerasinghe, 2016)

constructed asset. Only two interviewees (I02 and I04) opined that it is necessary to consider the demolition of an existing constructed asset and to consider a specific residual value. In the respective organisations, the practitioners consider depreciation based on the capital value and depreciation over the asset’s service life as an appropriate value for the residual value. Furthermore, for the interviewees (I01, I03, I04, I07, and I08), the estimated service life of an asset should be, at least, as long as the design life of the asset. Since maintenance, repair, and replacement should be required to achieve the estimated life cycle, service life is a critical variable in LCC. The interviewees (I01, I03, I04, and I07) opined that the discount rate is also a critical cost variable because it is critical to distinguish whether the real market rate or the discount rate with inflation factor is going to be used for the LCC. Moreover, the interviewees (I01, I03, I06, I07, and I08) opined that future inflation is highly uncertain. Thus, it is a critical cost variable. Always, consideration should be given to inflation rate changes. Only one interviewee (I04) indicated that taxes and subsidies are a critical cost variable and stated that “Taxes and subsidies can affect the relative price and the decision-making process. It is important to adjust for any incidence of tax arising from different options being considered. The existence of tax subsidies associated with the investment should be included when determining all the costs associated with the life cycle of an asset.”

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Considering the analysis period as a critical cost variable in LCC prediction is essential, even if the interviewees did not identify it. This is because costs occurring outside the period of analysis can significantly impact a client’s costs, particularly in cases where heavy maintenance costs or residual values need to be accounted for. The analysis period needs to be sufficient to capture all relevant costs and benefits associated with the asset, and the results are reviewed over several periods if a shorter period than the life cycle is selected. Based on the statements from the interviewees (I01, I03, I04, I05, and I08), it appears that relying on vendors’, manufacturers’, and suppliers’ recommendations is the most prominent way of handling uncertainty in cost variables in the context of commercial buildings in Sri Lanka. This is particularly true for operation and maintenance costs, service life, life cycle, and design life. Historical data and in-house experience are also commonly used to handle uncertainty, particularly for operation, maintenance, and end-of-life costs. However, it is essential to note that not all interviewees agree with this approach. One interviewee (I06) stated that their organisation uses a discounted payback method with LCC to handle uncertainty. In contrast, another interviewee (I07) mentioned using sensitivity analysis to examine how uncertainty variations could affect the relative merits of the different options being considered. Although sensitivity analysis may not be a widely adopted practice in the context of commercial buildings in Sri Lanka, practitioners are effectively utilising other readily accessible methods for managing uncertainty based on available knowledge resources. It is important to continue exploring and evaluating different methods of handling uncertainty to ensure accurate and effective LCC analysis.

4.4.4

Strategies of LCC

Moreover, the interviewees have proposed some valuable strategies to overcome those difficulties. Figure 8 indicates the strategies proposed by the respondents. When introducing those strategies, most interviewees (I02, I05, I06, I07, and I08) highlighted the lack of accurate and reliable data as the main barrier to conducting LCC and gaining reliable outcomes. Thus, the interviewees (I02, I07, and I08) proposed introducing databases in the local context. Again, the interviewees (I07

Fig. 8 Coding structure for strategies to overcome the barriers. (Source: Weerasinghe, 2016)

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and I08) suggested to define reference projects by responsible parties. As interviewee I08 stated, “The organisations which are responsible for the analysing of operation and maintenance costs of Sri Lankan buildings, should be formulated a method to collect the relevant costs from those buildings frequently and publish the analysis of the life cycle costs of major assets and buildings”. Moreover, two interviewees (I01 and I03) expressed the opinion that acquiring information from relevant vendors, manufacturers, and service providers could address the same barrier. Further, the interviewees (I01 and I06) clarified to maintain and use in-house operation and maintenance data and acquire data from similar industries. In a different view, the interviewee (I05) proposed to “go for Computerized Maintenance Management Systems (CMMS) which keep every data related to the operation and maintenance of assets and which can be used to estimate operation and maintenance costs for future acquisitions”. Only interviewee I04 proposed acquiring and comparing quotations from different LCC suppliers.

Life Cycle Cost Management (LCCM) LCCM is intended to establish where performance differs from the life cycle cost projection and to make recommendations on the more efficient utilisation of the building and its assets. The interviewees were asked to comment on the LCCM practices of the respective organisation. The interviewees (I02, I03, I04, and I08) opined that the respective organisations are not conducting any procedure for the LCCM. This indicates that some commercial buildings in Sri Lanka use LCC only at the inception of the investment, and there is no continuous procedure to assess the accuracy of LCC and change accordingly at the use stage of a particular asset. In contrast to the above view, two interviewees (I06 and I08) opined that the organisations practice the review of LCC assumptions made early with running costs. LCCM needs more experience gained through the profession to determine the service life of an asset, giving the client the optimum payoff. After identifying the performance differences from the LCCP projections, the commercial buildings can take minor or major replacements to reduce the costs and utilise the building more efficiently. Figure 9 indicates the coding structure for the different LCCM procedures of commercial buildings in Sri Lanka.

Fig. 9 Coding structure for LCCM procedures. (Source: Weerasinghe, 2016)

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Fig. 10 Coding structure for the limitations of LCC as well as exposure, knowledge, and awareness programmes on LCC. (Source: Weerasinghe, 2016)

Moreover, the interviewees were asked to comment on the kinds of exposure, knowledge, and awareness programmes needed to encourage the practitioners to adopt LCC in commercial buildings according to the limitations of the LCC applications’ identified by them. In addition to the challenge of obtaining reliable and consistent data, the majority of interviewees (I01, I02, I03, I04, and I05) pointed out that limited knowledge and experience in using LCC calculations are among the main limitations of LCC practices in commercial buildings in Sri Lanka. One interviewee (I01) opined that the lack of understanding of practitioners about the LCC’s advantages is a limitation on LCC practice. Following the above views, the proposed LCC exposure, knowledge, and awareness programmes are indicated in Fig. 10. Among the aforementioned strategies, knowledge and awareness programs proposed by practitioners, aimed at educating individuals about the significance of LCC, can be considered the most prominent.

5 Discussion and Conclusion The developed framework for collecting running cost data from commercial buildings identifies the main cost elements: insurance, utilities, administrative expenses, taxes and subsidies, general building maintenance, building services maintenance, cleaning, and external works. A descriptive analysis reveals that utility costs contribute between 29% and 38%, administrative costs range from 29% to 34%, taxes and subsidies vary from 6% to 16%, cleaning costs range from 10% to 11%, building service maintenance costs range from 6% to 8%, and general building maintenance costs vary from 6% to 9%. Insurance and external works account for less than 1%. Utility costs primarily consist of 87.44% to 93.85% electricity, 3.27% to 11.41% water, and 1.15% to 2.87% fuel costs in commercial buildings.

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The analysis highlights the main barriers practitioners face when using the LCC approach, including the lack of accurate and consistent data and the unpredictability of cost variables, such as inflation rates. Similar challenges were found in the literature review conducted by Al-Hajj and Horner (1998). Estimating operation and maintenance costs proves difficult, making LCC results unreliable. Sterner (2000) also identifies difficulties in collecting historical data and the absence of universal methods and standard formats, which are addressed by suggested strategies to overcome these barriers. The study proposes valuable strategies to overcome these difficulties, such as establishing local databases, gathering information from reputable vendors, manufacturers, and service providers; utilising in-house operation and maintenance data; acquiring data from similar industries; defining reference projects; obtaining and comparing quotations from multiple suppliers; and implementing CMMS. However, the literature does not explicitly mention CMMS as a strategy to overcome LCC difficulties, even though it provides an integrated database for building operation and maintenance, recording, and analysing costs related to activities. Obtaining accurate and consistent cost data is crucial for conducting LCC. The study suggests various methods for obtaining data effectively, including establishing country-level databases; gathering information from vendors, manufacturers, and service providers; utilising in-house operation and maintenance data; acquiring data from similar industries; defining reference projects; obtaining and comparing quotations; and implementing CMMS. Additionally, LCC practitioners can prioritise high-cost areas to focus their data collection efforts. Life cycle cost modelling is a technique to identify these areas, even with preliminary and marginally accurate data during the design development stages. Adequate documentation should be maintained to communicate LCC practices with interested parties, allowing for reviewing technical and cost data at all management levels and applying valuable information to future projects. Comparing estimated cost data with actual budget expenditures helps identify deviations and maintain relevant data in an integrated software database for life cycle costs. Furthermore, integrating LCC analysis into procurement procedures is recommended. The study suggests that LCC practitioners should acquire exposure, knowledge, and awareness of LCC-related matters, such as depreciation, methods to identify cost variables, parameters affecting calculations, and case studies and success stories. Awareness programmes on the significance of LCC should be implemented. In Sri Lanka, LCC is satisfactorily used at the running stage of commercial buildings for various purposes. However, its use at the design stage is not historically evident, but it is becoming a trend due to cost conservation attitudes. LCC is still used for a limited number of purposes. The limited availability of accurate and consistent data, the unpredictability of cost variables, practitioners’ imperfect understanding of LCC, and limited knowledge and experience in using LCC calculations contribute to the limited practice of the LCC approach in commercial buildings in Sri Lanka. The study proposes techniques such as relying on vendor recommendations to address the uncertainty of critical cost variables in LCC.

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References Al-Hajj, A., & Horner, M. W. (1998). Modelling the running costs of buildings. Construction Management and Economics, 16(4), 459–470. https://doi.org/10.1080/014461998372231 Ashworth, A. (1993). In J. W. Bull (Ed.), How life-cycle costing could have improved existing costing. Blackie Academic & Professional. British Standard Institution. (2017). Buildings and constructed assets-Service life planning-Part 5: Life cycle costing, BS ISO 15686-5:2017. London. Building Maintenance Cost Information Service. (1984). Standard form of property occupancy cost analysis: Principles, instructions, definitions and elements. Connelly-Manton (Printing). Cachia, M., & Millward, L. (2011). The telephone medium and semi-structured interviews: A complementary fit. Qualitative Research in Organizations and Management: An International Journal, 6(3), 265–277. https://doi.org/10.1108/17465641111188420 Cole, R. J., & Sterner, E. (2000). Reconciling theory and practice of life-cycle costing. Building Research & Information, 28(5/6), 368–375. https://doi.org/10.1080/096132100418519 Dell’Isola, A. J., & Kirk, S. J. (2003). Life cycle costing for facilities. Construction Publishers & Consultants. Flanagan, R., Kendell, A., Norman, G., & Robinson, G. D. (1987). Life cycle costing and risk management. Construction Management and Economics, 5, 53–71. https://doi.org/10.1080/ 01446193.1987.10462093 Gluch, P., & Baumann, H. (2004). The life cycle costing (LCC) approach: A conceptual discussion of its usefulness for environmental decision-making. Building and Environment, 39, 571–580. Goh, B. H., & Sun, Y. (2015). The development of life-cycle costing for buildings. Building Research & Information. https://doi.org/10.1080/09613218.2014.993566 Griffin, J. J. (1993). Life cycle cost analysis: A decision aid. Blackie Academic & Professional. Joseph, H. L. (2010). Building operation and maintenance: education needs in Hong Kong. Facilities, 28(9/10), 475–493. https://doi.org/10.1108/02632771011057206 Kehily, D. (2010). Guide to life cycle costing. Society of chartered surveyors Ireland. Retrieved from http://www.scsi.ie Kishk, M., & Al-Hajj, A. (1999). An integrated framework for life cycle costings in buildings. The Robert Gordon University. Korpi, E., & Ala-Risku, T. (2008). Life cycle costing: A review of published case studies. Managerial Auditing Journal, 23(3), 240–261. https://doi.org/10.1108/02686900810857703 Kumar, R. (2011). Research methodology (3rd ed.). Sage. Langston, C. A. (2011). Life-cost approach to building evaluation. Routledge. Law, J., & Smullen, J. (2008). A dictionary of finance and banking (4th ed.). https://doi.org/10. 1093/acref/9780199229741.001.0001 Lockie, S. (2008). Making sense of whole life costing. In ACES spring conference Birmingham May 2008 (pp. 63–70). Nolan, S. A., & Heinzen, T. E. (2008). Statistics for the behavioral sciences. Worth Publishers. Norris, G. A. (2001). Integrating life cycle cost analysis and LCA. https://doi.org/10.1007/ BF02977849 Royal Institution of Chartered Surveyors. (1986). A guide to life cycle costing for construction. Surveyors Publications. Saltelli, A. (2002). Sensitivity analysis for importance assessment. Risk Analysis, 22(3), 579–590. https://doi.org/10.1111/0272-4332.00040 Schade, J. (2014). Life cycle cost calculation models for buildings (Doctoral dissertation, Lulea University of Technology). Retrieved from http://www.researchgate.net/publication/242404432 Sterner, E. (2000). Life-cycle costing and its use in the Swedish building sector. Building Research & Information, 28(5–6), 387–396. https://doi.org/10.1080/096132100418537

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Applications of Life Cycle Costing in Waste-to-Energy Projects Abdulrahman Abdeljaber, Omar Mostafa, and Mohamed Abdallah

1 Introduction The conversion of waste, including waste-activated sludge, livestock manure, and municipal solid waste (MSW), into valuable fuels offers a sustainable pathway toward maximized waste valorization and reduced fossil fuel depletion. The global annual MSW generation level stands at around 2 billion tons and is expected to increase by 70% in 2050 (World Bank, 2018). As a result of its comparative economic advantages, landfilling is considered the prevalent management method of handling MSW (World Bank, 2018). However, such a practice is becoming impractical and infeasible due to its high environmental footprint and the limited land resources available (Abdeljaber et al., 2022). To mitigate adverse effects and conserve natural resources, the US Environmental Protection Agency, along with multiple global interventions, established a sustainable waste management framework that emphasizes waste reduction and reuse at source, followed by material and energy recovery (Abdallah et al., 2021). Moreover, due to the escalating population and industrialization worldwide, global energy consumption is projected to double in the next two decades (Moodley & Trois, 2021). This has induced international initiatives toward relying on renewable energy. Recently, waste-to-energy (WtE) systems have gained significant attention due to their immense capacity to tackle waste management challenges while contributing to the transition toward more sustainable energy systems (Abdeljaber et al., 2022). WtE is a process that entails waste transformation into usable energy forms, e.g., electricity, heat, and biofuel. It offers a promising sustainable management approach that

A. Abdeljaber · O. Mostafa · M. Abdallah (✉) Department of Civil and Environmental Engineering, University of Sharjah, Sharjah, United Arab Emirates e-mail: [email protected]; [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. S. Muthu (ed.), Life Cycle Costing, Environmental Footprints and Eco-design of Products and Processes, https://doi.org/10.1007/978-3-031-40993-6_4

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helps in producing energy and in diverting waste from landfills. Not only does this strategy harness renewable energy sources, but it also mitigates several environmental implications arising from inadequate conventional waste management methods. The viability of WtE systems has been widely assessed from diverse sustainability perceptions (Abdallah & Elfeky, 2021; Barrena et al., 2009; Di Lonardo et al., 2012; Dong et al., 2018, 2019). Nevertheless, WtE projects entail complex processes and multiple stakeholders, and their implementation involves substantial investments and long-term operational costs. Decision-makers frequently encounter challenges to determine the optimal WtE system that aligns with their specific needs. Therefore, assessing economic feasibility is as critical as evaluating the environmental performance of WtE projects to ensure their sustainability and optimize their benefits. The financial aspects of WtE systems have been tackled in the literature either through a comparative analysis with other technologies (Kim et al., 2011; Lee et al., 2020; Lu & El Hanandeh, 2017; Sharma & Chandel, 2021; Zhao et al., 2021a, b) or to assess their economic viability as a stand-alone implementation (Homagain et al., 2016; Hong et al., 2018; Jeswani et al., 2019; Liu et al., 2023; Rostami et al., 2020). One method for examining the overall economics of WtE projects is life cycle costing (LCC), a methodology that assesses the total expenditures of a service or product during its life cycle, from raw material extraction to disposal. LCC provides a systematic approach that can accommodate the numerous components involved in WtE projects and the high variability of revenue streams. It enables the identification of the cost drivers and environmental effects concerned with the different phases of a project’s life cycle, allowing decision-makers to make informed decisions that balance economic and environmental considerations. To this end, this chapter aims to present an overview of LCC applications within WtE projects. The chapter begins with a description of WtE systems and the different thermochemical and biochemical technologies, particularly incineration, gasification, pyrolysis, mechanical biological treatment (MBT), fermentation, and anaerobic digestion (AD). The next section introduces the concept of LCC and its relevance in assessing the economic sustainability of WtE processes. It then presents a review of the literature on LCC in WtE systems, highlighting the main findings and limitations. The chapter also discusses the challenges and opportunities of applying LCC to WtE projects, including the availability and reliability of data, the choice of system boundaries and functional units, and the integration of environmental and social costs. In the following sections, the chapter further illustrates the applications of LCC in WtE projects through conducting a case study from different contexts, such as developed and developing countries, and different types of WtE technologies. This case study covers different aspects of LCC, such as capital and operational costs, revenues from energy and material recovery, and local economic and marketing conditions. Moreover, it offers practical insights into LCC applications in WtE projects to further demonstrate the potential benefits of integrating financial and environmental considerations in planning and decision-making. The chapter seeks to

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add to the ongoing debate on the role of WtE in the circular economy and the achievement of the Sustainable Development Goals (SDGs) and to promote the use of LCC as a valuable tool for evaluating and promoting sustainability in WtE projects.

2 Waste-to-Energy Systems WtE systems are considered valuable waste valorization technique that involves converting waste materials into usable energy forms, such as electricity, heat, or fuel. They demonstrate a macro-approach that can contribute to sustainable and effective waste management. WtE facilities can be classified into (1) thermochemical conversion processes, such as incineration, gasification, and pyrolysis, and (2) biochemical conversion processes, such as MBT and fermentation. Figure 1 depicts the different types of WtE technologies. Table 1 summarizes waste types, by-products, and the fate of the by-products of different WtE systems. Each technology generates a valuable asset that can either be used to produce energy or marketed or be used as a feedstock for other plants.

Fig. 1 WtE system classifications based on their conversion process

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Table 1 Flow streams, by-products, and mass balances of the different WtE systems WtE system Incineration

a

Type of waste Mixed waste

By-products Heat Ash Syngas Ash and slag

Gasification

Organic matter

Pyrolysis

Organic matter

Syngas Bio-oil Biochar

Anaerobic digestion

Organic matter

Biogas Digestate

Fermentation

Organic matter

Mechanical sortinga

Mixed waste

Hydrogen Volatile fatty acids Bioethanol Recyclables Biodegradables RDF Rejects

Fate of by-product Energy production Market/landfill Energy production Market/incineration/ landfill Energy production Market Market/incineration/ landfill Energy production Market/incineration/ landfill Energy production Market

Fraction (%) 90 10 80 20

Market Market Bioconversion facility Market Landfill

30–50 30 30 18 22

10–50 10–30 20–50 60 40 40–70 5–15

Mechanical sorting is part of mechanical biological treatment

2.1

Thermochemical Technologies

Thermochemical WtE systems entail thermal processing of the organic fraction in the feedstock to generate heat energy, syngas, and/or biofuel. Approximately 65–80% of the stored energy in organic biomass can be reclaimed as heat energy, which can be harnessed to produce electricity (Chakraborty et al., 2013). Thermal conversion is often applied for dry feedstocks that contain a high fraction of nonbiodegradable organic matter (Kumar & Samadder, 2017). Thermochemical WtE systems can be classified, based on the atmospheric conditions and operating temperature, into incineration, gasification, and pyrolysis. Incineration, defined as controlled combustion at high temperatures, is the most commonly implemented thermochemical WtE, whereas pyrolysis and gasification are still considered in the research phase (Shi et al., 2016). Nevertheless, there are a few commercially operating pyrolysis and gasification plants around the world that process various feedstocks. Table 2 provides a detailed description of these systems, including the reactions, and the resulting products.

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Table 2 Description, reaction conditions, and products from thermochemical waste-toenergyWaste-to-energy (WtE) systems Parameter Principle Temperature (°C) Atmosphere Products Pretreatment Advantages

Disadvantages

2.1.1

Incineration Full oxidative combustion 850–1200

Gasification Partial oxidation combustion 800–1600

Pyrolysis Anaerobic combustion 400–800

Sufficient oxygen Heat energy and ash

Controlled oxygen Syngas

Not required Relatively simple Up to 90% volume reduction GHG emissions Low energy efficiency Flue gas cleaning

Required High efficiency of energy recovery Reduced emissions Complex technology Extensive syngas cleaning

Absence of oxygen Syngas, bio-oil, and biochar Required Zero-waste process

Extensive syngas cleaning Feedstock sensitivity

Incineration

Incineration is considered one of the most widespread waste management methods in developed countries as a result of the stringent policies on waste disposal (Scarlat et al., 2015). It is a thermochemical WtE technology that utilizes surplus oxygen to perform complete combustion at temperatures of up to 1200 °C. Incineration mainly yields hot combusted gases, along with other value-added by-products, including recycled metals and ash. The major advantages of incineration include generated heat energy, short residence time, and instantaneous waste volume and mass reduction by 90% and 70%, respectively. Nevertheless, incinerations face serious environmental concerns due to high greenhouse gas releases. This has led most contemporary incinerators to adopt an emissions control system. Incinerators can be classified into moving grates, rotary kilns, and fluidized beds. The conventional and most implemented technology is combustion with a moving grate. It can accommodate several feedstocks with varying waste composition and calorific values. The substrate is typically placed on the grate via a crane and moved to the ignition chamber. Rotary kilns are often used for industrial and hazardous waste. They consist of two primary compartments: (1) a main inclined compartment where the waste is introduced and thermally disintegrated and (2) a recombustion chamber at the rear end where the remaining wastes are combusted with the supply of secondary air. On the other hand, the mechanism of fluidized bed incinerators is based on injecting an upward flow of combustion air from below. Such a flow would expel substrate particles and assume a fluid-like behavior, where the generated turbulence enhances heat transfer, thus improving combustion efficiency.

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Gasification

Gasification is a thermochemical conversion process that takes place at higher temperatures (800–1600 °C) with an oxidizing agent, e.g., nitrogen, oxygen, and carbon dioxide. It converts organic biomass into hydrocarbon syngas, comprised of hydrogen, methane, carbon monoxide, carbon dioxide, and methane, in addition to bio-oil, char, and slag (Beyene et al., 2018). The composition of syngas is based on the gasifier’s temperature and configuration, the type and flow rates of processed biomass, and the type and quantity of catalysts. Recently, gasification has gained significant attention as a sustainable alternative to well-established incineration as it has more energy potential and emits fewer greenhouse gas (GHG) emissions. It was reported by DEFRA (2013) that advanced gasifiers are equipped with enclosures that heavily minimize soil and water contamination. However, the majority of gasification studies were limited by treating the homogeneous flow of solid fuels, such as wood and coal, along with specific types of MSW. Gasification processes vary from other methods in terms of reactor atmosphere, design, heating, and operating temperature. Gasifiers come in various configurations, including updraft and downdraft fixed beds, fluidized beds, and plasma. In general, feedstock is provided at the top, while oxidant intake occurs at the bottom (updraft) or at the top or sides of the gasifier (downdraft). In fluidized bed systems, a distributor plate is utilized to blow the gaseous oxidant in an upward direction, which then permeates through a bed of inert materials positioned at the bottom. On the other hand, the plasma gasifier functions at the maximum temperature, utilizing an electric arc to power the plasma torch.

2.1.3

Pyrolysis

Pyrolysis is considered a destructive distillation method involving the thermal decomposition of organic feedstock into heterogeneous solid, liquid, and gaseous intermediates in an oxygen-free environment at 400–800 °C (Chhabra et al., 2016). The pyrolysis process yields the following: (1) syngas at high temperature, low heating time, and long residence time; (2) bio-oil at moderate temperature, high heating rate, and short residence time; and (3) biochar at low temperature, low heating rate, and long residence time. The merits of the pyrolysis process lie in its capability to recover around 80% of the stored energy in the organic biomass to char and bio-oil with a calorific value of 38 MJ/kg (Nizami et al., 2017). A typical pyrolysis configuration consists of a two-stage chamber, where the first compartment runs at low temperatures, whereas the subsequent compartment is designed for high-temperature conditions to allow for the complete combustion of feedstock. Its efficiency depends on the type of substrate, heating rate, and operating temperatures (Beyene et al., 2018). For instance, higher and lower pyrolysis temperatures tend to generate more gaseous yields and liquid products, respectively.

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There are two common pyrolysis types, depending on the operating temperature: slow and fast pyrolysis. The former is characterized by the lowest heating rates, i.e., below 10 °C/s, and longer residence time, i.e., 5–30 min at 400–500 °C. The main yield is biochar, along with a few quantities of syngas and bio-oil. Typical configurations of slow pyrolysis include fixed beds and rotary kilns. On the other hand, fast pyrolysis has emerged as the preferred approach in both research and practical applications. It involves an expedited decomposition of organic feedstock at 100 °C/s average-to-high heating rates in the absence of oxygen at 400–650 °C. The primary outcome is 30–60% bio-oil, in addition to 15–35% syngas, and 10–15% biochar. Typical configurations of fast pyrolysis include fluidized beds and rotating cones.

2.2

Biochemical Technologies

Biochemical conversion is a process that uses enzymes and microorganisms to break down various types of biomasses (Pandey et al., 2021). However, this process typically has low productivity and requires a high capital investment, such as larger-sized reactors, to achieve the desired outputs. Microbial augmentation is often employed to promote the process yield (Lee et al., 2019). Since biochemical conversion is a bioprocess, factors such as temperature, pH, and solar exposure may influence its outputs. To ensure the success of the process, strict control of enzymatic conditions is often necessary to maintain enzyme functionality. Generally, biochemical WtE has several advantages over traditional waste management methods, such as landfilling and incineration (Mata-Alvarez et al., 2000). It reduces the amount of waste disposal, which helps minimize GHG emissions and environmental pollution. It also produces renewable energy that can fulfill the energy requirements of communities and industries, thereby reducing reliance on nonrenewable sources of energy.

2.2.1

Anaerobic Digestion

AD is a waste management method that relies on the activity of microbes for the degradation of MSW under anaerobic conditions, generating biogas and digestate (Abraham et al., 2021). The feedstock of the AD process first goes through the hydrolysis phase, where nonsoluble organics are converted to soluble compounds; acidogenesis, where soluble organics are converted to carbon dioxide and volatile fatty acids (VFAs); acetogenesis, where VFAs are transformed to acetate and dihydrogen; and methanogenesis, where the outcomes of previous stages are converted to methane gas. However, conventional AD processes without pretreatment have limited energy efficiency as a result of the extended period needed for full decomposition (Zamri et al., 2021). To address this issue, several

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pretreatment techniques, such as microwave irradiation, ultrasonication, and ozonation, among others, were implemented toward improved biogas yield (Ali et al., 2018). Although the AD process exhibits lower energy intensities than other management techniques, Kumar and Samadder (2020) suggested that AD has the potential to yield digestate to function as a biofertilizer and combustible gas for energy generation. Its high versatility allows the treatment of various carbonaceous feedstocks (Neshat et al., 2017). The AD process can take place in either mesophilic or thermophilic conditions. Hilkiah Igoni et al. (2008) reported that the generated biogas primarily consists of methane with traces of other gases, such as carbon dioxide and hydrogen sulfide. The energy efficiency and performance of the AD process depend on the feedstock composition and multiple operational settings, including C/N ratio, retention time, pH level, and organic loading rate (Jain et al., 2015; Kang et al., 2017). The benefits associated with AD include biogas, which can be harnessed for energy generation, and nutrient-rich digestate as a fertilizer for crops. However, fullscale digesters often require significant capital investment, posing a financial hurdle for adoption (Karki et al., 2021). Additionally, toxic components present in the waste may not be fully consumed during the AD process, necessitating secondary treatment and disposal measures. However, it is important to note that AD has high levels of commercial, technology, and societal technology readiness, as highlighted by its minimal automation and technical prerequisites (Ryue et al., 2020). Despite the challenges, the cost-effective operation and readiness of AD make it a promising waste management solution for industries and communities.

2.2.2

Fermentation

The generation of bioethanol from food waste involves a complex biochemical reaction that includes the hydrolysis of sucrose and the fermentation of sugars (Beyene et al., 2018). This process first involves the hydrolysis of sucrose into fructose and glucose through enzymatic reactions. Subsequently, glucose and fructose are transformed into ethanol by further enzymatic reactions. Finally, anhydrous bioethanol is formed after the ethanol is subjected to enzymatic hydrolysis, fermentation, distillation, and dehydration (Beyene et al., 2018). Bioethanol is typically formed from several food wastes, e.g., potato peel, grape pomace, and banana peel wastes, along with hospitality and household wastes. However, due to the intricate lignocellulosic nature of food waste, various pretreatment techniques were applied to enhance the cellulose solubility (Vavouraki et al., 2013). The utilization of food waste for bioethanol production offers an attractive and viable technology with the potential to enhance waste reduction and lower the environmental load. However, the financial feasibility of this process still requires further research to reduce the ethanol price from food waste. Therefore, continuous efforts are needed toward promoting the overall efficiency and sustainability of the process.

Applications of Life Cycle Costing in Waste-to-Energy Projects

2.2.3

85

Mechanical Biological Treatment

The MBT system is considered a pretreatment technique designed for waste processing by incorporating two distinct stages: mechanical treatment (MT) and biological treatment (BT). The waste is segregated using MT to obtain combustible matter, which is then converted into refuse-derived fuel (RDF) to produce energy. Meanwhile, the remaining waste is employed to produce organic fertilizer and biogas in the BT stage. By enabling the recycling of resources and reducing greenhouse gas emissions, the MBT method offers an effective means of generating sustainable fuels from MSW. Additionally, MBT practices facilitate the conversion of feedstock into various streams that can be utilized for material and energy recovery. To achieve mechanical separation, different mechanisms are utilized, including magnets, ballistic separators, near-infrared separators, and hand gathering, to eliminate large and heavy objects. Moreover, to facilitate separation, plastic and glass wastes undergo shredding, reducing them to an average size of a few hundred millimeters.

3 Life Cycle Costing LCC is a financial analysis performed for evaluating the economic viability of a system or technology. LCCs can be approached from a planning or analysis perspective (Martinez-Sanchez et al., 2015). Planning LCCs assess the financial performance of a technology/system with respect to ongoing changes, whereas analysis LCCs examine economic behavior during its current state. The standardization of LCC is currently limited to buildings and other constructed assets, as outlined in ISO 15686. To date, there are no established guidelines for conducting LCC on WtE or other waste management systems. Instead, researchers tend to adhere to the recommendations of SETAC (Swarr et al., 2011) and ISO 15663-1, 15663-2, and 15663-3 (Ramos et al., 2020a, b). Such standards were merged with the ISO 14044 and 14,040 guidelines of LCA to acquire an LCA-LCC-aligned framework.

3.1

Types and Methods

There are three LCC types: (1) conventional LCC (C-LCC), (2) environmental LCC (E-LCC), and (3) societal LCC (S-LCC), each serving a different purpose. In general, C-LCC focuses solely on the economic aspects of WtE systems while overlooking environmental and social burdens. It accounts for the costs of raw materials, manufacturing, transportation, installation, operation, maintenance, and disposal. It is conducted to (1) assess the economic feasibility of waste treatment systems, (2) determine the most cost-effective solution, and (3) evaluate the financial consequences of employing a particular waste management option (Coelho & De

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Brito, 2013; Karagiannidis et al., 2013). An E-LCC is an integrated environmental C-LCC through the incorporation of a life cycle assessment analysis. When either C-LCC or E-LCC study accounts for the stakeholders affected by the examined waste management scenario, the results reveal the net savings and the total cost distribution among them, i.e., which stakeholder incurs lower or higher expenses. These findings could be utilized to assess the need for economic compensation among stakeholders. As for S-LCC, it accounts for all costs borne by society. It is applied to determine the financial viability of specific waste management scenarios on a social level and to estimate the welfare gains and losses associated with reallocating resources. The three LCC types thereby provide an overall systematic economic assessment framework, either in integration with LCAs or as stand-alone indicators. In general, system boundaries for an LCC study are based on the specific situation at hand; however, their definition should be in line with those of the LCA. Nevertheless, C-LCC may often eliminate certain aspects of the life cycle, thus reflecting the specific objective of the C-LCC. For instance, in a C-LCC study, with a goal of estimating the downstream costs of certain households, the source-separation expenses incurred by said households should be excluded when determining a waste fee, as opposed to E-LCC and S-LCC, where all phases are included.

3.2

Cost and Revenue Streams

Initial and future costs and revenues can be integrated into an LCC analysis by incorporating the time value of money, which considers future interest rates and inflation (Islam et al., 2015). In the LCC approach, future expenses are discounted to the present value (PV), utilizing an appropriate discount rate over their expected lifetime (Kneifel, 2010; Leckner & Zmeureanu, 2011). PV refers to the entire amount required to be reserved today to cover future expenditures. In terms of identifying the costs in an LCC study, there seems to be a consensus on using capital expenditure (CAPEX) and operating expenditure (OPEX) to represent the total expenses. CAPEX refers to the capital costs linked to the initial investment in a WtE infrastructure, including the construction of a waste processing plant or the procurement of equipment and machinery. CAPEX is typically assigned equally among the entire tons of waste processed/disposed of by a certain WtE system throughout its economic life span. It is usually determined as a function of the technology’s annual usage rate, which could be equivalent to the entire or a percentage of the annual capacity of the said technology if the facility functions below its design capacity. OPEX refers to the regular expenses associated with operating and maintaining the WtE plant. Most OPEX occurs in the same year as when the waste is treated/disposed of. Operating expenses can be considered fixed (insurance, labor, and maintenance) or variable (water and electricity consumption). The former costs are obtained by dividing the annual costs by the facility’s annual

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usage rate, whereas the latter costs are acquired by multiplying the physical amounts needed to process 1 ton of waste by the item unit price. The CAPEX and OPEX of a WtE system depend on various factors, including plant capacity and efficiency and the composition of the waste stream. Plant size and capacity have direct implications on the feasibility of WtE plants. For instance, a plant double in size can raise the CAPEX and labor costs by 70% (Jamas & Nepal, 2010). Moreover, plant efficiency has a directly proportional relationship with capital costs, i.e., more efficient plants pose higher capital costs, albeit producing more revenue from increased electricity generation. Furthermore, the composition of waste may add additional necessary costly processes, such as the case of pretreating waste streams containing high levels of contaminants. Apart from those, the presence of other waste management and electricity generation alternatives can also affect the economics of WtE systems. For example, if other waste management options, such as landfilling, are relatively cheaper than the WtE plant, then it may need to be more competitive to remain economically viable. On the other hand, the revenue streams of WtE plants include energy sales, gate fees, carbon credits, and marketing by-products. Energy sales and gate fees are the primary revenue sources, although the percentage may vary across countries. Energy sales from thermochemical systems are usually in the form of heat captured to produce clean energy, whereas those of biochemical systems are in the form of biogas harnessed for producing electricity. Similarly, gate fees are a major revenue source for WtE systems, accounting for 70–80% of the entire revenues in the UK (Jamas & Nepal, 2010). WtE gate fees can be structured in different ways; some facilities charge a flat fee per ton of processed waste or based on waste composition. For example, waste with low calorific value, such as plastic or textiles, may be charged at a different rate than those with high calorific value. The magnitude of the fees can vary based on the quantity and composition of the accepted waste and on local market conditions. It was found that increasing landfill gate fees shall aid in diverting the waste away from landfills toward WtE plants (Jamas & Nepal, 2010). Furthermore, WtE plants can generate carbon credits by reducing the GHG emissions that could have been produced by conventional waste management strategies. Other revenue sources may seem relatively insignificant but can nonetheless be critical, such as the recycling of the produced by-products, e.g., ash, digestate, and biochar.

3.3

Assessment Parameters and Indicators

There are several LCC assessment parameters that are utilized to indicate the financial behavior of the examined system, e.g., net present cost (NPC), net present value (NPV), internal rate of return (IRR), payback period (PP), and levelized cost of energy (LCOE).

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Net Present Cost

NPC is a key economic indicator that accounts for the total costs of operating a system over its life span. NPC is the sum of the PV of the CAPEX and OPEX using the following equation: NPC = CAPEX ×

OPEX 1þi

ð1Þ

where NPC is the net present cost ($), CAPEX is the capital expenditure ($), OPEX is the operation and maintenance expenditure ($), and i refers to the discount rate (%).

3.3.2

Net Present Value

NPV is the PV of expected future cash flows produced during its life span. It is a profitability indicator in evaluating projects, where a positive NPV implies a profitable project. It is computed using the following equation: NPV =

ðCIt –COt Þ × ð1 þ iÞ - t

ð2Þ

where NPV is the net present value ($), CIt and COt are the cash inflows and outflows in year t ($), t is the project economic life (years), and i refers to the discount rate (%).

3.3.3

Internal Rate of Return

IRR is a discounted cash flow measure that determines the return of potential investments (achieving the NPV of all cash flows is equal to 0). It indicates the project profitability; an IRR greater than the discount rate implies a profit. It is typically estimated based on the annual cash flows using a built-in function in Microsoft Excel.

3.3.4

Payback Period

PP refers to the time required for recovering initial expenses. It is applied in assessing projects based on financial recovery. PP is determined from a graph plotted between the cumulative NPV over the project’s economic life, such that the intersection point between the zero NPV line and the curve denotes the PP value.

Applications of Life Cycle Costing in Waste-to-Energy Projects

3.3.5

89

Levelized Cost of Electricity

LCOE is an economic assessment in terms of electricity production from treating waste by WtE technologies. It represents the average minimum revenue per unit of generated electricity required to recover costs and reach a breakeven point during the project life cycle. WtE technologies with lower LCOE values have higher profit potential. It is computed as follows: LCOE =

n ðOPEXt þLt Þ t=1 ð1þiÞt ðEt Þ n t = 1 ð1þiÞt

CAPEX þ

ð3Þ

where LCOE is the levelized cost of electricity ($/kWh), OPEXt is the operation and maintenance cost in year t ($), Lt is the landfilling cost in year t ($), i is the discount rate (%,), n is the design period of the project (years), CAPEX is the investment cost at year 0 ($), and Et is the amount of produced electricity in year t (MWh).

4 Life Cycle Costing Applications in Waste-to-Energy Systems This section discusses the economic viability of multiple WtE systems, as well as outlines the cost and revenue streams of various technologies. Afterward, a thorough review of the existing literature on the application of LCC on WtE systems is presented to gain insights into their economic viability.

4.1

Thermochemical Systems

4.1.1

Cost and Revenue Structures

As mentioned above, thermochemical WtE processes involve the thermal decomposition of carbonaceous feedstock to produce heat energy. As a result, the economics of these processes revolve around the heating mechanism and local marketing conditions. The cost structure of thermal conversion systems mainly depends on reactor configuration, capacity, and operating temperature. For incineration, AlzateArias et al. (2018) have developed models for estimating the initial investments and operation costs: CAPEXðincÞ = 16,578 × P2sðincÞ

ð4Þ

OPEXðincÞ = 0:04 × CAPEXðincÞ

ð5Þ

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where CAPEX(inc) is the capital investment for incineration plants ($), Ps(inc) is the capacity of the incinerator (kW), and OPEX(inc) is the operational and maintenance costs of incineration plants ($). For gasification, Mabalane et al. (2020) established a generalized model to determine the capital and operation costs of gasifiers (Eqs. 6 and 7). It was based on a fluidized-bed gasifier due to its capability to process a wider range of feedstock and generate more syngas with higher energy content: CAPEXðgasif Þ = 3925 × Psðgasif Þ

ð6Þ

OPEXðgasif Þ = 0:04 × CAPEXðgasif Þ

ð7Þ

where CAPEX(gasif) is the capital investment for gasification plants ($), Ps(gasif) is the capacity of the gasifier (kW), and OPEX(gasif) is the operational and maintenance costs of gasification plants ($). In terms of pyrolysis reactors, although there are no established costing models for pyrolysis, there are two formulas that offer a rough estimate of the total expenses for a pyrolysis plant based on industry knowledge and common practice: CAPEXðPYRÞ = A × PsðPYRÞ

ð8Þ

OPEXðPYRÞ = 0:04 × CAPEXðPYRÞ

ð9Þ

where CAPEX(PYR) is the capital investment for pyrolysis plants ($), A is a cost factor depending on the reactor technology and feedstock quality and ranges from $1000–4000 per ton, Ps(PYR) is the capacity of the pyrolysis (ton/day), and OPEX(PYR) is the operational and maintenance costs of pyrolysis plants ($). On the other hand, the main revenue source from thermochemical WtE systems lies within the energy recovery potential and is computed as follows: E rev = ET × EP EP = W d × η ×

ðM i –CVt Þ

ð10Þ ð11Þ

where Erev is the annual energy revenues ($/year), ET is the electricity tariff ($/kWh), EP is the produced energy (kWh), Wd is the dry weight of processed waste (kg), η is the efficiency conversion of the thermochemical WtE systems, Mi is the mass fraction of material, i is the waste stream, and CVi is the calorific value of material i (kWh/kg). Other revenue sources include gate fees and the utilization of value-added by-products like bottom ash and biochar, which could be either marketed as road construction and cement material or recovered as ferrous and nonferrous substances (Meylan & Spoerri, 2014). Therefore, further technological development in material recovery and recycling shall improve the overall economics of the thermochemical systems (Morf et al., 2013).

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4.1.2

91

Review of Existing Literature

LCC analysis was widely adopted to examine the financial performance of thermochemical WtE processes. Table 3 presents some compiled LCC studies on thermal processing systems. Incineration was the most investigated technology, accounting for 50% of the conducted studies, followed by gasification (30%) and pyrolysis (20%). It can be observed that, although grate-based incinerators were the most examined configuration, they were only assessed for processing MSW. For example, Panepinto and Zanetti (2018) and Zhao et al. (2016) examined the financial status of domestic incineration plants in Italy and China, respectively, and disclosed that such systems were found to be economically viable. In contrast, an LCC study carried out in Brazil demonstrated that grate-based incinerators were economically infeasible, primarily due to the low tipping fees paid by the local municipalities (Leme et al., 2014). Such findings emphasize the significant influence of local marketing conditions on the economic considerations of WtE systems. On the other hand, Rostami et al. (2020) performed a gate-to-gate LCC on a fluidized-bed incinerator in Iran and reported that the incinerator was a more financially promising alternative for treating sewage sludge compared to landfilling. This agrees with Dong et al. (2014), who demonstrated that incineration was a more attractive scenario for managing MSW than conventional landfilling. Table 4 lists the capital and operational costs of the different incinerator configurations. The relatively wide ranges can be attributed to the treated waste type, drying mechanism, and/or operating temperature. On the other hand, multiple studies assessed the financial potential of sludge co-incineration. For example, Liu et al. (2023) compared several sludge incineration technologies from a cradle-to-gate perspective, particularly rotary kiln with coal, fluidized bed, and co-incineration in a waste-combusted power plant and coal-fired power plant. The findings revealed that the investment costs of individual incineration systems are twofold that of co-incineration plants. Moreover, the rotary kiln had the lowest costs among the stand-alone systems, while the fluidized bed had the most investment costs. Xiao et al. (2022a, b) had consistent findings, where it was found that mono-incineration was more cost-intensive than co-incineration systems. However, Hong et al. (2013) demonstrated that sludge co-incineration with a coalfired power plant yielded more revenues, despite resulting in a higher environmental load compared to coal-based energy generation systems due to the additional electricity consumption and direct GHG emissions. Gasification, on the other hand, has mostly been evaluated in the literature using the fluidized bed configuration. For instance, Ramos et al. (2020a, b) assessed the efficiency of gasification to treat cork waste in Spain. The findings showed that the thermal system resulted in more net savings with a 10-year PP. Moreover, Jeswani et al. (2019) carried out an LCC study to evaluate the financial sustainability of gasifying poultry manure in the UK. The results demonstrated that the unsubsidized cost of heat recovery from poultry manure was comparable to that of combined power and heat from natural gas but considerably lower than from other fossil-fuel

GASIF

INC GASIF INC GASIF GASIF

China

China China

China

China

Italy China

China

EU

Spain

Portugal

Iran UK China

Spain

Liu et al. (2023)

Liu et al. (2022) Cudjoe and Wan (2022) Xiao et al. (2022b)

Xiao et al. (2022a)

Innocenzi et al. (2021) Zhao et al. (2021a, b)

Yu and Dong (2020)

Ramos et al. (2020a)

Ramos et al. (2020b)

Hermoso-Orzáez et al. (2020) Rostami et al. (2020) Jeswani et al. (2019) Zhou et al. (2019)

Valente et al. (2019)

GASIF

GASIF

GASIF INC PYR GASIF INC

INC

GASIF INC GASIF INC

INC

Country

References

WtEa

Biomass

Plastic waste Sludge PW MSW

Cork waste

MSW

MSW

Wastewater MW

Sludge

MSW Plastic waste Digestate

Sludge

Feedstockb

$15.2–16.9 M $1.8 M $5.0/ton $8.2/ton $0.6/kg H2

$1294.4/kW

$63.0/MWh

$25.4/ton

$9.6/ton

$4.3/ton $116 M/ton $101 M/ton $0.06–0.09 M/ ton $0.03–0.09 M/ ton – –

$1.1–33.4/ton

CAPEX

$0.5–0.5 M/y $1.6 M $19.6/ton $14.5/ton $2.7/kg H2

$85.7–100/ ton $110.34/ MWh $64.72/kW

$4/ton

$0.5–31.4/ ton $1.3/ton $8.2 M/ton $8.5 M/ton $39.7–42 M/ ton $0.03–0.13/ ton – –

OPEX

Table 3 Summary of reviewed LCC applications in thermochemical WtE systems

– 13.5

– –

–

$9.17–7.6 M –

–

– $1.7 M -$8.3/ton -$10.3/ton. $3.8/kg H2

–

– 15.8–20

– $7591.6–16745.1

10

3–30 9.12

3–33.5

–

1.9–5.8 –

– –

–

–

–

$0.075 M

$0.12 M

-$(54–1031) M

–

$0.76–2.16 M –

$0.08–0.3 M/ ton $9.71–11.3 $123.4/ton $63.97/ton $198.4/ton. $8.8–28.6/ ton –

– $174.2 M $68.9 M –

-$0.64/ton $47.5 M $309.1 M -$(0.11–0.05) M/ton –

– – 11.7 5.3 –

–

–

$4.3–30.7/ton

PP (year)

– 8.1 27 –

IRR (%)

NPC

NPV

–

– –

5–6

–

–

–

– –

–

– 0.43 0.23 –

–

LCOE ($/kWh)

92 A. Abdeljaber et al.

PYR

PYR PYR GASIF GASIF INC

INC

INC INC PYR INC INC INC

PYR

INC INC

Australia

Greece – China Canada Italy

Thailand

UK China Canada Brazil China Mauritius

USA

China China

Sludge MSW

Mixed waste Polymers MSW Wood MSW MSW Plastic waste Switchgrass

Plastic MW Rice husk Biomass MSW

Wood

–

– – $7.9/ton

$28.4 M – $9.2/ton

b

$8.6/ton –

– $0.32 M – -$73.8 M – $2.3/ton

– $19.56/ton – $71.7 M $16.8/ton $321.6/ton $120–121/ ton $44.2/ton –

$124.1 – $364–437/y – $5.1/ton –

– –

–

– 12.22 – – – –

– –

–

– 12 – – – –

–

–

-$39.38/ton

-$32.4/ton

– $73,350/ton $0.27 M $28.9 M $3.9/ton $27.2/ton

– – 8.49 – –

– – 12.51 – –

– – – – –

-$2711 – $24.7 M – $299–417 M

–

–

–

-$(13–47)/ton

$24.8/ton

$1487 $293.4/ton -$13.9 M $64.7–134 M $562.7 M

$3 M

$13.6/ton

$1260 $189.9/ton -$52.8 M $647–1340 M $347.67 M

$26 M

INC incineration, GASIF gasification, and PYR pyrolysis MW medical waste, MSW municipal solid waste, and PW poultry waste

a

Lu and El Hanandeh (2019) Ghenai et al. (2019) Hong et al. (2018) Yang et al. (2018) Salkuyeh et al. (2018) Panepinto and Zanetti (2018) Menikpura et al. (2016) Li et al. (2016) Zhao et al. (2016) Homagain et al. (2016) Leme et al. (2014) Dong et al. (2014) Foolmaun and Ramjeawon (2013) Lerkkasemsan and Achenie (2013) Hong et al. (2013) Zhao et al. (2011) – –

–

– – – 0.397 – –

–

– – – – –

–

Applications of Life Cycle Costing in Waste-to-Energy Projects 93

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Table 4 Capital and operational costs of the different incinerator configurations Incinerator type Moving grate Rotary kiln Fluidized bed

CAPEX ($/ton) 8.32–11.86 21.72–28.37 4.94–33.48

OPEX ($/ton) 6.56–21.91 16.31–31.37 16.90–26.27

References Liu et al. (2023), Zhao et al. (2011) Liu et al. (2023) Liu et al. (2023), Zhou et al. (2019)

Table 5 Capital and operational costs of the different gasification configurations Gasifier type Fixed bed Fluidized bed Plasma

CAPEX ($/ton) 3.89–26.95 12.32–50.00

OPEX ($/ton) 1.18–116.73 2.46–10.00

References Liu et al. (2022), Tarpani and Azapagic (2018) Sara et al. (2016)

4.35–25.72

2.00–85.72

Liu et al. (2022), Ramos et al. (2020b)

options. Furthermore, Yang et al. (2018) explored the potential valorization from different biomass and indicated that fluidized-bed gasifiers produced promising results with 8.5-year PP. Additionally, Salkuyeh et al. (2018) conducted a comparative LCC on fluidized beds and entrained flow gasification and revealed that the latter has more financial burdens. Ramos et al. (2020a) presented a prefeasibility assessment during the early development stages of the implementation of plasma gasifiers in EU countries. They proposed three scenarios: two of them included slag commercialization with electricity replacement, while the third regarded it as disposable waste. The most financially profitable condition was the combination of selling slag and electricity, which resulted in revenues of over $121.5 per ton of processed MSW, reducing the final treatment cost from $24.7 to $1.9 per ton. Furthermore, Liu et al. (2022) evaluated the efficiency of plasma gasification with different scales in treating mixed domestic waste at the village, town, and county levels. The LCC findings showed that the village model delivered the highest expenses, whereas the town model exhibited the lowest burdens. Table 5 summarizes the capital and operational costs of the different gasification configurations. As for pyrolysis, Lu and El Hanandeh (2019) explored the effect of operating temperature on fast pyrolysis to maximize economic and environmental benefits. The study showed the NPC was inversely proportional to pyrolysis temperature due to the increased production of bio-oil, which has a higher calorific value compared to biochar. Moreover, Lerkkasemsan and Achenie (2013) evaluated the economic performance of the fast pyrolysis of switchgrass to replace coal in conventional power plants. The findings demonstrated that NPC was influenced by the reactor size, with costs rising from $120.53 to $121.72 per ton as the capacity grew from 100 to 5000 tons per day. On the other hand, Homagain et al. (2016) conducted a comprehensive LCC of biochar-based bioenergy production using slow pyrolysis. It was revealed that financial feasibility is directly dependent on pyrolysis capacity and feedstock pretreatment.

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There were limited studies that focused on comparing multiple thermochemical WtE systems. Overall, gasification and pyrolysis have been found to be more financially favorable, mainly due to their higher energy potential compared to conventional incineration (Zaman, 2010). For example, a study in China compared the financial feasibility of plasma gasification compared to incineration in treating mixed plastic wastes (Cudjoe & Wang, 2022). The results demonstrated that plasma gasifiers were more economically beneficial with a higher NPV of $309.1 million and IRR of 27%, in addition to a lower PP of 5.3 years and LCOE of $0.230/kWh, compared to incineration, with NPV, IRR, PP, and LCOE of $47.5 million, 8.1%, 11.7 years, and $0.433/kWh. Another study examined the economic efficiency of incineration, gasification, and pyrolysis in treating medical waste (Zhao et al., 2021a, b). The LCC findings revealed that incineration delivered the lowest NPC. Nevertheless, gasification and pyrolysis have not yet been widely applied on a commercial scale globally for treating a variety of wastes, which can be tentatively explained by their poor efficiency and extensive gas-cleaning processes (Luz et al., 2015).

4.2 4.2.1

Biochemical Systems Cost and Revenue Structures

Biochemical WtE processes involve the decomposition of organic biomass to generate methane by biological means. The cost structure of biological conversion systems mainly depends on reactor configuration, capacity, and efficiency. The models for determining costs are the same for all bioconversion processes: CAPEXðbioÞ = C pðbioÞ × PsðbioÞ

ð12Þ

OPEXðbioÞ = 0:03 × CAPEXðbioÞ

ð13Þ

where CAPEX(bio) is the capital investment for biochemical WtE plants ($); Cp(bio) is the plant-specific cost, assumed as 4339 kW; Ps(bio) is the plant capacity (kW); and OPEX(bio) is the operational and maintenance costs of the plant ($). On the other hand, the main revenue source from biochemical WtE systems depends on the amount of methane produced and is computed as follows: Erev = ET x M CH4 × ECCH4 × η

ð14Þ

where Erev is the annual energy revenues ($/year), ET is the electricity tariff ($/kWh), M CH4 is the generated methane mass (kg), ECCH4 is the methane energy content (14.31 kWh/kg), and η is the efficiency conversion of the biochemical WtE systems.

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Review of Existing Literature

As with thermochemical systems, LCC analysis was implemented to evaluate the financial performance of biochemical WtE processes. Table 6 presents the complied LCC studies on AD, fermentation, and MBT. AD was the most investigated technology, accounting for around 60% of the conducted studies, followed by MBT (25%) and fermentation (15%). It can be observed that the continuous stirred tank reactor was the most examined configuration for a different mixture of feedstocks, such as cow manure, food waste, and MSW. For example, Balcioglu et al. (2022) examined the economic performance of AD plants with various feedstock mixes in Turkey and concluded that such systems were economically feasible. On the other hand, LCC studies performed in China and Oman demonstrated that these AD systems can be financially infeasible, mainly due to the low selling price of produced biomethane (Al-Wahaibi et al., 2020; Li et al., 2020). These findings accentuate the substantial influence of local marketing conditions on the economic feasibility of WtE systems. In addition, several studies further assessed the financial perspective of AD systems. Chowdhury (2021) assessed the viability of an AD plant for a variety of mixed waste types, including waste from university/hotels, slaughterhouse waste, and poultry farm manure. In addition to generating clean energy from waste, the financial evaluation (PP = 5.4 years) suggests that this plant could serve as a practical option for achieving the United Nations’ SDG of sustainable and affordable energy by 2030. This conclusion agrees with outcomes from Rouhollahi et al. (2020), where the PP of all considered AD scenarios was less than 5 years, which further illustrates the economic feasibility of such systems. Furthermore, Franchetti (2013) evaluated the economic performance of four different AD technologies using LCC for a large food service provider in the United States. This study analyzed the economic feasibility of several AD configurations for treating food waste compared to conventional landfilling. The results demonstrated that the implementation of on-site WtE systems would lead to reduced operational costs and decreased environmental impacts. Moreover, multiple studies have investigated the financial viability of MBT systems. Rigamonti et al. (2019) conducted a comprehensive LCC analysis of various scenarios for the processing of residual MSW using pretreatment in MBT plants, followed by energy and material recovery. In this regard, four distinct scenarios were carefully evaluated, and the research represents the first-ever adoption of the E-LCC methodological framework in Italy for such purposes. The results indicated that treating residual waste in single-stream MBT plants and generating solid recovered fuel with the requisite characteristics for co-combustion was the most cost-effective approach, leading to the highest exergy yield and the lowest cost per unit of exergy. It is, however, essential to note that these results must be considered within the limitations of the data and estimates analyzed, including the examination of only four scenarios, as the actual costs may vary based on numerous factors.

AD

FERM

FERM

AD

–

China

Iran

China

USA

USA

Oman

Italy

China

USA

Li et al. (2021)

Rouhollahi et al. (2020)

Li et al. (2020)

Muhammad and Rosentrater (2020a, b) Muhammad and Rosentrater (2020a, b) Al-Wahaibi et al. (2020)

Rigamonti et al. (2019)

Fei et al. (2018)

Franchetti (2013)

a

FERM

–

Marzocchella et al. (2022) Chowdhury (2021)

Food waste

MSW

Feedstock MSW Poultry waste Food waste Mixed waste Dairy manure Cow manure Dairy manure Food waste Food waste Food waste MSW

$14500–23,500

$8000–11,100/y

$184.4–236.1/ ton $17.3–23/ton

– –

$322.4

$25–44 M/y

$21–37 M/y

$59,453–89,396

$1300/y

$97284–118,909

$1.8 M

$0.008–0.01/MJ

OPEX $0.2–0.4 $1.5/ton

$6448

$387–545 M

$387–677 M

$0.52–0.55 M

$20,500

$1.3–1.6 M

$34.2 M

$0.08–0.14/MJ

CAPEX $1.1–4.3 M/ton $75/ton

AD anaerobic digestion, FERM fermentation, and MBT mechanical biological treatment

AD

MBT

MBT

AD

AD

AD

WtEa AD AD

Country Italy Turkey

References Demichelis et al. (2022) Balcioglu et al. (2022)

Table 6 Summary of reviewed LCC applications in biochemical WtE systems

–

($158.7–39.5) M –

–

– –

6.18–9.65

–

$2421.8–3108.3 –

(-24.7)–6

1.35–2.36

7.44–8.82

–

–

–

–

– 10

–

–

– 10

–

– 14–20

-$0.9–0.4 M

–

31

–

– –

–

–

-$1.6–$1.1 M

–

–

LCOE ($/kWh) – 61–93

3.2

5.5

–

–

–

–

PP (year) 10–12 1.6–2.1

$17.7 M

IRR (%) 22.47–28.45 –

NPV $2.6–3.1 M $4.7–6.2 M

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As for fermentation, Muhammad and Rosentrater (2020a, b) carried out a technoeconomic analysis to assess the economic impact of three different scenarios of fermenting food waste. The three scenarios were (1) two-step distillation fermentation with hydrolysis enzymes, (2) two-step distillation fermentation without enzymes, and (3) one-step distillation fermentation without enzymes. The results showed that the production of ethanol with two-step distillation fermentation without enzymes had a competitive price of $0.64 per liter, which suggests that fermenting food waste without enzymes is more cost-effective and practical compared to enzymatic-assisted fermentation.

4.3

Comparative Assessment

This section intends to provide a comparative assessment of the financial feasibility of various WtE systems. Table 7 lists the reviewed LCC studies on different WtE systems. The most reviewed LCC case studies used a functional unit based on the waste input, i.e., the unit mass of waste received/treated at the WtE plant (80%). Such functional unit indicates an evaluation perspective associated with the “treatment of X ton of waste”, which subsequently facilitates comparison among individual WtE technologies. The other 20% either had functional units related to specific output from the treatment facility, such as 1 MWh of energy and 1 kg of H2, or did not define one at all. Table 7 indicates that gasification usually outperformed other WtE systems. For instance, Abdeljaber et al. (2022) examined the economic feasibility of incorporating gasification and MBT as part of integrated solid waste management strategies. The LCC results proved that the MBT- and gasification-based scenarios appeared to be the most economically viable, scoring an NPV of $364 and $284 million, respectively, compared to the conventional incineration- and AD-based alternatives. Similarly, Mabalane et al. (2020) found that gasification had lower financial burdens compared to AD, with a PP of 6.8 and 7.6 years, respectively. Moreover, Tarpani and Azapagic (2018) demonstrated that gasification was the most promising management system for MSW, compared to incineration and AD. Another LCC study revealed that gasification had the highest NPV in treating wood waste compared to pyrolysis, incineration, and fermentation. Conversely, a study in Nigeria showed that AD had a slight economic advantage over gasification, with NPV scores of $632 and $610 per ton of waste (Nubi et al., 2022). This is mainly due to the development status of Nigeria, where food waste comprises the highest waste fraction. On the other hand, incineration was mostly found to be more financially favorable than AD systems. Although the initial investment in AD is lower than in other systems, it does not directly reflect its profitability. The ancillary facilities, including substrate pretreating, pollution control systems, and other supplementary features for AD may greatly increase the financial load. Abdallah et al. (2018) assessed the financial feasibility of implementing AD and incineration systems in the UAE. The findings revealed that incineration was more cost-effective in terms of PP and IRR,

FERM GASIF INC INC&AD

Nigeria

Canada

China

China

–

Nubi et al. (2022)

Zhuang et al. (2022)

Zhang et al. (2022)

Liu et al. (2022)

Talang and Sirivithayapakorn (2021)

AD INC

AD INC PYR

AD GASIF

Country UAE

References Abdeljaber et al. (2022)

WTEa AD INC GASIF MBT

MSW

MSW

Sludge

Sludge

MSW

Feedstock MSW

$12/ton $17/ton

$771/ ton $1317/ ton $3.1/ton $3.9/ton

CAPEX $300/ ton $550/ ton $500/ ton $450/ ton $4339/ kWh $3025/ kWh $16 M $13 M $19 M

$0.97/ ton $1.2/ton $21.5/ ton

$3.2/ton $0.9/ton

$130/ kWh $157/ kWh $0.32 M $0.26 M $0.38 M

OPEX $30/ton $30.25/ ton $60/ton $36/ton

–

– $(494–447)/

$2.6/ton $1.5/ton

–

–

-$754/ton -$1366/ton

–

– $17900/ ton $18000/ ton $12900/ ton –

–

33.5 36.7

IRR (%) 7 8 9 9

$467/kWh $419/kWh

NPC –

$632/kWh $610/kWh

NPV $33 M/ton $258 M/ton $364 M/ton $284/ton

Table 7 Summary of the reviewed comparative LCCLife cycle costing (LCC) studies on various WtE systems

–

–

–

–

2.87 2.72

PP (year) 25 17 16 17

–

–

–

–

(continued)

0.08 0.077

LCOE ($/kWh) 0.12 0.134 0.108 0.141

Applications of Life Cycle Costing in Waste-to-Energy Projects 99

Country

India

South Africa

USA

UK

UAE

UK

References

Sharma and Chandel (2021)

Mabalane et al. (2020)

Lee et al. (2020)

Slorach et al. (2019)

Abdallah et al. (2018)

Tarpani and Azapagic (2018)

Table 7 (continued)

AD INC PYR

AD INC

AD INC

AD INC

AD INC AD GASIF

WTEa

Sludge

MSW

FW

OFMSW

MSW

MSW

Feedstock

$3.3/ton $5.1/ton $2912/ kWh $194/ kWh $25 M/ton $140 M/ton $8.6/ton $34.2/ ton $3000/ kWh $4000/ kWh $22.9/ ton $18.6/ ton $26.9/ ton

CAPEX

$66/ton -$25.4/ton $355.8/ton

-$127 M $180 M

$33/ton $45/ton

-$50 M/ton -$150 M/ton

ton $(380–298)/ ton $32/ton $36/ton –

$37.8/ ton

$3.8/ton $8.2/ton $145.6/ kWh $7.8/ kWh $20 M/ton $30 M/ton $9.8/ton $31.8/ ton $66/ kWh $128/ kWh $33.5/ ton $49.0/ ton $116.7/ ton

NPV

OPEX

–

–

6.9 11.3

–

– $134.5/ ton $86.8/ton –

–

–

–

–

NA 19

6.8 7.6

17.0 15.1

–

–

PP (year)

–

IRR (%)

–

NPC

–

0.101 0.096

–

–

1.61 0.19

–

LCOE ($/kWh)

100 A. Abdeljaber et al.

a

Nigeria

Australia

KSA

Korea

Ogunjuyigbe et al. (2017)

Lu and El Hanandeh (2017)

Hadidi and Omer (2017)

Kim et al. (2011)

AD INC

AD GASIF

FERM INC GASIF PYR

AD INC GASIF AD INC

FW

MSW

Wood

MSW

MSW

$4339/ kWh $3925/ kWh $34.7/ ton $111.8/ ton

$130.1/ kWh $157.0/ kWh $90/ton $8/ton

$2.3 M/y NA $2.3 M/y $2.9 M/u

–

–

$23.7 M NA $27.0 M $24.5 M

–

–

-$110.5/ton -$88.8/ton

$125 M $35 M

$2.1/ton -$22.4/ton -$2.3/ton $3.7–55.7/ ton $(6.7–69.7)/ ton –

$124.7/ ton $119.8/ ton

$183/ton $270/ton $177–305/ ton $204–276/ ton –

$9.9/ton $57.6/ton $30.1/ton –

4.9 9.7

–

–

–

–

25.2 11.1

2–19 21–47

–

–

–

AD anaerobic digestion, FERM fermentation, MBT Mechanical biological treatment, INC incineration, GASIF gasification, and PYR pyrolysis

Spain

Fernández-González et al. (2017)

–

0.14 0.13

–

0.012–0.081 0.203–0.458

–

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Fig. 2 Uncertainty analysis in LCC on WtE systems

mostly due to the larger amount of treated waste. Moreover, it was found that AD was significantly influenced by the high landfilling cost and the low separation efficiencies. However, adopting specific legislative and local adjustments made it more financially viable. Another study in the UK showed that incineration outperformed AD with NPV values of $45 and $33 per ton of processed waste (Slorach et al., 2019). Any LCC analysis is evidently influenced by the assumptions made for the investigated system. Therefore, conducting a sensitivity analysis is deemed essential to address uncertainties within the system and factors considered in the LCC study. Figure 2 presents the variables addressed in the uncertainty analysis in the literature. The variations of those parameters can potentially occur due to changes in energy production, equipment purchase, and legislation, among other market and local conditions. The common practice was to apply ±5–10% change in the examined variable. Around 60% of the reviewed studies have performed a sensitivity analysis to provide insights on the impact of multiple operation parameters on the financial feasibility of WtE systems. Varying the CAPEX and OPEX was the most frequently

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targeted parameter. It is considered challenging to estimate those costs since they depend on several factors. Nubi et al. (2022) indicated that a 10% increase in CAPEX led to a 10% increment in NPC, LCOE, and PP. Moreover, it was found that NPV has an inverse relationship with the changes in CAPEX and OPEX. Discount rate and electricity tariff were found to be crucial factors regarding the financial viability of WtE projects. Most LCC assessment indicators shall change significantly once the discount rate varies. For example, increased discount rates shall minimize the LCC, NPV, and LCOE, but IRR and PP will remain unchanged. Nubi et al. (2022) demonstrated that applying a 10% increase in the discount rate for an incineration plant lowered the LCC, NPV, and LCOE by 1.7, 8.0, and 4.4%, respectively. Similarly, NPV was found to be extremely sensitive to electricity tariffs and energy potential (Abdallah et al., 2018; Mabalane et al., 2021). This is expected as the main revenue stream of WtE systems relies heavily on those parameters. For separation efficiency, the findings revealed that higher efficiency would ensure the effective functioning of the WtE plants. Hence, it is critical to adopt various reforms that promote public participation in separating recyclables and food waste at home.

5 Case Study: Life Cycle Costing Application As mentioned above, the financial feasibility of WtE systems relies heavily on the composition of feedstock and the socioeconomic status of the concerned country. Therefore, a comparative LCC case study was carried out on selected regions across the world to reflect the effect of waste management profiles and market conditions on the viability of WtE systems. The analysis focused on the two most common WtE technologies, incineration and AD, in Germany and Egypt as representatives of developed and developing countries, respectively. Two WtE-based management scenarios were developed according to varied waste separation and processing methods. The main components involve material recovery (material recovery facility (MRF)), energy potential (incineration and AD), and ultimate disposal (sanitary landfilling). Figure 3 illustrates the proposed WtE strategies. The first incineration-based scenario contains a dual-bin collection system for recyclables and commingled wastes. The former is sent to MRF for material recycling, whereas the latter, along with MRF rejects, are combusted for heat and ash. On the other hand, the second, AD-based, scenario includes source separation into food and commingled wastes. The AD plant transforms organic waste into biogas harnessed for generating electricity and digestate disposed of in landfills. In addition, a dirty MRF is utilized to separate recyclables, whereas the rejects are disposed of in landfills. The recyclable participation rate was assumed to be similar in both countries at 20%, with 5% annual increments, upon achieving a maximum of 90% (Abdeljaber et al., 2022).

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Fig. 3 Schematic flow diagrams of the developed WtE systems based on (a) incineration and (b) anaerobic digestion

5.1

Local Conditions

With a population of 83.2 million inhabitants with a growth rate of 0.3%, Germany is the most populated country in the European Union (World Bank, 2023a). In 2018, the MSW generation rate in Germany was 1.71 kg/capita/day, translating to an annual generation of 51.9 million tons (World Bank, 2018). On the other hand, Egypt has a population of 109 million, with a growth rate of 2.1% (World Bank, 2023b). The MSW daily generation level in Egypt stands at 0.82 kg/capita (Abdallah et al., 2019). The average waste composition of the two countries is shown in Table 8. The high fraction of food waste in Egypt is beneficial if the waste is anaerobically digested, whereas high proportions of paper and plastics in Germany can potentially generate higher energy through incineration.

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Table 8 Waste compositions of Germany and Egypt Waste composition (%) Germany Egypt

Recyclables Paper Plastic 31 11 6 12

Glass 7 1

Metals 6 4

Nonrecyclables Textiles Food 3 28 2 66

Wood 3 1

Others 11 8

Fig. 4 Energy potential of the proposed WtE strategies for Germany and Egypt

The investigated scenarios were assessed over a 25-year life span at a discount rate of 6%. The utilized electricity tariff was $0.03 and $0.06 per kWh for Germany and Egypt, respectively, while the tipping fees at the WtE plants and landfill were $60 and $30 per ton of waste, respectively. It is worth noting that the profits from MRFs were not considered in this study to focus on the economic behavior of the WtE systems. The CAPEX values were taken as $550 and 300 per ton for incineration and AD, respectively, whereas OPEX was counted as 10 and 5.5% of CAPEX, respectively (Abdeljaber et al., 2022).

5.2 5.2.1

Findings and Discussion Energy Potential

The waste composition and generation data were utilized to determine the energy potential for each scenario. For the incineration-based strategy, the calorific value for each waste fraction was used to compute the amount of energy generated. Figure 4 depicts the energy potential of the proposed strategies for Germany and Egypt. In the incineration scenario, Germany produced around 577.94 kWh per ton of waste, translating to a total of 777.9 GWh over the end of the 25-year assessment period

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(GER-INC). On the other hand, Egypt demonstrated a lower energy yield of 429 kWh per ton of waste, with a total of 353 GWh (EGY-INC). This difference is mainly due to the high energy content of recyclables, such as paper and plastic, resulting in greater energy potential in Germany compared to Egypt, with 42% and 18% recyclables, respectively. On the other hand, the methane yield from AD was computed via stoichiometry. Egypt generated around 893 kWh per ton of waste, corresponding to a total of 544.2 GWh (EGY-AD), whereas Germany produced 324 kWh per ton of waste, translating to a total of 432 GWh (GER-AD). This changeover can be attributed to the considerably higher food waste portion in Egypt. Therefore, applying incineration (thermochemical) and AD (biochemical) to manage the waste in Germany and Egypt, respectively, would potentially produce enough energy to cover around 15% of the required domestic electricity.

5.2.2

Life Cycle Costing Results

The economic model was established to evaluate the performance of the two proposed WtE scenarios by assessing a set of financial indicators, particularly NPV, IRR, LCOE, and PP. The NPVs of the investigated scenarios are presented in Fig. 5. For Germany, the cumulative NPV computations demonstrated that GER-INC was more profitable than GER-AD, with NPVs of $3621.5 and $665.8 million, respectively. Such difference is mainly the result of considerably larger energy revenues for the incineration-based scenario. In contrast, the AD-based alternative was more financially feasible, with an NPV of $3735.8 million, compared to the incineration-based scenario ($1181.9 million). It can be observed that the least economically viable strategy for Egypt (EGY-INC) is still considered to be more profitable than the most financially feasible option for Germany (GER-INC). This can be attributed to the higher electricity tariff in Egypt (twofold that of in Germany).

Fig. 5 Annual net present value of the developed WtE strategies for Germany and Egypt

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Fig. 6 Cumulative net present values of the developed WtE strategies for Germany and Egypt

The profitability of GER-INC and EGY-AD was further confirmed with 8.3% and 9.0% IRR, respectively, which are more than the applied discount rate, implying profitable systems. Similarly, for Germany, the incineration-based strategy achieved a lower LCOE of $0.090 per kWh, whereas the AD-based alternative demonstrated a lower LCOE of $0.097 kWh. Albeit the proven economic profitability, the LCOE values of all scenarios were greater than the current electricity tariff since it does not account for other revenue streams of WtE processes, such as tipping fees. The PP of the developed WtE system is depicted in Fig. 6 by plotting the cumulative NPV during the assessment period. In the opening year, GER-INC had the lowest NPV, followed by EGY-INC, EGY-AD, and GER-AD. This is mostly attributed to the higher CAPEX at the full treated capacity. For Germany, the incineration- and AD-based scenarios took almost 16 and 21 years to recover from the initial investment. In contrast, for Egypt, the AD-based strategy required less duration (18 years) to start generating profits compared to the incineration-based alternative (24 years).

5.2.3

Summary and Remarks

This chapter performed a comparative LCC study to assess the economic viability of various WtE approaches based on different parameters. Two WtE processes, one thermochemical (incineration) and one biochemical (AD), were applied in two countries with different development statuses, e.g., Germany and Egypt. Table 9 summarizes the LCC findings of the investigated systems. Based on the results, incineration was the more economically viable option in Germany, while AD was more profitable in Egypt. This indicates that the economic

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Table 9 Summary of the LCCLife cycle costing (LCC) assessmentLife cycle costing assessment indicatorsIndicators for the case study Country Germany Egypt

WtE system Incineration Anaerobic digestion Incineration Anaerobic digestion

NPV ($ million) 3621.5 665.8 1181.9 3735.8

IRR (%) 8.25 7.30 6.71 9.30

LCOE ($/kWh) 0.090 0.078 0.182 0.097

PP (Year) 16 21 24 18

viability of WtE systems is highly dependent on local and market conditions, waste composition, and the country’s economic status. The results suggest that countries with a higher organic fraction would benefit more from using biochemical WtE processes, while countries with a higher proportion of recyclable waste would be better suited to thermochemical WtE systems. Moreover, the findings highlight the need for a context-specific approach to WtE system selection.

6 Conclusion Sustainable urban development requires tackling waste management challenges from different aspects. WtE conversion processes have emerged as promising alternatives to minimize the environmental load of extensive waste management operations while diversifying the energy mix to promote a better renewable energy share. However, the substantial expenses required, along with the obscured and unverified long-term gains have impeded the widespread adoption of these systems. As one of the three pillars of sustainability assessments, LCC provides a systematic approach for evaluating the financial feasibility of WtE projects over their life span. The present chapter offered a comprehensive overview of the applications of LCC in WtE systems. The review revealed the overall economic superiority of thermochemical conversion processes over biochemical systems. Moreover, a comparative LCC case study was conducted to investigate the economic viability of various WtE processes for different conditions. Two WtE processes, incineration (thermochemical) and AD (biochemical), were each applied for Germany and Egypt. The results showed that incineration was the more financially feasible management method in Germany, while AD was more profitable in Egypt. Moreover, it was indicated that the economic behavior of WtE systems is highly dependent on local and market conditions, waste composition, and the country’s development status. Future prospects include the incorporation of life cycle and social impact assessments, along with LCC, to offer policymakers valuable insights on sustainable and cost-effective waste management strategic planning.

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Life-Cycle Cost Assessment of Various Wave Energy Converters (Based on Energy and Carbon Intensity) Mohammad Hossein Jahangir and Shahrzad Bahrizadeh

1 Introduction to WECs LCC According to REN21 reports, although global energy transition has not happened yet, the share of renewable energy use in the electricity sector in 2021 was 28%, and its noteworthy wave power is the most consistent and predictable source with the highest energy density over all other renewable energy sources. However, it’s emission-free during electricity generation process, but the environmental impacts throughout wave converter’s supply chain should be assessed. Herein, life-cycle costing assessment is used to evaluate various wave converts for the environmental impacts of products and services throughout their life cycle. A technology’s life cycle will include “cradle-tograve” steps, consists of material extraction, processing, manufacturing, assembly, installation, operation and maintenance, and decommissioning, and this loop continues through recycling. In each process, environmental impacts should be trade-off to harness high efficiency energy converters, mostly comprise climate change, ozone depletion, acidification, eco toxicity, and metal depletion. In comparison with nonrenewable sources, impact potentials like climate change are significantly lower, so it should be considered in their LCC definitions that various impact categories can play a role in decision making. Wave energy converters (WECs) are capable of attaining 2–10 GW worldwide wave power; however, they are rarely deployed in oceans and also need to be

The original version of the chapter has been revised. A correction to this chapter can be found at https://doi.org/10.1007/978-3-031-40993-6_7 M. H. Jahangir (✉) · S. Bahrizadeh Faculty of New Sciences and Technologies, Renewable Energies and Environmental Department, University of Tehran, Tehran, Iran e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023, Corrected Publication 2023 S. S. Muthu (ed.), Life Cycle Costing, Environmental Footprints and Eco-design of Products and Processes, https://doi.org/10.1007/978-3-031-40993-6_5

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Fig. 1 Roadmap of the chapter

assessed for commercial competitiveness. The hydrodynamic performance of different WEC types could be investigated based on the degree of freedom (DOF) to increment wave power capture and hence commercial justification. Depending on DOF, subsystems can be categorized into oscillating body, oscillating water column, overtopping device, and diaphragm pressure differential. Wave Dragon, as an overtopping WEC, oscillates with one or more degrees of freedom. The Mega Roller WEC is an oscillating body with movement like pitching as its type of degree-offreedom. Pelamis as the world’s first commercial WEC is a multifreedom device based on pitch and yaw freedoms, so various LCC methods were used to study environmental impacts in completion. Life-cycle cost assessment as a well-established approach ascertains which technology is the most appropriate for energy conversion due to greenhouse gas emissions (GHG) and return of energy investment (EROI). In addition, it employs inventory analysis and impact assessment. To collect the data needed for the inventory analysis from different databases, a life-cycle inventory (LCI) is used, and these data will be analyzed through life-cycle impact assessment (LCIA) methods. It should be noted, life-cycle costing (LCC) is a generally life-cycle approach which different elements are taken into account, in comparison with LCC and its environmental impacts. So, overall, this chapter will consider the various LCC methods used for the three types of wave converters: Wave Dragon, Mega Roller, and Pelamis p1. In this chapter, LCC analysis of these three types will be reviewed which are analyzed by LCC software tools, SimaPro. The ecoinvent database was used as the source for production to decommission of referred materials and systems, published by the Swiss Centre for Life Cycle Inventories. Mass balance on the British foundry manufacturing sector, carried out by Donohoe et al. was considered in some cases. Another database which comprises landfill of materials was selected from the European Life Cycle Database. It should be noted recycled content method can be effective and somehow was discussed. Eventually analysis for uncertainty was used over the requirements that Monte Carlo can be mentioned as well. This chapter is a guide to understanding life-cycle cost assessment (LCC) of three models of wave energy converters (WEC) with different energy and carbon intensities, foregoing roadmap is presented in Fig. 1.

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2 Theory 2.1

Wave Energy

Long ago, the discovery of coal mines and oil wells was considered a great change in the life of mankind, and with access to these underground resources and their by-products, a prompt move toward development became feasible, but it did not last long as the nonrenewability of these resources turned it into a nightmare for the world. Therefore, due to the limited underground energy that can be extracted and on the other hand, the environmental pollution from the beginning of the extraction process to the processing stages, countries are trying to accede renewable energies and most importantly, energies with the least negative environmental consequences that are compatible with the environment and have the least impact on ecological changes. Based on the erstwhile researches, seas and oceans can be endless sources of clean and renewable energy, along with solar and wind energy that can guarantee the world’s energy future. Sea is the source of various energies that mankind has managed to discover so far, among which the energy of waves, tides, sea currents, energy caused by heat and density difference can be pointed out. Research indicates that Iran has the largest amount of marine energy, which can be found in the southwestern part of Qeshm Island in the Persian Gulf, in an area between Jask and Chabahar, and in a special area called Khormusi in Bushehr province. Marine energy is considered to be the cleanest and most potent renewable energy, accordingly advanced countries have comprehensive plans to extract energy from the seas and oceans. The source of marine energy is the sun, like all other renewable energies. Due to REN21 report, tidal stream and wave power are concentrated mainly in Europe, especially the United Kingdom with significant resources. However, most researches and development plans are in Canada, the United States and China and also EU expects to reach 40 GW of ocean power by 2050, including at least 100 MW of pilot projects by 2025 and 1 GW by 2030 (National Renewable Energy Laboratory (NREL), 2021). Extractable energies from the sea generally include the following sources, which are also briefly described in the next sections: – – – – – –

Sea and ocean waves (wave energy) Tidal energy Sea currents (marine current energy) Temperature difference (ocean thermal energy conversion) Density difference (salinity) Biological resources and marine sediments

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Wave Energy Conversion

Wave energy is a renewable source and is usually more renewable than wind energy. Wave energy is quickly replenished by interactions with the two surfaces of oceans, also it is irregular, fluctuating, and has a low frequency that must be converted to 60 Hz before being sent to the grid. The total prospective of wave energy in the world will be 2 TW (2 million MW) of electrical energy. Approximately, up to 20% of this energy can be exploited. Until the middle of the 1990s, more than 12 different systems were proposed to extract this energy, but only a few of the new systems were economically and technically feasible. Wave energy cannot be extracted at any point; the best areas for a power plant are places where the wave height is high. Therefore, suitable windy areas would be areas with 40–60° latitude generally or narrow straits, the edges of islands, and high land parts of the sea. The west coast of Scotland, northern Canada, and southern Africa and the northeast and northwest coasts of the United States are rich in wave energy potentials. Estimations indicate that only in the Pacific Northwest, it is possible to produce 40–70 kW electrical energy/meter of the west coast. These beaches are more than 1600 km long, and at least 10 MW/km of the beaches can be extracted on average. The highest density of wave energy occurs on coasts that are exposed to long and continuous wind current. Despite covering more than two-thirds of the earth’s surface by the seas and the presence of outspread beaches for the exploitation of the great energy of strong waves, mankind has not been able to seriously exploit this huge source of energy. In recent centuries, the idea of extracting wave energy has been discussed sporadically, but practically, in line with this, in the 1970s, some countries, including England, America, India, Japan, Ireland, and Canada, Relaunched projects, mostly at experimental and researches levels. Wave energy conversion methods are divided based on system movement, which is presented in Fig. 2:

Fig. 2 Type of system movements (Aubry et al., 2011)

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Heave (top and down movement) Pitch Surge Yaw Compartment resonator (cavity resonators) Particle movement (particle motion converter) Oscillating water column

2.3

The History of Wave Energy in the World

The idea of wave energy production emerged in the eighteenth century, but until the mid-1970s, specific practical measures were not taken to harness wave energy. After the oil crisis, the scientists realized the need for alternative sources of energy; the most important step to meet this need was taken by Stephen Salter, one of the innovative engineers at the University of Edinburgh, Scotland. He invented Salterduck, a generator for sea waves, which consisted of several floating and flexible tanks, which are equal to a small house in size and connected to each other on the sea surface. When the waves hit these tanks, each of them shook like a cradle and produced significant mechanical energy, which was converted into rotational movement by means of hydraulic arms installed inside these tanks and caused the generator to rotate. As claimed by the designers, each of these reservoirs was capable of producing 6 MW of electricity, which is sufficient for 4000 Ω. The initial estimates for the cost of generating electricity were close to 1 dollar/kWh, which was much higher than the cost of nuclear power (expensive source of electricity generation) at that time. However, Dr. Salter and his research group were able to significantly improve and strengthen their design, cost reduction of electricity generation using their innovative method and make it equal to nuclear power. But these developments did not prevent the British government from stopping the program in 1982. Although the main reason for this closure was never announced publicly, many experts believe that the main opponents of the continuation of this project were the stakeholders in the nuclear sector in the government and legislative institutions of England, who saw the wave energy development as a disadvantage. Unfortunately, the tanks designed by Salter were never sent to the seas and oceans, but they became a basis for the formation of new ideas for the optimal harnessing of wave energy. He got an interesting and useful tool to bring his teacher’s dream closer to reality by installing facilities in the areas of Scotland, England, and Portugal, each of which consists of three reservoirs for generating electricity of waves with a capacity of 750 MW. These three reservoirs, which are nearly 140 m in length, are connected from different directions, and through wave collision, these tanks collide simultaneously and violently, generating power that can be used by the hydraulic levers to rotate the generators and turbines installed between these tanks. The interesting and surprising point about these facilities is

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Fig. 3 The map of distribution of the energy sources of the waves (https://en.wikipedia.org/wiki/ Wave_power)

that generating electricity from weaker waves (especially in areas close to the coast) is much easier and more affordable. The designers of these facilities avoid placing them in stormy and turbulent areas as they are afraid of breaking and destroying the reservoirs as a result of the strong waves. Vancouver Renewable Energy Company is one of the pioneers in the field of energy production from sea waves. This company has designed and built a set that works in a different way than Dr. Salter’s method and has recently been tested off the coast of California. This complex consists of several 25-m pipes that are placed vertically between the floor and the sea level and move up and down with each wave, which causes the rotation of turbines and electricity generators installed in the system. Due to the map of distribution of energy sources of the waves, as shown in Fig. 3, in another part of the world, on the coast of Scotland, Aquamarine Power Company has used a different structure to generate electricity from sea waves. Oyster’s wave energy production facilities consist of a series of rotating cylinders that are 12 m high and 18 m wide, which are installed in shallow areas near the shore and move back and forth through wave collision, hence pistons moves and the hydraulic generators rotates. These generators, located on land, can be connected to several cylinders at the same time and produce more than 600 kw of electricity. The first sample of these facilities was tested in the summer of 2008 on the coast of Orkney. Renew Blue Company, which has succeeded in producing energy from tidal waves in a laboratory, decided to implement this project widely in the state of Texas for the first time. According to the Science and Technology center of Saba Information Base, quoted by Fars News Agency, this company as a subsidiary of the Independent Center for Natural Resources of America, managed to generate electricity from the energy of coastal waves in Texas and announced that it has now received a license from the Texas Natural Resources Center, and in cooperation with the Renew Blue company, the center will set up its systems in 18 different coastal points which in addition to electricity production from the waves, the equipment will be used to start this

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project. Toward, it will install its devices at a distance of one mile from the Texas coast. The produced water during this project will be stored in plastic bottles and will be sold. In another part of Scotland, Viogen Company has designed a set of coastal facilities in cooperation with the German company Voss Siemens Hydro. They did this by using an innovative method that transforms the energy of waves landing on the beach into electrical energy. These facilities, called Limp, are made up of a series of rooms that are built near the beach. These chambers have no floor, and sea water can enter them. When big waves move from the sea to the shore, the water enters these chambers with pressure and pushes the air inside them upward. This model of air transfer causes the rotation of sensitive turbines that are installed in the roof of the rooms. After water recedes from the chambers and its drainage, the air that was pushed up moves down again and rotates the turbines again; this process is repeated with every wave entering the beach. The main advantage of this method, as compared to the other methods mentioned before, is that all the facilities are established on dry land, and the security level of the facilities against storms is high. Currently, Limpet has installed three chambers on the coast of Islay, Scotland, with each room capable of producing 300 kilowatts of electricity.

2.4

Wave Energy Potential in Iran

Estimating the energy potential of the wind, waves, and tides in the Persian Gulf, the Caspian Sea, and the Sea of Oman, in light of technological and ecological limitations, is an unanswered problem. Wave potential measurement of an area in order to build a power plant and electricity generation is one of the basic targets. Usually, an investigation of wave energy potential is done by analyzing meteorological information, including wave height and leap. By examining the six areas in the Oman Sea and the Persian Gulf, it is concluded that the highest wave power per latitudinal unit is related to Mahshahr port and the lowest power is related to AbuMusi and Siri islands. Also, the amount of energy in Chabahar region was investigated separately and it was concluded that this region also has a low amount of energy and in general, the Oman Sea due to the windward length and also the continuous blast in some seasons relative to The Persian Gulf has a much higher average monthly energy.

2.5

Wave Formation of Wind

Wave energy is mainly caused by the effect of the wind on the sea surface, and the wind itself is a special form of solar energy conversion, which as a source of clean and renewable energy can play an important role in resolving the ever-increasing energy needs of the world. The rate of this energy transfer depends on the wind speed and the distance which the wind has been in contact with the water surface. Wave

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energy contains potential energy, due to the mass of carried water relative to the average sea level, and kinetic energy, due to the speed of water particles. The potential energy is lost through friction and turbulence with an intensity affected by the characteristics of the waves and the depth of the water. Long waves in deep water lose their energy very slowly, so the wave systems are very complex and often originate from both local winds and storms that happened earlier. Waves are characterized by their height, wavelength, and period. The power of the waves is usually expressed in kW/m which represents the intensity of energy transfer or passing through a 1 m hypothetical line and parallel to the wave front. Wave power plants have a lot of varieties; some are floating on water and others are installed onshore. Today, the technology of energy production from ocean waves is so diverse that many inventions have been patented in this field including three main methods; using a channel in the shape of an incomplete cone, use of the general movement of ocean waves by various mechanisms and the use of an oscillating column of water. In addition to research works, small samples of some wave systems have been built and tested in different parts of the world. Iran also is so potent with a very long coastline (more than 1800 km in the south) and various islands among the countries. Although the abundant use of kinetic energy from sea waves is not currently possible on a large scale, small samples have been used for electricity production.

2.6

Sea Waves Mechanism

The energy of the seas can be exploited from the waves, tides, temperature differences and even the difference in salt concentration in depths of sea. The kinetic energy of waves from blowing and contacting the sea surface transforms to potential energy, and again converts to kinetic energy (wave). The amount of wave energy depends on the relative wind speed to the sea. In other words, the higher the wind speed is, the more the wave energy will be. High energy waves are arisen from strong storms in areas far from the coast and in the sea depth, which slowly lose their energy while moving toward the coast. So near the shore, wave energy comes both from the local winds (near the coast) and the strong storms that happened in the distance recently. These are the types of waves that can be exploited on the sea: 1. Wind waves: the irregular shape of the sea surface is caused by these waves, so different peaks and wavelengths can be observed. In different situations, their shape is usually considered as a set of alternating sin and cos functions and the averaging operation is proposed for them. The best acceptable parameter about these waves is the wave height, which is measured as the average height of one third of the highest waves at a fixed time. The duration of these waves is between 1 and 30 s. Gravitational force is the controlling factor of these waves.

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2. Dead waves: this type observed in the absence of local winds, otherwise there is no source of them. The period of these waves is between 5 and 30 s. The amplitude of these waves is smaller compared to their wavelength. (These waves are the main waves in the sea of Oman.) 3. Capillary waves: these are small turbulent waves that are completely different from those caused by wind. The creating force is wind, but the controlling factor of these waves is the force of surface tension of water. The period of these waves is less than 0.1 s and in this type, the group speed is higher than the solo one. 4. Seiches waves: as a result of the earth’s rotation around its axis, water fluctuates naturally, and when the frequency of these fluctuations is the same as the frequency of atmospheric changes, these waves are created. The period of this type is between 30 s and 5 min. 5. Storm waves: when the wind blows on the sea surface, water is collected on the coast, the sea level increases, and in another place the sea level decreases, which creates these waves. The period of these waves is 30 s to 5 min. 6. Internal waves: these waves are created in the interface of two sea zones with different densities. 7. Tidal waves: the cause of these waves is the gravitational force of the moon, sun, and other planets, and the controlling force of these waves is gravitational force and the Coriolis force. The period of these waves is more than 12 h.

2.7

Wave Energy Harvesting

The idea of using the energy of sea waves has been discussed in last century but so much effort for technology establishment has begun in mid-1970. Since then, various research projects have been done in 13 countries, and machines and plants were built. Wave energy harvesting has experienced various inventions, for example in 1974–1985, more than 2000 devices have been tested just in England. Wave energy is nonpolluting and environmentally friendly. The most risky feature is boat and ship crash with facilities which can be prevented with right navigation and using of signs. The most important devices used for wave energy exploitation are described below: 1. Onshore devices: these systems are all along the coastal line and can extract wave energy. Due to researches, coastal line can produce nearly 200 MW/km electricity with absorption system. 2. Near-shore devices (fixed on the ocean bed): the systems close to the beach are placed 10 to 20 m depth of sea. 3. Offshore devices: the systems far from the beach are usually at a depth more than 40 m (131 ft).

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The technology of wave energy production includes energy harvesting and its conversion into electrical energy through primary and intermediate conversion, energy production and its transmission. Energy converters developed very quickly during recent years of researches. In general, primary converters can be divided into five groups: 1. Oscillating body: this method uses wave energy to move an object and convert its movement into electrical energy. 2. Oscillating water column (OWC): a column of water in a bottomless tube or floating box goes up and down on the sea surface, and this movement produces a flow of air at a high speed, which can drive the turbine. 3. Diaphragm pressure differential: the system uses changes in water pressure to create compressed air inside a submerged system. This pressure can be converted into a stream of air or water and then converted into electrical energy. 4. Wave overflow device: in this method, wave height increases by reducing the depth of the water and the water is pumped up to a higher height. 5. Overtopping device: these systems increase the average level of sea water in specific points and use the methods of funnel-shaped structures and trapping long waves. This division can be considered in terms of installation locations in offshore, near the beach and onshore. These systems may be installed in different ways; in a fixed form on the sea bed, floating on water, submerged under water in offshore areas or on the seabed in shallow coastal areas. They can also be installed completely submerged in the offshore area, in which case the facilities can be extended to the water level. Of course, in practice, except for navigation buoys that use waves to provide their energy, most of the practical examples of these systems are built near the coast. It is possible to collect three to eight times more electrical energy in the offshore area than in coastal areas, but the costs of building offshore facilities and offshore power transmission lines are so high, which makes the project impractical.

2.8

Pros and Cons of WECs

The main advantages of wave energy absorption is described below: 1. 2. 3. 4.

Infinite and a renewable source of energy. No need for large areas, unlike solar and wind facilities and equipment. No pollution generation along wave conversion. Wave energy conversion slows downstream currents and also controls erosion coastal, acts like wave breaker at ports and facilities coastal, creates space for breeding fish and other aquatics. 5. Easy access to marine and offshore platforms.

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And the disadvantages are as follows: 1. 2. 3. 4. 5. 6. 7. 8.

Limited potent areas with long waves Necessity of intense facilities in front of storms Probability of corrosion in the corrosive part of the sea Sediment salt probability on facilities Accumulation of plants and marine creatures on equipment Unusual expenses for equipment repair and maintenance Random behavior of waves Electricity transfer from offshore facilities to the beach

2.9

Wave Energy Converter

Useful energy extraction with any wave energy converter requires applying force to some reaction mechanisms that are capable of resisting the wave generator force. This mechanism transmits the energy between the waves and the converter and finally converts it into a form of useful energy, called a PTO (power take-off) system. The important issue in energy extraction is wave power potential evaluation which in first place is investigated in two areas: 1. Ensuring that the relevant machines can remain intact under difficult sea conditions. 2. Acceptability of the amount of final energy obtained. In order to maximum power extraction of random waves (with no specific function and rule), the PTO system must be capable of restraining force that is proportional to time, along with the reactions of the WEC system. This issue requires real measurement of WEC reactions as well as controlling the PTO system during the wave cycle, in addition, the controller system as well as the PTO system should be adapted with different sea conditions in regard to function improvement in such a way that: 1. Maximize absorbing and receiving power in small seas. 2. Minimizes the risk of damage to devices in large seas. Another solution for obtaining the maximum amount of energy absorption is that when a WEC device is in the opposite direction of the waves, it should react in a way that the driving force and the reaction speed stay in the same phase. However, with a proper design, a WEC device can have a suitable dynamic so that the reaction frequency falls in the correct range and matches the wave driving frequency under most sea conditions. However, active control is needed in order to maximize the absorbed energy from the waves under different sea conditions. Also, the control system can play an important role in characteristic improvement and the survival of the device.

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Wave Energy Plants

Maximum energy of waves can be harnessed in wave energy plants, with converters which consists of different parts such as interface unit. Damping of wave movement in this interface exploits wave energy so that optimal hydro mechanical design is a solution for efficiency improvement. Waves have various features, so that each features is considered for energy harvesting in different converters. Hereupon, these converters can be classified based on interface, energy harvesting method, interfering with wave or type of movement that is going to be absorbed. Hence, interface type is classified into two systems as as follows: 1. Direct wave energy converter (active device): devices that move with wave collision and produce useful mechanical force with a fixed body. 2. Indirect wave energy converter (passive device): devices with a fixed body which harness energy with relative movement of water; however, converters can be floating or fixed in seabeds. In these systems, water fluctuation is used for air fluctuations, and with this variable air current, turbine movement leads to electricity generation (Inanlu & Aegean, 1388). Wave plants can be classified according to wave collision type as follows: 1. Terminator: systems in wave plants that are along or equivalent with wave front which absorb and return them. In this type, most of this converters parallel with each other should be perpendicular to wave current, hence wave collision leads to the most energy conversion. 2. Attenuator: when the converter is in a terminator state and turbulent wave situation, considerable part of energy destroys. In favor of lost energy reduction, device can be parallel with wave direction to lower collision wave interface. Most of plants have joint bodies, like Pelamis with 130 m long that can produce 750 kW energy, it should be noted the best situation for its converter is 5–10 km from the beach (Carter, 2005). 3. Absorbent point (point absorber): these are systems with floating or immersed body which absorbs wave currents in a point. The Pelamis is an attenuating wave energy converter that as point absorber transducers absorb the energy of changes in the sea level by the method of the buoyancy of the floating body (Heave). These devices are made with nominal power between 10 and 35 kW and consist of two parts: buoy and submerged body, energy conversion, storage and control systems are located in their submerged body. The maximum exploited power is obtained at the resonant time (intensification) of heave buoy in these converters. The efficiency and the amount of energy extracted from the wave in the point absorber converters has a direct relationship with the difference in the oscillation amplitude and heave speed between the buoy and the submerged body of this type of converter, the increase of these two differences will increase the absorbed (produced) energy.

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The point-absorbing converter is a special mode of converter placement, which is neither terminating nor reducing. The dimensions of the converter are much smaller than the wavelength, which makes the converter unable to absorb the energy of large waves. Among the point absorbing transducers are Wave Star, Wave Bob and two transducers made in real scale and offshore, Archimedes Wave Swing and Power Buoy. 4. Overtopping converters (OTS): in this converters, wave energy is used for sea water transfer into sloping channel and reserve it in one tank. Using height difference, water turbines with low height moves and energy is produced. Wave Dragon is an example of this converter that is tested offshore and at one-third scale (Cruz & Elkinton, 2009). 5. Oscillating water column converters (OWC): this method as an underwater structure, air above the water surface is falling trapped which drives the turbine. This method has a simple mechanism with 80% efficiency and is resistant to offshore storms (Badi’i, 1371). Limpet and Pico are two onshore converters, while Linux is an oceanic converter near the beach (Cruz & Elkinton, 2009). The Mega Roller concept is based on the existing design implemented as Wave Roller, an oscillating wave surge converter. But also, wave power plants are classified by energy harvesting method as below: 1. 2. 3. 4.

Direct mechanical system Low-pressure hydraulic system High-pressure hydraulic system Pneumatic system

2.11

Different Types of Wave Converters

2.11.1

Introduction to Wave Roller WECs

A Finnish designer named Rauno Koiosari has created rolling wave with moving doors. This roller door weighs 20 tons, and by installing it at a depth of 6–23 m in the sea and in the direction of the waves, it can produce 300 kW energy. AW-Energy Company has now allocated three million euros for the development of a pilot model for these sliding doors in a lake in Portugal, as shown in Fig. 4. The rolling wave is equipped with a hydraulic system that converts the kinetic energy of waves into electrical energy. The basis of this system is a connected plate to the sea floor from its thinner part so that with the wave movement, the hinge installed at the bottom of the plate causes it to move back and forth. The mechanical energy of the plate is transferred to it’s the piston pump and produces electricity through a generator or a turbine-generator. Each of these sliding doors can produce 300 kW energy, and the system power of three rollers is approximately equal to 1 MW. The commercial supply of Wave Roller started in 2013.

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Fig. 4 Wave Roller, an absorber system (https://aw-energy.com/waveroller/)

2.11.2

Pelamis System

This system consists of a semi-submerged joint structure connected by hinges, and the joint movement causes high-pressure oil to be pumped into them. The engine drives a generator to produce electricity, so that a laboratory sample with a 150 m length, 3.5 m diameter and three power conversion sections produce 750 kW power (each section 250 kW), which is presented in Table 1. The Pelamis system is an offshore and suspended wave energy converter that is intermittently connected to the sea floor and consists of a series of cylinders that are hinged together; its preliminary design is presented in Fig. 5. Half of these cylinders’ volume is below the water surface, so by moving adjacent cylindrical parts relative to each other through joints with two degrees of freedom, they produce energy. Both axes with a connection to the hinge, are oblique to the horizon, so that the inclined

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Table 1 Specifications of a 750 kW Pelamis power plant (Banerjee, 2011) Structure Overall length Diameter Displacement Nose Power takeoff Power conversion unit Power takeoff Ram speed Power smoothing/storage Working pressure Power conversion Generator Speed Power Overall power rating Annual output Nominal wave power

150 m 3.5 m 700 tonnes (including ballast) 5 m long, dropped clonical 3 independent power conversion units 4 × hydraulic rams (2 heave, 2 sway) 0–0.1 m/s High-pressure accumulators 100–350 bar 2 × variable displacement motors 2 × 157 kVA/125 kW 1500 rpm 750 kW 2.7 GWh 5.5 kW/m

reaction of the entire network is created by the PTO. In this case, the PTO device resists and reacts to the relative angular movement of the connections (hinges). The inclined reaction produces an effective hydrostatic stiffness lower than a vertical reaction, which causes a natural vibration dependent on the tilt of the axis. Therefore, the machine can be designed in such a way that the resistive reaction is equal to the dominant frequency of the wave (maximum wave frequency) in the area where the machine is installed, thus leading to the PTO device’s required power reduction. The amount of drive energy converted into resistive reaction is controlled by the PTO device. The PTO (power take-off) system includes a set of hydraulic cylinders that pump the desired fluid through connecting pipes to high-pressure accumulators for shortterm storage. Electric motors use high-pressure fluid energy that comes from accumulators and feed the generators that are connected to them. The connected PTO of the Pelamis converter can be examined in two parts, one of which is called the primary power transmission part, and the other is called the secondary power transmission part. The primary power transmission part includes hydraulic cylinders and their control devices that convert wave energy into stored energy. The secondary power transmission part, including hydraulic motors connected to electric generators, converts the stored energy in the tanks into electricity and finally transfers the generated electricity to the beach. This converter enables efficient energy absorption and receiving under different weather conditions of the sea and variable force of waves.

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Fig. 5 A preliminary design of the Pelamis system in the Portuguese Sea (Thomson et al., 2012)

The joint torque of the cylinders must change with each wave cycle so that maximum wave energy is exploited. Momentary joint torque control is achieved using a series of electrically controlled valves, which control fluid flow between hydraulic cylinders, accumulators, and main reservoirs. Conventional hydrostatic power transmission, which uses a pair of variable displacement pumps to deliver variable fluid flow and pressure, typically has a maximum efficiency of about 60%. In these systems, the efficiency drops significantly. In addition, an old and evaluated power transmission system for full wave energy absorption has an uneconomical and low impact activity against low-power and weak waves. On the other hand, in the PTO device, whenever a housing exchanges fluid energy with the accumulators under high-pressure conditions, the only issues that cause efficiency decrease are related to fluid compressibility, device friction and the bending of hinges, and lost flows through pipes and valves. Therefore, a careful

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design can reduce total drops to less than 20% in all working conditions. The primary power transmission part of Pelamis can easily increase energy absorption from sudden waves up to ten times compared to the old system. Also, high efficiency is obtained against weak waves that dominate the sea most of the year.

2.11.3

Wave Dragon System

In this system, ocean waves are raised to a reservoir above the sea level and the output water passes through a number of turbines so that electricity produces. This structure is very simple and the only moving part is the turbine. Its performance would be efficient in the sea, under high forces and sedimentation. In general, this device is a floating wave energy converter system that is based on wave overflow as shown in Fig. 6. This system briefly consists of three parts: 1. The part of wave concentration in one point and wave reflector 2. Equipment for capturing wave crests and directing them to a low-level tank 3. A number of turbines with low head to convert hydraulic head into electricity The main structure consists of reinforced concrete in the lower part and steel shells in the upper part, which are filled with a kind of gas. As seen in the figure, this structure is equipped with arms that concentrate most of the waves coming toward the structure and lead to the main part of the structure. During a storm, the arms of this converter can be closed in regard to protect the equipment and the heart of the system. Many research studies have been done on this system, including linear analysis methods and numerical methods of the wave reflection phenomenon, and even these models have been tested on-site. This system is installed near the coast, at a distance of less than 20 km from the coast, and at a depth between 20 and 30 m.

Fig. 6 Roaring and small waves for Wave Dragon converter in Denmark (Sørensen et al., 2007)

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Wave Converter’s Supply Chain

This industry is still in the precommercial phase, which is demonstrated at TRL (Technology Readiness Level) 7. Wave converters comprise the following: PTO and generator, electrical and automation, bearing, marine operations, hydraulic components, and coating (David, 2017). Various activities are involved in wave energy development due to different life-cycle stages, which can be summarized in five stages: development and planning, manufacturing, installation, operations, and decommissioning (dismantling and recycling). These first two stages include engineering design, environmental surveys and monitoring, professional services, construction activity comprising structure and prime movers, electrical system, foundations, mooring, transmission system, substation, and export cables. The installation stage includes transportation, preassembly work, support structure, mooring system, and commissioning work. The operation stage consists of maintenance and repairs. Decommissioning, as the final stage, includes disassembly, the removal and disposal of main and support structure, environmental work, and recycling material (European Union’s Horizon 2020 Research and Innovation Programme, 2020).

3 Concepts 3.1

Introduction to Life Cycle Costing Assessment

This concept is defined in various terms in different literature. Dhillon introduces the sum of all costs incurred during the life span of an item or system (i.e., the total procurement and ownership costs); the procurement cost is the total of investment or acquisition costs (nonrecurring and recurring), and the ownership cost is the total of all costs other than the procurement cost during the life span of an item. It must be mentioned that each cost model is an approach based on technical parameters, and useful life is the length of time. Life cycle costing fundamental is an approach for estimating the total life cycle cost of equipment procurement. LCC is described in different models, Sherif, Kolarik and Gupta defined in three categories; conceptual, analytical, and heuristic models. There are inputs for these models, which can be described as warranty coverage period, average material cost for failure, cost of training, cost of installation, a system’s or item’s listed price, the cost of carrying spares in inventory, the mean time between failures, the mean time to repair, requirements for spares, the cost of labor per corrective maintenance action, and the time spent for travel. Sensitivity analysis and risk assessments should be done for these inputs to formulate results as LCC analysis (Dhillon, 1947). Likewise, Thomas et al. accomplished a code of practice for environmental LCC that a case study in

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Fig. 7 General scope of the environmental LCC (Hunkeler et al., 2007)

automobile industry considered which costs are isolated across the various life-cycle stages and addressed in fragmented ways (Swarr et al., 2011). Further, David Hunkeler et al. executed a conventional method of life-cycle costing termed as a well-established technique. It goes back to 1933 in United States. That LCC included operation and maintenance costs; in 1970, LCC was used for weapon procurement systems. In Europe, LCC has attracted attention in the public sector since mid-1970s, which thereafter prescribed in building and procurement activities. Costs can be categorized as internal and external which life cycle of products with internal implying as an actor paying for product, summarily it’s connected to business cost. But also, external costs include social and environmental impacts with indirect affect in product life cycle, such as firms, consumers, government as product users. Further, SETAC-Europe group has another category for LCC; conventional, environmental and societal which have different aspects such as cost model, ISO 14040/ 44, system boundaries, also different perspectives have been considered by Rebitzer and Hunkeler in product manufacturer part, product and supply chain integration, consumers which is presented in Fig. 7. It must be noted that LCC planning and analysis are different in definition, whereas environmental consideration is integrated in LCC analysis, LCC planning has more uncertainties due to the lack of existing product in cycle (Hunkeler et al., 2007). Sieglinde K. Fuller et al. define measures for economic performance analysis with LCC method, especially in energy conversion projects that expect to reduce annual energy consumption so that energy prices are converted to future cost equivalent in each year of the service period by price increase for the specified rate type, fuel type and region. In LCC analysis, they performed based on a checklist which all details including present value, saving to investments, discounted payback are considered for a federal office building (Fuller et al., 1995).

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“Cradle-to-Grave” Analysis

Wave energy converters are the most promising devices to harness available wave power but environmental impacts of production process should be assessed. Therefore, it is essential to gain a thorough understanding of wave converter’s life cycle including “cradle-to-grave” steps, in order to make conscious decision for future development of the energy conversion system. Indirect effects on the environment brought on by the production, use, and decommissioning of generators and network infrastructure. The present study’s system boundary will span the whole life cycle, from “cradle to grave” which the device, its moorings, and the subaqueous connecting cable are all included in the physical study, but all downstream electrical components are excluded. So, an LCC analysis seeks to fully account of all project expenditures. This covers expenses incurred by the company, such as engineering fees, contracting, construction, maintenance, rehabilitation, management, salvage, and waste disposal. To make decisions given the complexity of cost factors in wave energy development, sophisticated technologies are needed. It is crucial to take into account a variety of charges that will accrue from project construction through it’s decommission. The LCC Analysis is the method of choice for cradle to grave economic analysis due to its effectiveness in accounting for all these dimensions of choice, summarily, the framework will consist of goal and scope definition, inventory analysis, impact assessment including classification, characterization, normalization, weighing and interpretation, which inventory analysis involves material and manufacture, assembly and installation, operation and maintenance, decommission and disposal (Thomson et al., 2012).

3.3

GHG and EROI

The ongoing effort to reduce greenhouse gas (GHG) emissions and combat climate change has increased the demand for low-carbon energy sources. As a result, new methods for harvesting renewable energy have emerged. Although the energy sources themselves are “carbon-free,” the process of turning the energy into electrical power has larger environmental effects. Therefore, it is essential to develop a thorough understanding of the life-cycle environmental impacts that result indirectly from power generation due to the manufacture, use, and decommissioning of generators and network infrastructure in order to make informed decisions for the future development of the energy system (Thomson et al., 2012). While wave energy sources have inherently low impact, energy is consumed and pollutants are emitted during the production, operation, and decommissioning of energy converters. This has raised concerns about whether these new technologies will result in a net decrease in greenhouse gas (GHG) emissions and a workable energy return on investment. (EROI). There have been a few studies using the lifecycle assessment (LCA) technique as a result of the need to determine the life-cycle

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GHG emissions and energy consumption of the converters in order to respond to this. (Douglas et al., 2008; Parker et al., 2007; Sørensen & Naef, 2008; Uihlein, 2016). However, it is challenging to confirm findings or make firm decisions regarding the effects of wave energy due to the dearth of research that have been conducted and the wide range in design of marine energy converters. Additionally, all but one of the current studies only include GHG emissions and embodied energy, and they can ignore possible conflicts or side effects of environmental effects according to WRI and WBCSD 2011 (Camilla Thomson et al., 2019).

3.4

LCC Based on WEC Mechanism

The study will aid in understanding variances between wave and tidal devices as well as variations between ocean energy device types with regard to environmental implications. It will also help identify the most significant life-cycle stages of ocean energy devices, according to the difference between wave and tidal devices. Ocean energy device manufacturing, use, maintenance, and decommissioning will all have an impact on the environment in different ways. Howbeit, only a small number of life-cycle analyses (LCAs) of specific wave and tidal energy converters have been carried out thus far, with a primary focus on technologies in an advanced level of development, according to Magagna and Uihlein (2015). The majority of studies (such as Parker et al., 2007; Douglas et al., 2008; Walker & Howell, 2011) have solely examined the impact of the categories of energy and carbon. Uihlein and Magagna (2015) assert that more LCA studies are required to generate more estimates for all ocean energy technologies because there is a dearth of high-quality studies, particularly for tidal current, ocean thermal energy, and salinity gradient devices, which can be seen in the flowchart of Fig. 8. There are no LCA studies at all for some of the wave energy devices, including point absorbers and attenuators (the most used types) (Uihlein, 2016). It is anticipated that wave energy will aid in the development of carbon-free electricity generation. Theoretically, the oceans’ wave energy potential is estimated to reach 1–10 TW, which can meet the world’s current energy needs. Research societies have demonstrated that it is feasible to harvest electricity from ocean waves due to its enormous potential. Similar to offshore wind turbines, wave energy converters (WEC) are intended to be installed in an array made up of several converters. The earliest iterations of the mathematical modeling of hydrodynamic forces on submerged buoys are still in the research phase. However, it has undergone considerable development. The focus of the subsequent research phase was on improving the design and power take off (PTO) system of a single buoy. Other experiments introduced the concept of WEC array. Another study focused on employing numerical, parametric, or optimization-based algorithms to determine the ideal value for WEC’s array parameters (such as the ideal location or arrangement). Because hydrodynamic interactions between them can be beneficial or harmful, the distribution of converter positions within an array has a direct impact on the array’s performance. The array’s

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Fig. 8 Flowchart for a typical wave energy converter’s life cycle (Uihlein, 2016)

configuration affects these interactions. In order to use these interactions to strengthen the overall power output, this is the major motivation behind their investigation. This array’s configuration has been the subject of several pertinent publications by various R&D units around Europe in the past by the pioneering researches, and it continues to be an exciting study area as seen by the recent publication of multiple studies. Additionally, researchers have looked at the identification of decarbonization pathways and sustainability transitions that are technologically and economically possible. Some of the related research projects that took into account the performance of arrays or the distance between the converters were carried out, and the influence of the interactions between WECs and the effects of nonlinear mooring forces using timedomain analysis was also investigated. A quick review of some current research on the various features of WECs, such as layouts, PTO, and design optimization, is included in novel research studies. While some of the aforementioned studies employed hindcast wave models, this study took into account various layout arrangements in relation to real wave conditions (Amini et al., 2020).

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There are various wave energy converters being developed by the Joint Research Centre (JRC) which ocean energy database of the European Commission presently has information on over 100 distinct designs. According to Uihlein (2016), the The Intergovernmental Panel on Climate Change recommends three oscillating body systems, oscillating water columns and overtopping devices, while the JRC recommends eight, subdividing oscillating body systems into attenuator, point absorber, oscillating wave surge, pressure differential, and rotating mass systems, as well as taking new devices into account to better reflect more recent developments in this field. These devices’ mounting options – whether on land, in water, or on the seabed – can add even more variety to the designs (Camilla Thomson et al., 2019). Summarily, not only the material but also the design can play a significant role in cost estimations and, as a result, in LCC analysis.

3.5 3.5.1

Practices Introduction to Mega Roller LCC

The Wave Roller, an oscillating wave surge converter (OWSC), is the design upon which the Mega Roller is one of this type. In order to absorb wave energy through the horizontal movement of the prime mover, this kind of wave converter technology, which is presented in Fig. 9, employs bottom-hinged plates that oscillate in

Fig. 9 Schematic of the Mega Roller system (Apolonia et al., 2021)

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Fig. 10 Flowchart for Mega Roller’s life cycle costing (Apolonia et al., 2021)

pitch in response to the surge movement of the water flow in the nearshore zone (10–25 m sea depth). The panel of the device, which is situated 400 m from the shore at a depth of 8–20 m, is moved by the wave surge. The apparatus is fixed to the seabed and is partially or completely submerged, depending on the tidal conditions. The Mega Roller project established a number of critical variables that were important for the initial design of the device and for the current LCA study (Apolonia et al., 2021). The wave energy converter, Mega Roller was established at Peniche, Portugal primary to investigate its environmental effects that are caused by the usage and production of materials, mostly steel. The flowchart in Fig. 10 presents Mega

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Roller’s LCC in detail. Quantities of materials used in the LCA study were varieties of steel, especially wire that will substitute stainless steel chains, according to AW Energy research. As mentioned earlier, the array design play a significant role in the environmental implications reduction, associated with each kWh of power produced through this innovation, as doing so will lower the material intensity of some components (such as foundation systems) (Apolonia et al., 2021).

3.5.2

Introduction to Pelamis LCC

As it draws energy from the oscillation caused by the wave motion on various tube sections, the Pelamis WEC is an illustration of a floating oscillating body system of the attenuator type which the components are presented in Fig. 11. It is an offshore, semi-submerged, “snake-like” device created by Pelamis Wave Power Ltd. (PWP), which at first seemed to be among the most promising ones in the marine energy field (Camilla Thomson et al., 2019). This offshore device’s P1 type was successfully installed in 2008 in the world’s first wave farm at Aguaçadoura, off the coast of Portugal. The test data were immediately used to design the second-generation P2 device, which is currently being tested at the European Maritime Energy Centre. Leasing agreements comprising roughly 70 devices have been made for two farms off the coast of Scotland, and more projects are now in the planning phases (Thomson et al., 2012). Due to the supply chain in Fig. 12 and in order to GHG & EROI estimation related to Pelamis, LCC analysis has been performed mainly by Parker et al. which in material and manufacture part, this device is cut, welded, sand blasted and painted, it should be mentioned that many pre-fabricated components are used beside steel. Data in assembly and installation including panel and structure, operation and maintenance by an onshore computer and decommissioning and disposal to unlatch the Pelamis and its recovering are approximated by scaling ecoinvent based on 1 kWh output power. Other important points should be considered; 420 km distant, 24 h at 7 knots, 45% capacity factor, made in the UK based on British grid, the entire

Fig. 11 Subcomponents of the Pelamis device (Thomson et al., 2012)

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Fig. 12 Pelamis’s life cycle (Thomson et al., 2012)

LCA P1 Pelamis WEC, which is situated off the northwest coast of Scotland, includes 10% of the overall cost. In six areas of environmental effect, the Pelamis was shown to have much fewer environmental impacts than fossil fuel production, while in eight out of the remaining 13 categories, it outperformed than other methods of energy production. Amongst all, Steel production and maritime vessel operations had the biggest effects. The system works admirably in the two most typically evaluated impacts for renewable energy converters: cumulative energy demand and climate change. Depending on the emissions intensity of the mix of displaced generation, the carbon payback period is predicted to 24 months, and the energy return on investment is 7.5. The constraints of focusing just on carbon and energy intensity are illustrated by the difference between this and the subpar performance in other impact areas (Thomson et al., 2012). The mentioned P1 model was successfully installed as the first WEC at a commercial scale in 2008 in Aguaçadoura, Portugal (Aquaret, 2008) which Parker et al. (2007) and Walker and Howell (2011) found that the carbon footprint and embodied energy for the Pelamis and Oyster were similar (23 g CO2/kWh and 293 kJ/kWh for the Pelamis and 25 g CO2/kWh and 236 kJ/kWh for the Oyster) but Uihlein (2016) estimated the potential climate change impacts for these types of device to be higher, at 44 and 64 g CO2 eq/kWh for an attenuator or oscillating wave surge device, respectively. The precise circumstances taken into account might be the cause of this mismatch, although there are some parallels between the Pelamis and Oyster devices since they are both massive, steel structures with hydraulic power take-off systems. The Wave Dragon, which is primarily made of concrete, has a climate change impact of just 13 g CO2/kWh, according to Sørensen and Naef (2008). It is difficult to draw conclusions about the environmental implications of the industry from the few studies that have already been

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conducted because of the wide range of designs for maritime energy converters (Camilla Thomson et al., 2019).

3.5.3

Wave Dragon’s LCC

The Wave Dragon is a floating wave energy converter that primarily draws power from waves that overtop a reservoir. Between 2003 and 2005, a 1:4.5 scale prototype was tested over 21 months in comparable sea conditions at a less active location. Black and Veatch reviewed the entire concept at the beginning of 2005, including a first attempt to offer an “embedded carbon assessment,” and in the autumn of that same year, a comprehensive LCA was carried out at the Technical University of Denmark. Both research projects rely on a labor-intensive procedure for modeling the Wave Dragon’s life cycle and for gathering the necessary data. It may be carried out using the GABI software, which is arranged into “plans” and “processes.” The LCA evaluation employs normalization and weighting in accordance with the EDIP approach. In order to make the evaluations comparable, the functional unit is 1kWh, much like in previous LCAs for power plants. The Danish power grid mix by consumption, 2001 process serves as the foundation for all electrical energy utilized and generated (Sørensen et al., 2007). Consequently, it is feasible to create a different balance for each principal fuel type (or to some extent CO2). In order to account for the energy loss during the conversion process in power plants, a more straightforward method is to only multiply the examined power production by a factor of 2.5 (assuming the average degree of efficiency for avoided production is 40%). This makes sense as long as the generation of wave power is minimal, compared to the generation of fossil fuels, without the use of heat. If this is done, the energy return for the Wave Dragon would increase from 20 to 50 times in just 1 year (Sørensen et al., 2007). The Wave Dragon technology has been shown to be scalable up from tank testing at a size of 1:50 to actual sea testing on a prototype at a scale of 1:4.5 by SPOK Spn program (2002–2006). Scaling up to a 1:1 scale precommercial prototype is believed to be feasible. Grid connection has been proven to be feasible, and the Wave Dragon, which has been supplying electricity to the grid since June 2003, is considered to be the world’s first offshore wave power device. A 1:1 prototype’s design and operation and maintenance will benefit from a plethora of crucial knowledge that has been accumulated during more than 20,000 h of power output (SPOK ApS Company et al., n.d.). The Wave Dragon technology has been shown to be scalable up from tank testing at a size of 1:50 to the actual sea testing on a prototype at a scale of 1:4.5. Given that no unexpected events have been noted and the model laws in issue have been evaluated and have been shown to be reliable, scaling up to a 1:1 scale precommercial prototype is believed to be feasible. Grid connection has been proven to be feasible, and the Wave Dragon, which has been supplying electricity to the grid

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since June 2003, is considered to be the world’s first offshore wave power device. A 1:1 prototype’s design and operation and maintenance will benefit from a plethora of crucial knowledge that has been accumulated during more than 20,000 h of power output. In a wave climate with 36 kW/m speed, a production price of 0.11–0.04 EUR/kWh (short term/long term) based on a full-scale 7 MW Wave Dragon has been established. Since the system’s viability and dependability have been established, a large-scale offshore demonstration project may now be started (SPOK ApS Company et al., n.d.).

4 Case Study for WEC LCC As mentioned in practices, three WEC have been chosen so that complete researches and LCC analyses were performed as below: 1. Mega roller by Walker and Howell and... 2. Pelamis by Parker et al. and also Thomson et al. and... 3. Wave Dragon by Hans. Chr Sørensen et al. and... One of the most complete analyses of Wave Dragon’s LCC was performed by Aalborg University in a 1:4.5 scale between 2005 and 2007, in Northern Denmark. This 7 MW prototype was the largest WEC in the world which produces enough electricity for 2600 Ω and was made of the flow incoming into the reservoir. The power production in the form of power matrix depends on wave state parameters, including wave height and peak period. In this model, seven turbines are equipped with generators and three dummy turbines with 40–80% efficiency, comparing to full size one with 16–20 turbines with increasing efficiency up to 80–95%. One of the key components is reflector’s design to prevent extreme motions and increased overtopping so that is made in two sections. The challenges here are measuring the waves approaching the ramp, accurate transform to control system, new control strategies which resolved and the performance improved to 10%. This device consists of 4–11 MW units of concrete buckets filled with ballast rocks, approximately 30,000 tonnes, 300 m length and 170 m width. As mentioned earlier, a life cycle analysis of this Wave Dragon was performed by SPOK ApS (SPOK ApS Company et al., n.d.); based on the flowchart in Fig. 13, the energy payback is 29 months and its lifetime is 50 years. The main goal is to investigate LCC results’ variations due to input’s propagation, due to cradle to grave life cycle i.e., processing, manufacturing, assembly, installation, operation and maintenance, decommissioning. Also, subsystems are included; either structure, mooring, generator, rope and electrical and mechanical components or maintenance tools; cables, etc. After resource quantification, energy intensity for material manufacturing including welding, casting, painting, etc. was investigated so that infrastructure and equipment for system manufacturing and installation consisting main platform, hydro turbines, mooring and cable was calculated. Next step was considering operation, maintenance especially for corrosion and

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Fig. 13 Flowchart of Wave Dragon’s LCC (Sørensen et al., 2007)

decommission of these material, hence the value of most serious avoided impacts, global warming, human toxicity soil, bulk waste and acidification were normalized and weighted over the entire life cycle. Based on weighted resource consumptions, zinc, tin, nickel, natural gas, manganese, lignite, iron, hard coal, crude oil, copper and aluminum analyzed; tin with the most person reserve index was a known global reserve around 1.1 kg/person, mainly because of recycling bronze. Recycling of waste material has a significant influence on the environmental impact of a device which avoids primary material production. The emission data for one kWha electricity by parameters as follows; CO2 = 1.32E-02, CH4 = 2.96E-05, NO2 = 4.89E05, NMVOC = 1.04E-05, SO2 = 6.39E-05, PM 2.5 = 1.45E-05, PM10 = 3.57E-05 and occupation, built up area including mineral extraction and dump sites = 6.38E04 m2a (Sørensen et al., 2007).

5 LCC Assessments In conclusion, the main environmental impacts of ocean energy devices from an LCA perspective are due to material use, while installation, maintenance, and operation do not show significant impacts. It can be concurred, all prior studies disclose detailed information on the spread of impacts across life cycle phases

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Table 2 Life cycle costing assessment for various WECs by Uihlein1 (Uihlein, 2016) Device type Attenuator

Oscillating wave surge Point absorber Horizontal axis turbine

Impact category Global warming

Unit g CO2-eq./kWWh

Ozone depletion Freshwater eutroph. Marine eutroph. Global warming Global warming Ozone depletion Global warming

g CFC-11 eq./kWWh mg P eq./kWWh

Literature 22.8a– 29.8a 2.3b 9.84b

mg N eq./kWWh g CO2-eq./kWWh g CO2-eq./kWWh g CFC-11 eq./kWWh g CO2-eq./kWWh

21.0b 25c 39–126d 1.48–4.58d 15–20e

This study 43.7 1.8 0.16 10.0 64 104.5 4.2 23.1

a

Parker et al. (2007) b Thomson et al. (2011) c Walker and Howell (2011) d Dahlestan (2009) e Douglas et al. (2008)

(e.g. Walker & Howell, 2011). The scenario models have showed that there is still chance to significantly decrease the environmental effects of ocean energy devices due to various devices which are compared in Table 2. In order to ocean energy technology progress and further costs reduction, developers are already concentrating on improvements such as greater efficiency, durability and dependability as well as better anchoring systems (Magagna & Uihlein, 2015). Moving equipment to offshore places with more resources is one method to boost efficiency and lessen environmental effects. (e.g. higher wave energy). The requirement of larger cable lengths, however, may outweigh any environmental advantages; thus, this alternative has to be carefully considered. Ocean energy technology will eventually be put in arrays and perhaps ocean energy farms. Since some components (such as cable, electrical hubs, and substations) may be shared, the environmental consequences per kilowatt hour of power produced will unquestionably decrease. Thus, a broad range of ocean energy technologies should be the subject of future LCAs. Studies that take into consideration the swings in power output would also be highly helpful for measuring the environmental advantages of ocean energy because ocean energy supplies are changeable (but fairly predictable, such as in the case of tidal currents) (Uihlein, 2016). One of the main tools for assessment is SimaPro, an LCA software program used to model the system, with LCI data taken from the ecoinvent database (mostly version 3.4). SimaPro is a tool intended to gather, examine, and keep track of information on the sustainability performance of goods and services. Key performance indicators, carbon and water footprint calculations, product design, making environmental product declarations, and sustainability reporting are just a few uses for the program, especially carbon and energy intensity that are compared in Table 3. The LCI database ecoinvent has more than 16,000 distinct datasets that span a wide

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Table 3 Carbon and energy intensity for various ocean energy converters by Maria Apolonia (Apolonia et al., 2021) Device WEC Mega Roller Oyster Pelamis Wave dragon Wavestar Point absorber BRD WEC OBREC WEC Offshore wind turbine

Carbon intensity (g CO2 eq/kWh) 33.8 25.0 35.0 13.0 47.0 39.0–126.0 89.0 37.0 11.0

Energy intensity (kJ/kWh) 432.0 236.0 493.0 – 536.0 – 387.0 – –

References [31] (https://ecoinvent.org/) [34] [32] Thomson et al. (2011) [35] [36] Apolonia et al. (2021)

range of goods, services, and operations, from construction materials to food, from resource extraction to waste management (Apolonia et al., 2021). What is expected from LCC assessment was performed by Thomson, RC et al. for Pelamis WEC with carbon and energy audit to make a better decision in comparison to Parker et al. research. The recent LCC by Parker determined that Pelamis P1 had a carbon and energy intensity of 23 gCO2/kWh and 293 kJ/kWh, respectively. The research’s fundamental assumptions were in line with this research, which predicted that a typical site off the coast of Scotland would produce 2.97 GWh annually over the course of its 20-year design life and that its major and minor components would be produced in the UK at a distance of 200 miles from a commercial port. In order to maximize recyclability at the end of life, a mass balanced analysis was conducted using SimaPro and data from Europe (Ecoinvent, 2010) based on Parker’s records. In the first scenario, 90% of key metals were recycled, and the other went to the landfill. In the second scenario, a 50:50 method was used, where half of the interest was allocated to each product to maximize recyclability. This extensive LCC evaluation leads to increased embodied energy with a 50:50 recycling process (Thomson et al., 2011). (a) And eventually about Simapro, its structure consists of characterization, damage assessment, normalization, weighting and impact assessment, also its based on CML IA, environmental prices, ecological scarcity, EF method, A2 method, EPD, EPS 2015d method. Classification and characterization that are used as indicators at “mid-point level” (problem oriented approach)” are: 1. 2. 3. 4. 5. 6.

Depletion of abiotic resources Climate change Stratospheric ozone depletion Human toxicity Freshwater aquatic ecotoxicity Marine ecotoxicity

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Fig. 14 Simapro process network (https://simapro.com/)

7. 8. 9. 10.

Terrestrial ecotoxicity Photo-oxidant formation Acidification Eutrophication

And also Impact categories available in Simapro and presented in Fig. 14 are: • • • • • • • • • • • • • •

Global warming (different time frames) Upper limit of net global warming Lower limit of net global warming Ozone layer depletion (different time frames) Human toxicity (different time frames) Freshwater aquatic ecotoxicity (different time frames) Marine aquatic ecotoxicity (different time frames) Terrestrial ecotoxicity (different time frames) Marine sediment ecotoxicity (different time frames) Average European (kg NOx-eq); average European (kg SO2-eq) Land competition Ionizing radiation Photochemical oxidation; photochemical oxidation (low NOx) Malodorous air

Life-Cycle Cost Assessment of Various Wave Energy Converters. . .

• • • •

149

Equal benefit incremental reactivity Max. incremental reactivity Max. ozone incremental reactivity Ecoinvent

(b) A nonprofit organization called ecoinvent is situated in Zurich, Switzerland, and is committed to making high-quality data accessible for sustainability evaluations all across the world. The LCI database, ecoinvent, has more than 16,000 unique datasets that span a wide range of items, services, and operations, from construction materials to food items, from resource extraction to waste management. Since this dataset is one of the most complete sources of cradle-to-gate resource use and emissions data for materials, transport, and other processes in Europe, life-cycle inventory data is primarily sourced from the ecoinvent database, which is maintained by the Swiss Centre for Life Cycle Inventories (https://ecoinvent.org/). The ecoinvent database includes uncertainty ranges for all background data, which are often described as a lognormal distribution where the 95% confidence interval is covered by the square of the geometric standard deviation. In Section S1 of the Electronic Supplementary Material, a complete inventory of all the ecoinvent processes and materials is presented, which was used in the Thomson research (Camilla Thomson et al., 2019). (c) The research studies used the Monte Carlo method to investigate the mentioned uncertainty caused by poor data quality (Camilla Thomson et al., 2019). Monte Carlo has been generally acknowledged as an effective strategy for addressing LCA uncertainties and has been used in several case studies, such as LCA of a car’s front panel (Hong et al., 2010) and LCA of agricultural production (Niero et al., 2015). An MC uncertainty analysis typically includes the following sequential processes: (a) selecting the probability distributions of the input data, (b) selecting the mathematical expression of the outputs as functions of the inputs, (c) selecting the mean and standard deviations of the outputs and then producing the discrete probability distribution functions and the confidence intervals (e.g., 95%) of the outputs, and (d) selecting the mean and standard deviations of the outputs (Zhai et al., 2021).

References Amini, E., et al. (2020). A parametric study of wave energy converter layouts in real wave models. Energies, 13, 23p. Apolonia, M., et al. (2021). Life cycle assessment of an oscillating wave surge energy converter. Journal of Marine Science and Engineering, 9, 206. Aquaret. (2008). Case Study-Okeanos Pelamis Wave Farm. http://www.aquaret.com/images/ stories/aquaret/pdf/cswavepelamis.pdf. Accessed June 2018.

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Aubry, J., Ahmed, H. B., Multon, B., Babarit, A., & Clément, A. H. (2011). Wave energy converters. In Marine renewable energy handbook. https://doi.org/10.1002/9781118603185. ch11.Hal-01156751 Banerjee, S. (2011). Ocean energy assessment: An integrated methodology (Doctor of philosophy). Coventry University. https://pureportal.coventry.ac.uk/en/studentthesis/ocean-energy-assess ment-an-integrated-methodology(227fb061-69df-4f68-b522-fd2ad52ac2cb).html Camilla Thomson, R., et al. (2019). An LCA of the Pelamis wave energy converter. The International Journal of Life Cycle Assessment, 24, 51–63. Carter, R. W., (2005), Wave energy converters and a submerged horizontal plate (Doctoral dissertation, University of Hawaii at Manoa). Chr, H., Sørensen Stefan, N., Stefan, A., Hauschild, M. Z. (2007). Life cycle assessment of the wave energy converter: Wave Dragon. In Proceedings of the Poster session presented at Conference in Bremerhaven, Bremerhaven, Germany, 26–27 June 2007. Cruz, J., & Elkinton, C. (2009). Oregon Wave Energy. Trust Utility Market Initiative. Oregan, USA. Dahlsten, H. (2009a). Life cycle assessment of electricity from wave power. Master’s Thesis, Swedish University of Agricultural Sciences, Sweden. http://www.divaportal.org/smash/get/ diva2:460946/FULLTEXT01.pdf. Accessed on 19 July 2020. Dahlsten, H. (2009b). Life cycle assessment of electricity from wave power. Uppsala, Sweden; Available online: https://stud.epsilon.slu.se/5364/1/dahlsten_h_130321.pdf. Accessed on 17 Feb 2021. Dalton, G., Madden, D., Clare Daly, M. (2014). Life cycle assessment of the Wavestar. In Proceedings of the 2014 Ninth International Conference on Ecological Vehicles and Renewable Energies (EVER), Monte-Carlo, Monaco, 25–27 March 2014. David, M. et al. (2017). Supply chain of renewable energy technologies in Europe: An analysis for wind, geothermal and ocean energy (50p.) (Science for policy report). The Joint Research Centre (JRC). Dhillon, B. S. (1947). Life cycle costing for engineers (224p). CPC Press Taylor and Francis Group. Douglas, C. A., Harrison, G. P., & Chick, J. P. (2008). Life cycle assessment of the Seagen marine current turbine. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 222(1), 1–12. https://doi.org/10.1243/ 14750902JEME94 https://ecoinvent.org/the-ecoinvent-database/data-releases/ecoinvent-version-2/ European Union’s Horizon 2020 Research and Innovation Programme. (2020). Advanced design tools for ocean energy systems innovation, development and deployment. https://www. dtoceanplus.eu/content/download/6213/file/DTOceanPlu_D8.2_Analysis_of_the_European_ Supply_Chain_EDP_20200729_v1.0 Fuller, S. K., et al. (1995). LIFE-cycle costing manual for the Federal Energy Management Program (228p.). NIST Publication. Hong, J., Shaked, S., Rosenbaum, R., & Jolliet, O. (2010). Analytical uncertainty propagation in life cycle inventory and impact assessment: Application to an automobile front panel. The International Journal of Life Cycle Assessment, 15, 499–510. https://doi.org/10.1007/s11367010-0175-4 Hunkeler, D., et al. (2007). Environmental life cycle costing (61p.). CPC Press Taylor and Francis Group, SETAC. National Renewable Energy Laboratory (NREL). (2021). Renewables 2021 global status report. https://www.ren21.net/wp-content/uploads/2019/05/GSR2021_Full_Report.pdf Niero, M., Ingvordsen, C., Peltonen-Sainio, P., Jalli, M., Lyngkjær, M., Hauschild, M., & Jørgensen, R. (2015). Eco-efficient production of spring barley in a changed climate: A life cycle assessment including primary data from future climate scenarios. Agricultural Systems, 136. https://doi.org/10.1016/j.agsy.2015.02.007

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Parker, R. P. M., Harrison, G. P., & Chick, J. P. (2007). Energy and carbon audit of an offshore wave energy converter. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 221(8), 1119–1130. https://doi.org/10.1243/09576509JPE483 Patrizi, N., Pulselli, R. M., Neri, E., Niccolucci, V., Vicinanza, D., Contestabile, P., & Bastianoni, S. (2019). Lifecycle environmental impact assessment of an overtopping wave energy converter embedded in breakwater systems. Frontiers in Energy Research, 7, 32. Available online: https:// www.frontiersin.org/article/10.3389/fenrg.2019.00032/full. Accessed on 6 Jan 2021. [CrossRef] Sorensen, C. H., & Naef, S. (2008). Report on Technical Specification of Reference Technologies (Wave and Tidal Power Plant). New Energy Externalities Developments for Susrainability, NEEDS. Sørensen, H. C., Stefan, N., Stefan, A., Hauschild, M. Z. (2007) Life cycle assessment of the wave energy converter: Wave dragon, Wave Dragon ApSUniversity of Copenhagen. SPOK ApS Company, et al. Sea testing and optimisation of power production on a Scale 1:4.5 test rig of the offshore wave energy converter wave dragon. European Commission. http://www. spok.dk/consult/wavedragon_e.shtml Swarr, T. E., et al. (2011). Environmental life-cycle costing: A code of practice. The International Journal of Life Cycle Assessment, 16, 389–391. Thomson, R. C., et al. (2011). Life cycle assessment in the marine renewable energy sector (pp. 120–125). Edinburgh Research Explorer, American Center for Life Cycle Assessment. https://doi.org/10.1007/s11367-011-0287-5 Thomson, R. C., et al. (2012). Full life cycle assessment of a wave energy converter. IET renewable power generation conference, Edinburgh, 7p. Thomson, R. C., Chick, J. P., & Harrison, G. P. (2019). An LCA of the Pelamis wave energy converter. International Journal of Life Cycle Assessment, 24, 51–63. Available online: http:// link.springer.com/10.1007/s11367-018-1504-2. Accessed on 3 Jan 2021. [CrossRef] [PubMed] Uihlein, A. (2016). Life cycle assessment of ocean energy technologies. International Journal of Life Cycle Assessment, 21, 1425–1437. Uihlein, A., & Magagna, D. (2016). Wave and tidal current energy – A review of the current state of research beyond technology. Renewable and Sustainable Energy Reviews, 58, 1070–1081. https://doi.org/10.1016/j.rser.2015.12.284 Walker, S., & Howell, R. (2011). Life cycle comparison of a wave and tidal energy device. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 225, 325–337. Available online: http://journals.sagepub.com/ doi/10.1177/1475090211418892. Accessed on 5 Jan 2021. [CrossRef] Walker, S., Howell, R., Hodgson, P. & Griffin, A. (2013). Tidal energy machines : A comparative life cycle assessment study. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 229. https://doi.org/10.1177/ 1475090213506184 Zhai, Q., Zhu, L., & Lu, S. (2018). Life cycle assessment of a Buoy-Rope-Drum wave energy converter. Energies, 11, 2432. Available online: http://www.mdpi.com/1996-1073/11/9/2432. Accessed on 6 Jan 2021. [CrossRef] Zhai, Q., et al. (2021). Life cycle assessment of a wave energy converter: Uncertainties and sensitivities. Journal of Cleaner Production, 20, 14p.

Towards Optimising the Life Cycle Costs of High-Rise Residential Apartments: A Case of Sri Lanka Kajavathani Panchalingam

and Thanuja Ramachandra

1 Introduction As suggested by Janjua et al. (2019), having a long-term perspective and understanding the significant effects of early design decisions on a building’s life are essential for being environmentally responsible. To operate more efficiently, the environment must balance structural building systems, long-term building functions, and economic, environmental, and social constraints (Abou Hamad & Abu-Hamd, 2019). Using life cycle costing (LCC), one may evaluate the relative costeffectiveness and cost-competitiveness of various environmentally friendly options (Goh & Sun, 2016). EN 15643-4:2012 suggests conducting an assessment as early as possible during the conceptual phases of a construction or refurbishment project, such as the sketch plan stage, to provide a broad estimate of the environmental, social, and economic performance. The assessment may be reviewed and updated on a regular basis to aid decision-making as the project progresses, and a final evaluation (as built) should be performed, with the results used to inform all stakeholders involved. LCC variables are classified by the International Organisation for Standardisation (BS-ISO 15686-5, 2006; BS-ISO 15686-5, 2017) as construction, operation, maintenance, management, disposal costs, residual values, and other expenses, including discount rate, taxes, inflation, and subsidies. Running costs (RCs) are the two basic cost components, including operation and occupancy expenses and maintenance costs. Despite a major focus on beginning costs, the RC of most structures surpasses 50% of the entire LCC (Alqahtani & Whyte, 2016a). According to Haugbolle and Raffnsoe (2019), initial construction costs and RCs should be identical in the long

K. Panchalingam · T. Ramachandra (✉) Present Address: Department of Building Economics, University of Moratuwa, Moratuwa, Sri Lanka e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. S. Muthu (ed.), Life Cycle Costing, Environmental Footprints and Eco-design of Products and Processes, https://doi.org/10.1007/978-3-031-40993-6_6

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term. An LCC approach, for instance, can reduce energy consumption by up to 33%, which far exceeds any premium spent over the design and phase of construction (Dwaikat & Ali, 2016). Weerasinghe and Ramachandra (2018) report that there is evidence to imply that myopia exists in investment decisions, with estimates of the initial construction cost taking precedence over operation and maintenance costs over the life of buildings. By 2017, Oxford Business Group indicated that Sri Lanka, one of Asia’s fastestgrowing economies, and its total developed floor area continually expanded due to new developments. The increase in high-end dwellings, housing developments, workplaces, colleges, universities, hotels, and hospitals is primarily attributable to rapid economic development, which is linked to urbanisation, a rise in demand for housing from the country’s wealthy and middle-class populations, a rise in demand for office and business premises from local and foreign corporations, and a rise in tourist interest in the country. In Sri Lanka, particularly in the capital city of Colombo, the number of skyscrapers is increasing (Kumar et al., 2020). Many high-rise luxury condominiums with a combined height of more than 36 m have been constructed in Sri Lanka in recent years, and many more will be built in the future (Madhushani et al., 2019). Over the past decade, residential high-rises have dominated the Colombo skyline (Jayalath, 2016). Sustainability, which remains a major concern in the construction industry, requires that residential buildings be constructed to higher standards of excellence, accessibility, energy conservation, and the provision of amenities that encourage active and social activities in a safe residential setting. Early prediction of RC can facilitate cost-related decisions and reduce future expenditures (Chan et al., 2003). According to Nyayemi (2013), based on the function of the structures, RC patterns vary. They account for over 70% of total LCC expenditures (Lai & Yik, 2008; Goh & Sun, 2016), particularly in commercial structures. According to Wang et al. (2014), residential structures were constructed next to commercial buildings, with operation costs accounting for over 69% of total LCC. On a slightly different topic, Alqahtani and Whyte (2016b) found that commercial buildings absorb 60–74% of their LCC, while residential buildings absorb 40–55%. Moreover, operation and maintenance costs account for 75% and 25% of the RC of office buildings in Sri Lanka, respectively (Weerasighe et al., 2016). The LCC method provides essential criteria for assessing the financial viability of a construction project (Shen et al., 2007). Numerous cost inputs are frequently required to calculate the costs at distinct phases of a project’s life cycle. Daily building activities, equipment, and systems require operation and maintenance to perform as intended (Ihsan & Alshibani, 2018). The quality of operation and maintenance ensures that the system operates with minimal interruptions (Lai & Yik, 2011). Nevertheless, operating expenses, such as housekeeping and utility costs, are frequently excluded from LCC estimates (Bourke, 2016). The building’s RC has a significant impact on the budgets of its occupants. Throughout the building’s service life, RC should be continuously monitored and optimised (Joseph & Tretsakova-McNally, 2010). Although clients must be informed of their future financial obligations and cost implications as the planning, construction, and use phases progress, the existence of multiple cost data collection

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systems, as well as various types of devices, equipment, and systems, hinders the development of a simplified model for estimating RCs at the initial stage (Krsti & Marenjak, 2012). All of these factors influence the building expenses of houses (Ashworth & Perera, 2015; Belniak et al., 2013), so they can be used to predict RC during the design phase (Belniak et al., 2013; Ashworth & Perera, 2015). More information must be compiled on the design elements that influence the RCs of a building. In addition, having a solid understanding of the LCC determinants would enhance the competence of cost estimators and result in the early delivery of a reliable and accurate cost estimate (Elhag et al., 2005). Increasing LCC is a significant concern for the building industry (Lai & Yik, 2008). Besides having unique characteristics, every building requires an unprecedented level of spending allocation and distribution (Ali et al., 2010). Based on Perera et al. (2016), managers can allocate funds with the optimum RC for each operation if the primary factors are identified early. Every month, Sri Lanka’s Construction Industry Development Authority (CIDA) produces construction cost indices, price indexes for building materials, labour remuneration, and plant and equipment. However, neither the LCC nor the RC of buildings can be predicted by indices. In addition, environmental, social, and political factors influence the cost of maintaining structures in a country (Omari, 2015). Seasonal fluctuations, for example, have a direct influence on building running costs in nations such as the United Kingdom and the United States (Catalina et al., 2011). Similarly, a developed country’s intrinsic economic features, such as its inflation rate, market pricing, and manufacturing cost, differ significantly from those of a developing country (Ashworth, 2004). End-user knowledge and awareness, behaviour, and attitudes are all important elements in determining a building’s operating and maintenance costs (Omari, 2015). The primary problems need a look at LCC analysis in Sri Lanka. Thus, this research aims to use a case study technique to evaluate the LCC components of high-rise residential flats in Sri Lanka, followed by a survey to discover the variables that impact these LCC components. This research attempts to identify the primary cost drivers and variables that have a substantial influence on LCC in this setting using a case study analysis and subsequent survey. The ultimate goal is to give insights and suggestions for lowering life cycle costs and promoting sustainable practices in the construction and operation of high-rise residential flats in Sri Lanka.

2 Literature Review The economic efficiency of a structure should be measured across its full life cycle. The building life cycle cost (BLCC) strategy is critical in this context since it focuses on cost optimisation over a building’s full life cycle (Korytarova & Hromadka, 2010). LCC was defined by Zabielski and Zabielska (2018) as the total cost of acquisition (project execution expenses), cost of ownership, and cost of disposal less

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the property’s residual value. In the following subsections, a comprehensive analysis of the significance of LCC application, the LCC contribution of various building elements with varying functions, and the factors affecting LCC high-rise residential buildings is provided.

2.1

Significance of LCC Application

According to Aye et al. (2000), LCC may be utilised as a decision-making tool for property and building initiatives. Nonetheless, preliminary cost estimates are important in the early stages of making decisions for building projects (Ji & Ahn, 2019). The authors also emphasised the importance of evaluating available alternatives early on and then deriving their cost implications. According to Pomponi and Moncaster (2016), the construction phase is frequently the focal point of life cycle evaluations at the building scale, which typically comprise between 20% and 40% of the life cycle stages. There is a paucity of information regarding the effects of many construction products after they depart the factory. Saravi et al. (2008) noted that a realistic estimate of expenditures to be spent throughout a building’s in-use phase would allow designers to avoid wasting time and money on buildings that are not economically feasible. As a result, designers expect to utilise publicly accessible RC information at the stage of conceptual design in order to anticipate the cost and improve the design in terms of cost and performance (Saravi et al., 2008). An early energy cost evaluation of a building, for example, gives critical cost information on the most important contribution of RC, encouraging contractors/clients to pick alternative energy-consuming equipment with regard to cost, design, and performance. To obtain the most economically advantageous tender, Kehily (2011) believes that the design and build tenders should be evaluated and selected based on the potential subsequent future costs rather than only the initial capital costs. Further, the tenderer may also be exposed to substantial risk if the bidder fails to view all the possible LCCs during the maintenance phase (Sterner, 2000). In this respect, the author further elaborates that the costing of the life cycle should be included in the cost-planning process and, where applicable, in the tendering process.

2.2

LCC Components and Its Contribution to Buildings

Operation expenses typically surpass construction costs; hence, building life cycle cost planning has become critical for informed decision-making (Kovacic & Zoller, 2015; Fantozzi et al., 2019). The operation and maintenance costs include all costs incurred due to the building’s operation and maintenance during the building’s operational phase (Morrissey & Horne, 2011). BS.ISO (2008) established operation

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cost as the cost of managing a building’s built facility, including the cost for administrative support, service attendants, rent, insurance, rates, local charges and taxes, and energy and other regulatory/environmental inspection costs. Building maintenance expenses have a substantial influence on the overall cost, notably energy usage (Boussabaine et al., 1999). According to Chua et al. (2018), each structure necessitates routine maintenance to function optimally over its extended lifetime. Building maintenance can be viewed as the technical and administrative tasks that ensure that the components can perform and function to an appropriate and acceptable standard (Lateef et al., 2010). The effectiveness of operation and maintenance ensures that the system operates with the fewest possible interruptions (Lai & Yik, 2011). Utilities (25%), repairs and maintenance (23%), cleaning (17%), and administrative labour (17%) contribute 82% of RC in a typical company building. In typical commercial buildings, air conditioning accounts for 43% of energy usage, followed by office equipment (17%), elevators and escalators (6%), and lights (33%). On the basis of the preceding information, it can be deduced that the RC of buildings contributes a substantial portion of LCC, whereas energy costs contribute a greater proportion. Another study estimated that roughly 80% of a building’s energy consumption is related to its operating phase (REEB Consortium, 2010).

2.3

Factors Affecting the LCC of High-Rise Residential Apartments

According to Islam et al. (2015), LCC is one of the most important functions in early-stage project decision-making. During the preconstruction, construction, and postconstruction stages, the building’s design, the nature of the site, technical considerations, tenant variables, the building’s purpose, the number of residents, and regulatory limits all have an influence (Islam et al., 2015; Shabniya, 2017). The building characteristics include building design elements and the nature of the site. The building design can be further assessed considering the location, lifetime, building size, plan shape, wall-to-floor ratio, aesthetical appearance, degree of circulation space, physical condition, the total height of the building, storey heights, the grouping of buildings, climate conditions, services, and resource availability (Meng et al., 2009; Ali et al., 2010; Nyayiemi, 2013; Perera et al., 2016). The LCC is also affected by the site’s characteristics. The characteristics of the site influence construction and maintenance expenses (Cunningham, 2013). Location, physical condition, service availability, resource availability, and climate compose the site’s nature (Cunningham, 2013; Nyayemi, 2013). Physical conditions include subsurface and geotechnical conditions, as well as environmental and hydrological conditions (Cunningham, 2013), whereas the service availability of a site refers to what services are available, such as sewer and stormwater drainage, mains water supply, electricity, telecommunications services, etc. (Krstic & Marenjak, 2012). Resource accessibility refers to the availability of resources on

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or near a construction site, including products and materials, construction plants, equipment and tools, human resources, space and amenities, subcontractors, and financing (Krstic & Marenjak, 2012). The climate condition of a location consists of weather temperature, soil temperature, radiation, relative humidity, wind direction and speed, precipitation, weather-damaging phenomena, and weather forecasting (Branch & Saghez, 2013). The technical factors include the technology employed, the craftsmanship, the quality of the materials and equipment employed, and the durability of the materials and equipment (Lateef et al., 2010; Chua et al., 2018). The LCC is affected by tenant factors, such as tenant expectations, property use, tenant vandalism, and delays and failures in reporting problems and accessibility to the property (Cunningham, 2013; Ali et al., 2010; Nyayemi, 2013). Ali et al. (2010) and Perera et al. (2016) state that the LCC of a building may also be influenced by the building’s function, number of occupants, legislative constraints, and third-party vandalism, among other factors. Building functions directly impact the LCC as the building design, the technology used, and other factors are dependent and interrelated to building functions. Similarly, the number of occupants impacts the construction, operation, and maintenance costs (Cunningham, 2013; Ali et al., 2010; Nyayiemi, 2013). The legislative constraint also impacts changes to the country’s legislation, including the incensement of taxes and other rules and regulations (Cunningham, 2013; Nyayiemi, 2013). The passive design approach can minimise the energy usage for cooling and enhances occupant comfort, thus improving low-energy architecture and reducing the overdependence on energy use and electricity demand in residential buildings (Akande, 2010). However, residential buildings are constructed without adequate consideration for the characteristics contributing to thermal comfort and energy efficiency, resulting in the widespread use of artificial lighting and ventilation in all apartments (Akande, 2010). Table 1 summarises the factors affecting the major elements of the LCC of buildings.

2.4

Calculation of Life Cycle Cost

The LCC calculation of a building is contingent on a number of variables, including the calculation method, discount rate, and longevity of the structure. Among the numerous methods available to calculate the LCC, such as the net percent value (NPV) method, payback method, internal rate of return (IRR) method, equivalent annual cost method, and net savings method, the NPV approach is primarily regarded as an appropriate instrument for analysing LCC (Schade, 2007). The NPV is computed by discounting cash flows to their present value using a discount factor based on the required rate of return on the annual projections of internal and external cash flows. The NPV takes into account the time value of money, generates a return equal to the market interest rate, is easy to implement, and is readily understood by all parties (Schade, 2007).

Factors Building design Life time Plan shape Building size Wall-to-floor ratio Circulation space Storey height Building height Grouping of buildings Aesthetic appearance Nature of the site Location Physical condition Service availability Resource availability Climate condition Technical factors Technology used Workmanship Durability of materials & equipment used [2]

[1]

[3] [7] [2] [5]

[1, 3] [1, 3] [5]

[7] [2] [7]

[4] [2] [7]

[4] [2]

[5] [5]

[3]

[3]

[3] [4] [5]

[2, 3] [1] [3]

External works

[3]

[2]

[3]

[2] [1]

Fabric cost

[1]

[4] [1] [3] [4] [3] [3] [1] [1]

Decorations & finishes

(continued)

[4] [2] [7]

[2]

[1]

[5]

[3]

[1]

Cleaning

[2, 7] [2, 3]

[3] [5]

[3]

[5] [4] [3] [2]

Repairs & replacement

[3]

[4]

[5] [3] [3] [3] [2] [5] [5]

Service management

[2, 3]

[2]

[1] [1, 3]

[7] [4] [2] [1]

Maintenance management

Maintenance

[3]

[2, 3] [3]

[7]

[1, 3, 4] [1,3]

[2]

[6, 7]

[4]

[1, 3] [2, 3] [2]

Construction

Operation Administrative costs, including taxes & Utility insurance

Construction

Table 1 Factors affecting significant elements of life cycle cost

[4]

[1, 3]

[3]

[2, 3]

[2, 5]

[2–4]

[7]

[5]

[3]

[3] [1]

Operation Administrative costs, including taxes & Utility insurance

[4]

[2, 6]

Construction [5]

Construction

[5]

[1, 3]

[4]

[1]

Maintenance management [2]

Maintenance

[3]

[4]

[5]

[3]

Service management [4]

[2]

[5]

[5]

[3]

Repairs & replacement [4]

[4]

[2, 3]

Cleaning

[2]

[6]

[2, 3]

External works

[2]

[1]

[1]

[4]

Fabric cost

[1]

[1]

[4]

Decorations & finishes

Source: [1] – Meng et al. (2009); [2] – Bari et al. (2012); [3] – Krstić and Marenjak (2012); [4] – Cunningham (2013); [5] – Islam et al. (2015); [6] – Che-Ghani et al. (2016); [7] – Shabniya (2017)

Factors Quality of materials & equipment used Tenant factors Use of the property Expectation of tenant Vandalism by the tenants Accessibility to the property Delay and failure in reporting the problem Other factors Function of the building Number of occupants Legislative constraints

Table 1 (continued)

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The most difficult aspect of the calculation is defining and quantifying the operational expenditures incurred during the building’s life cycle (Ying Liu et al., 2012). Further, describing the scope and frequency with which various structure sections must be maintained, repaired, or replaced is vital. The maintenance and repair of each structure have various requirements and a variable expected lifetime. Generally, the material utilised to construct a particular structure is one of the most important elements influencing the predicted lifespan. Additionally, it is important to consider production quality, construction quality, and maintenance frequency. Calculating LCC requires dividing the investigated structure into functional sections. Therefore, it is essential to determine the correct repair, maintenance, and replacement cycles and costs (Marková, 2011). The estimation of the building’s and its components’ useful lives is a second important aspect of the LCC calculation. Juan and Hsing (2017) have used 30-, 50-, and 100-year service lives. Others use 60 years (Han et al., 2014; Anuradha et al., 2018). In general, the tenure should correspond with the anticipated duration of use, which can be determined by the technical specifications of the structure or the estimated operating time. Additionally, the lifespans of distinct building components must be considered. For instance, Robati et al. (2018) determined the replacement term for glazed windows to be 25 years, indicating that this component will be replaced multiple times over the building’s lifetime. In addition, a suitable discount rate must be applied to future costs, such as maintenance and restorations, in order to determine their equivalent present value (Buyle et al., 2013). Individual investors are able to determine the discount rate used to model LCC. Nonetheless, it should correspond to the rate of return on other comparable endeavours or specific public initiative criteria (Silvestre et al., 2019). In addition, a sensitivity analysis is conducted to improve the precision of the NPV calculation for the primary elements that are impacted (Gluch & Baumann, 2004). Sensitivity analysis also determines the effects of changes in the values of a dependent variable on the values of a specific independent variable (Law & Smullen, 2008).

3 Research Methodology Initially, the LCC of the residential high-rise apartments was assessed using a case study methodology. After considering several variables that affect the LCC of residential buildings, three (03) high-rise residential buildings in Colombo with more than 30 storeys were selected for the study. To ascertain the LCC of the selected apartments, bills of quantities, drawings, annual budget forecasts, utility bills, maintenance budgets, manuals, and other cost documents were reviewed. In addition, semi-structured interviews were conducted to acquire additional information regarding building profiles and significant restorations and replacements carried out during the operational phase. Professionals involved in the construction and maintenance of the designated apartments, including the facilities engineer, project manager, and facilities manager with more than 3 years of experience,

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were interviewed. The distinguishing characteristics and features of the structures were also documented. For information on the LCC elements, BCIS (2008), BS.ISO (2008), New Rules of Measurement (NRM) 03, Building Cost Information Service (BCIS) standards, and Geekiyanage and Ramachandra (2020) were consulted. The LCC elements framework consists of fourteen (14) primary elements, thirty-two (32) sub-elements, and twenty-four (24) additional divisions. In addition to construction costs, the study examined nine (09) elements under maintenance costs and four (4) elements under operation costs. Other less significant elements, such as porterage, emergency lighting, built-in fixtures, and ground maintenance, were eliminated due to a lack of data. The collected LCC data were tabulated, and the required calculations were derived, such as the percentage contribution of construction cost, operation and maintenance elements to LCC, LCC per GIFA(m2), and LCC per occupant. The NPV of LCC was calculated utilising the formula outlined in the European ISO 15686-5:2017 standard, which is founded on the discount factor of future costs during the examined period (BS-ISO 15686-5, 2017). The following formula was used to determine LCC: T

LCC = t=0

Ct ð1 þ r Þt

Ct represents all costs as equivalent cash flows in year t, r is the discount rate, t is the analysed year (t = 0, 1, 2,..., T), and T is the life cycle’s duration in years. In addition, Pareto analysis was performed to determine the factors accountable for 80% of the LCC in residential apartments. Tembo Silungwe and Khatleli cite the Pareto principle as the basis for the Pareto theory (Tembo Silungwe and Khatleli, 2020). In certain situations, the Pareto principle states that approximately 80% of outcomes result from 20% of causes. Then a questionnaire survey was administered to thirty-four (34) professionals to evaluate the effects of factors affecting LCC elements on the LCC. On a five-point scale, respondents rated the extent to which the factors identified in the literature review affected LCC elements, with 1 indicating “Not at all impacted”, 2 “Slightly impacted”, 3 “Neutral”, 4 “Impacted”, and 5 “Highly impacted”. The values of weighted mean rate (WMR) was used to assess the factors impacting the LCC elements. The WMR was calculated using the formula shown below: WMR =

5 i = 1 ð xi

× f iÞ

R

where WMR = weighted mean rate, fi = response frequency, x_i = Likert scale (1–5), and R = total respondents. In addition, as reported in the literature, a sensitivity analysis was conducted on the collected data for the possible ranges of discount rates between 1% and 8% and the building lifespans of 40, 50, and 60 years. The following sections present the findings derived from the case study analysis and questionnaire survey responses.

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Table 2 Profile of the selected apartment buildings Details GIFA (m2) Circulation space (m2) Number of floors Number of units Total height (m) Number of occupants (Nr) Building age (years) Shape of the plan Orientation Grouping of buildings Location Specific feature

AP1 44,528 3670 47 152 160 600 05 Rectangular North Detached Colombo Green certified

AP2 Tower 1 25,620 2760 33 48 142 180 13 Rectangular North Attached Colombo Twin towers

Tower 2 28,698 3090 37 56 131 220

AP3 42,300 4243 45 115 146 448 12 Rectangular South Detached Colombo –

4 Research Findings 4.1

Profile of Case Study Buildings

The three selected high-rise structures for the investigation were designated AP1, AP2, and AP3. Table 2 details the historical context of the selected cases. Since all three residences were located in the same location, Colombo, where the political, social, and environmental conditions are comparable, the effects of these factors are likely to be minimal. Similarly, the effect of lessee factors on operating and maintenance costs is believed to be minimal when tenant income and ability to pay are considered. Facilities management companies were contracted to oversee the maintenance of three apartments. Two of the apartments are managed by the same company, while the third apartment is managed by a similar company. Therefore, nearly identical maintenance methods and standards are utilised in all three apartments, and it is believed that the impact of technical factors is minimal. In addition, design and construction defects will have little impact because the apartments have only been operated for a limited time. The apartment complex AP1 has 152 units. There are only two residences per floor and penthouses on the upper levels of the 104-unit AP2 building. The 115 units of AP3 consist of residences with one to four bedrooms and penthouses. Each floor contains only four apartments. All three apartments have distinct amenities that make living in high-rise dwellings convenient.

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4.2

K. Panchalingam and T. Ramachandra

Life Cycle Cost Analysis of Case Study Buildings

Initially, the operation and maintenance cost data were obtained for 1 year, the latest completed calendar year (2020). Table 3 provides the LCC calculation and the comparison of all three residential apartments. The NPVs of the apartments were worked out using a 40-year study time, a 4% discount rate, and a lower inflation rate. As the selected buildings completed the number of years (as per age) before the operational year considered for the study, the construction cost was taken as per the current market rate. The total construction cost was equally distributed for the construction period of 3 years. The NPV for cumulative operation and maintenance costs were calculated for 40 years, including significant replacements every 5 years. The contribution of residual value to the LCC is minimal. Due to the lack of data on the remaining value, this study mostly focused on the cost of building, running, upkeep, and major repairs. As evidenced in Table 3, on average, construction cost is 28%% of LCC, whereas operation and maintenance costs account for 49% and 22% of LCC, respectively. The contribution is significant, or an average replacement to the LCC is 1%. Considering RC as a significant contributor to the LCC, the elements of operation and maintenance costs were analysed based on its total contribution to RC, representing the cost of a building per year in its working life. Table 4 depicts the annual distribution of primary operation and maintenance elements as a percentage of RC for each of the three residential apartments. The RC of the selected buildings ranges from 49.7 to 60 Mn per annum. The RC per GIFA for all three buildings varies from LKR 1100.00 to LKR 2200.00. However, a notable variation is observed with RC per resident per month. The increased number of residents and units in A1 decreases the unit cost of A1 compared to the other two buildings. As shown in Table 4, the average operating costs of apartments comprise 70% of the RC, while maintenance costs account for 30%. According to Weerasinghe et al. (2016), operation and maintenance costs comprise 75% and 25% of the total RC in Sri Lankan office buildings, respectively. Compared to Weerasinghe et al. (2016), there is a negligible difference in Sri Lanka’s residential high rises. Four elements of operation costs have a greater effect on the RC than nine elements of maintenance costs. Approximately 45% and 17% of the RC is comprised of administrative and utility expenses, which are the most significant operational costs. While services and maintenance management account for approximately 14% and 5% of RC, respectively, they comprise most of the maintenance expenditure.

4.3

Pareto Analysis

Based on Pareto analysis, nine of the 36 sub-elements account for 80% of RC. Security accounts for 16% of RC, while each electricity and service attendant cost accounts for 14% of RC. Property management accounts for 13% of RC,

LCC elements LCC Construction Operation Maintenance Major replacement LCC per m2 of GIFA

AP1 (LKR.) 1,376,473,734.75 388,512,744.65 651,303,372.75 332,272,478.10 4,385,139.25 30,912.54 % 100% 28.2% 47.3% 24.1% 0.3%

Table 3 Distribution of major LCC elements of the residential buildings AP2 (LKR.) 1,630,034,046.34 425,513,958.43 829,283,162.35 358,665,667.05 16,571,258.52 36,606.94 % 100% 26.1% 50.9% 22.0% 1.0%

AP3 (LKR.) 1,536,417,691.39 462,515,172.20 785,320,935.68 279,939,204.90 8,642,378.61 34,504.53

% 100% 30.1% 51.1% 18.2% 0.6%

Towards Optimising the Life Cycle Costs of High-Rise. . . 165

LCC elements Running costs Operation Administrative costs Utilities Insurance Taxes Maintenance Service maintenance Maintenance management Fabric External works Repairs & replacement Cleaning Decoration RC per m2 of GIFA RC per occupant (per month)

AP1 (LKR.) 49,693,684.00 32,906,119.00 21,850,139.00 8,142,493.00 2,908,987.00 4500.00 16,787,565.00 6,587,837.00 2,883,000.00 1,119,440.00 5,047,288.00 500,000.00 480,000.00 170,000.00 1116.01 6,901.90 % 100% 66.22% 43.97% 16.39% 5.85% 0.01% 33.78% 13.26% 5.80% 2.25% 10.16% 1.01% 0.97% 0.34%

AP2 (LKR.) 60,019,320.00 41,898,279.00 27,449,885.00 11,344,398.00 3,100,000.00 3996.00 18,121,041.00 9,690,581.00 2,950,000.00 1,544,000.00 1,216,460.00 1,870,000.00 680,000.00 170,000.00 2209.92 25,117.52

Table 4 Distribution of annual operational and maintenance elements in residential apartments % 100% 69.81% 45.74% 18.9% 5.17% 0.01% 30.19% 16.15% 4.92% 2.58% 2.03% 3.12% 1.13% 0.28%

AP3 (LKR.) 53,820,659.34 39,677,153.91 24,001,816.80 7372697 8,298,140.11 4500.00 14,143,505.43 6,872,505.43 2,571,000.00 1,675,000.00 1,130,000.00 1,000,000.00 750,000.00 145,000.00 1272.36 10,011.28

% 100% 73.72% 44.6% 13.7% 15.42% 0.01% 26.28% 12.77% 4.78% 3.11% 2.10% 1.86% 1.39% 0.27%

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whereas lifts and escalators account for 9% of RC, insurance accounts for 6% of RC, and maintenance management accounts for 5% of RC. According to Table 5, the remaining constituents contribute less than 2%. The remaining elements contributing an average of less than 2% were consolidated and represented as “other elements”. The remaining components consist of internal decoration, external decorations, internal finishes, internal/external surfaces, internal drainage, external finishes, windows, roof structures, other structural components, plumbing and fittings and fixtures, gas installations, heating and ventilation, telecommunication and data, taxes, water rates, fuel oil, and effluent and drainage charges. Table 5 presents the distribution of RC elements. The average cost of insurance, landscaping, maintenance, and decoration was determined by disregarding high-end values because the contribution of each element is significantly higher in the luxury apartment than in the other two. The first four contributors of Pareto analysis – security, electricity, service attendants, and property management – belong to the operation cost elements with a cumulative value of 57%. The security cost is the major contributor to RC, accounting for 14–18% of RC. The cost of security includes the cost of security staff and the cost of security equipment. All three apartments have outsourced the security from specialised organisations. The security staff includes an officer in charge, senior security officers, and other security officers. All three apartments have incorporated a 24hr CCTV monitoring system and use an access card facilities system to enter the particular floor. Security is one of the important reasons for people to select apartments as residences that provide safe and secure space. Next to security, electricity is a significant cost element. The electricity for each apartment unit is maintained through separate electricity meters, and the cost of electricity for common amenities and lifts was distributed among the residents and collected monthly. The cost for service attendants accounts for 12% to 16% of RC. Service attendants’ costs include staff salaries, EPF/ETF charges, staff bonuses, overtime, welfare, staff insurance, and gratuity. Further, it also increases with the welfare and facilities given to the staff. Property management accounts for 11–17% of RC. In all three apartments, a manager is appointed for the overall administration of the apartment complex. Further, the property management staff includes a secretary, an accountant, and an assistant. Property management costs vary with the number of employees and their salaries. The lifts and escalators contribute 7–11% of RC compared to previous elements. The elevators and escalators are the most important service maintenance costaccounting subcomponents. Compared to other apartments, apartment AP3 spends two types of costs to maintain lifts and escalators, including the expense for maintenance carried through an outsourced company and maintenance carried by the management. At the periodical maintenance service, all the cables are checked, lubricated, repaired, and replaced if necessary. According to Table 5, A3 shows a marginally increased percentage of operating costs than A1 and A2. The contribution of insurance to the operation costs of A3 is higher compared to A1 and A2. A1 and A2 have insurance ranging from 5% to 6% of RC, whereas A3 shows an unusual increase of 15% of RC. The increased insurance cost is due to the two different

14 15 20

1 2 3 4 5 6 7 8 9 10 11 12 13

No

Cost components Running cost Security Electricity Service attendants Property management Lifts & escalators Insurance Maintenance management Repairs & replacement Electric power & lighting Waste disposal Repairs and decoration Gas Fire detection & protection system Landscaping Other M&E services Other elements

3,500,000.00 415,484.00 2,971,263.00

AP1 (LKR.) 49,693,684.00 8,626,000.00 6,957,139.00 5,942,970.00 6,411,660.00 4,016,318.00 2,908,987.00 2,883,000.00 500,000.00 833,126.00 869,509.00 1,547,288.00 550,000.00 565,440.00

Table 5 Distribution of cost of elements of RC

7% 1% 6%

% 100% 17% 14% 12% 13% 8% 6% 6% 1% 2% 2% 3% 1% 1% 480,000.00 490,000.00 3,759,613.00

AP2 (LKR.) 60,019,320.00 10,804,322.00 9,959,161.00 9,517,563.00 6,500,000.00 6,738,056.00 3,100,000.00 2,950,000.00 1,870,000.00 967,590.00 628,000.00 736,460.00 593,616.00 720,200.00 1% 1% 6%

% 100% 18% 17% 16% 11% 11% 5% 5% 3% 2% 1% 1% 1% 1% 500,000.00 490,505.43 3,931,677.60

AP3 (LKR.) 53,820,659.34 7,550,000.00 5,805,475.00 6,911,496.80 8,971,500.00 3,795,500.00 8,298,140.11 2,571,000.00 1,000,000.00 1,059,500.00 568,820.00 630,000.00 841,544.40 650,000.00 1% 1% 8%

% 100% 14% 11% 13% 17% 7% 15% 5% 2% 2% 1% 1% 2% 1% 490,000.00 465,329.81 3,554,184.54

Average cost (LKR.) 54,511,221.11 8,993,440.67 7,573,925.00 7,457,343.27 7,294,386.67 4,849,958.00 3,004,493.50 2,801,333.33 1,123,333.33 953,405.33 688,776.33 683,230.00 661,720.13 645,213.33 1% 1% 6%

16% 14% 14% 13% 9% 6% 5% 2% 2% 1% 1% 1% 1%

As % of RC

87% 87% 94%

16% 30% 44% 57% 66% 72% 77% 79% 81% 82% 83% 85% 86%

Cumulative %

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169

policies obtained by A3. The A3 has taken insurance for fire and additional perils and public liability. Fire and other perils account for 94%, and public liability accounts for only 6% of the insurance costs of A3. The increased expense for insurance has predominantly impacted the RC of A3 compared to the other two apartments. The maintenance management of all three apartments is outsourced externally and accounts for nearly 5–6% of RC. The cost of repairs and replacements in AP1 is 1%, whereas they range between 2% and 3% for the other two apartments. Compared to AP1, AP2 and AP3 have higher expenditures. AP2 and AP3 have been operational for almost 10 years, while AP1 has been operational for only 3 years. With the expansion of the operational period, repair and replacement expenditures have increased. All three apartments’ electric power and lighting maintenance expenses account for 2% of RC. Electricity power and lighting include five sub-elements: lighting conductors, high-voltage generation, transmission and distribution, power, lighting, and lamp replacement. High-voltage generation contributes significantly to electric power and lighting costs among the five sub-elements. Less than 1% of the RC is made up of M&E services, including landscaping, gas, fire detection and protection, and waste disposal. In all three apartments, the waste disposal is managed with the assistance of municipal councils. The repairs and decoration include the cost of pest control services and drain and road pavement maintenance. All three apartments are equipped with smoke, heat, and gas detectors, as well as fire safety equipment, including automatic fire sprinklers, wet riser systems for firefighting hoses, and floor-by-floor fire extinguishers. While the other two apartments only spend 1% of RC on gardening, AP1 spends 7% of RC on it. The AP1’s maintenance, drip watering system, and pest control are more expensive since it is a green-certified home vertical garden. The administration has contracted with a qualified landscape architect to handle the continuous upkeep of the apartment complex’s green terraces. Refrigeration equipment, fire alarms, security cameras, access control, and loose appliances are the four sub-elements that make up the other M&E services. There are no records of the purchase of loose appliances and refrigeration equipment in any of the three units. The Pareto analysis for the RC elements is shown in Fig. 1. Security (16%), electricity (14%), service personnel (14%), property management (13%), escalators and lifts (9%), insurance (6%), maintenance management (5%), repairs and replacement (2%), and electric power and illumination (2%) account for 80% of RC, as shown in Fig. 1.

4.4

Sensitivity Analysis for LCC Calculation

Sensitivity analysis determines how the dependent variable’s values vary depending on the various values of the specific independent variable. The lifespan and discount factor were treated as independent variables in the sensitivity analysis. The discount rate may be altered from 0.9% to 7.2%, according to the Central Bank of Sri Lanka.

K. Panchalingam and T. Ramachandra

10.00

100%

9.00

90%

8.00

80%

7.00

70%

6.00

60%

5.00

50%

4.00

40%

3.00

30%

2.00

20%

1.00

10%

0.00

0%

Cumulative %

LCC of buildings (Rs.) (Mn)

170

Fig. 1 Pareto analysis for the RC elements per unit

The discount factor was changed from 1% to 9% in order to achieve an exact distribution, while all other variables remained fixed. For all flats, sensitivity analysis is shown in Fig. 2 for discount rates ranging from 1% to 8%. According to Fig. 2, the value of LCC decreases with the increasing discount rate. The LCC of AP2 has the highest value, whereas AP1 has the lowest LCC in all ranges. The difference between AP2 and AP3 decreases with an increase in the discount rate. Figure 3 presents the sensitivity analysis for all the apartments with a building lifespan of 40–60 years with a 4% discount rate. According to Fig. 3, the LCC differs from lowest to highest when the lifespan changes from short to long. Accordingly, AP2 has the highest LCC, while AP1 has the lowest LCC with changing lifespans. Therefore, with the increased building lifespan, operating and maintenance costs increase, which increases the LCC costs of the apartments. Further, the LCC of high-rise residential apartments increases with the increased GIFA and number of floors.

4.5

Analysis of Factors Affecting LCC

The questionnaire was distributed to 37 professionals who develop and manage high-rise residential apartments and are familiar with the LCC concept. The participants in the survey questionnaire are described in Table 6. Table 6 demonstrates that thirty-four (34) professionals responded to the survey, with a reasonable

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171

LCC of buildings (Rs.) (Mn)

3,000 2,500 2,000 1,500 1,000 500 1%

2%

3% AP1

4%

5%

AP2

AP3

6%

7%

8%

Discount factor

LCC of buildings (Rs.) (Mn)

Fig. 2 Sensitivity analysis for all the apartments with discount rates from 1% to 8%

2,000 1,800 1,600 1,400 1,200 1,000 800 600 400 200 40

50 AP1

AP2

60 AP3

Building life span - years

Fig. 3 Sensitivity analysis for all the apartments with a building lifespan from 40 to 60 years

representation of architects, facilities managers, engineers, quantity surveyors, and project managers. In addition, 76% of respondents had more than 5 years of work experience. Respondents used a Likert scale ranging from 1 to 5 to assess the impact of each factor on different LCC elements. The WMR for each factor was determined using the replies. To determine the extent of the effect of the variables on LCC components, further clustering was done based on the WMR values. The scale used to express the degree of impacted level is shown in Table 7. Table 8 shows the factors affecting LCC elements and the degree of affected level.

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Table 6 Profile of survey participants Professionals Quantity surveyor Engineer Facilities manager Project manager Architect Total

Frequency No % 10 29% 9 26% 7 21% 4 12% 4 12% 34 100%

Table 7 Criteria that were impacted based on the WMR

WMR 4.75 ≤ x ≤ 5.00 3.50 ≤ x < 4.75 2.00 ≤ x < 3.50 1.00 ≤ x < 2.00

Level of experience 1–5 years 5–10 years 10–15 years 15–20 years 20–25 years

Frequency No % 8 24% 9 26% 5 15% 8 24% 4 12% 34 100%

Influence level and degree HA Highly affected A Affected SA Slightly affected NA Not affected

Fourteen out of 24 elements have a significant impact on building costs, as shown in Table 8. The GFA and plan shape of a building were regarded as having a significant impact on the construction costs of all the respondents. Next to that, durability of materials and equipment utilised, and workmanship were also highlighted with the mean values of 4.85 and 4.82, respectively. Wall-to-floor ratio, resource availability, circulation space, governmental restrictions, and climate conditions are five factors that have an impact on construction costs. On the other hand, the grouping of buildings, service availability, property use, tenant vandalism, and the number of occupants have a minor impact on construction costs. The weighted mean analysis demonstrates that the number of residents and the building’s purpose have a significant effect on utility and administrative expenses, with mean values of 4.97 and 4.94, respectively. The building’s function affects both its service and maintenance costs. According to the weighted mean values of the variables, the aggregation of building and legislative changes has a negligible effect on service maintenance costs. In addition, the aesthetic allure of a building has a significant impact on the fabric and adorning costs. The purpose of the building and the surrounding environment have a significant impact on cleansing expenses. In contrast, the function of the building and the tenant’s expectations have a substantial effect on external work expenses. The utilised technology has an average weighted impact of 4.94 on repair and replacement costs. With 4.91 of the WMR value, workmanship is recognised as the second most essential element, following the employed technology. The cost of maintenance management is significantly influenced by 14 variables. The component that has the least impact on maintenance management costs is the technology employed.

Construction Factors Building design Plan shape HA HA Size of the building (GFA) Life time HA Storey HA height Building HA height Aesthetic HA appearance Grouping of SA buildings Wall-toA floor ratio Circulation A space Nature of the site Physical HA condition Location HA Resource A availability

Construction

SA HA

A NA

HA NA

SA

NA

A

A

A

NA

SA HA

SA HA

HA HA

HA

NA

HA

A

A

NA

SA NA

A HA

SA

HA

HA

A

A

HA

HA HA

Maintenance management

Maintenance

Operation Administrative costs incl. taxes Utility & insurance

Table 8 Factors affecting LCC elements and degree of the affected level

A SA

A

HA

A

NA

SA

HA

HA HA

HA HA

Services management

NA NA

SA

A

A

SA

A

SA

HA HA

A A

Repairs & replacement

NA NA

NA

A

NA

SA

HA

A

SA SA

HA HA

Cleaning

A SA

NA

A

NA

A

A

A

A A

A NA

External works

SA A

NA

HA

NA

SA

HA

A

A NA

HA A

Fabric cost

A NA

SA

HA

NA

A

HA

A

A A

HA SA

Decorations & finishes

10 13

6

23

21

20

18

17

8 14

1 2

Rank

(continued)

4.82 4.76

4.88

3.65

3.71

3.79

4.21

4.35

4.85 4.62

5.00 4.97

WMR

Construction Factors Service SA availability Climate A condition Technical factors Quality of HA materials & equipment used Technology HA used HA Durability of materials & equipment used HA Workmanship Tenant factors Vandalism SA by tenants Expectation HA of tenants Use of the SA property

Construction

Table 8 (continued)

SA

HA

A

NA

NA

NA

A

HA

HA

NA

NA

A

SA

SA

NA

SA

A

HA

A

HA

A

HA

SA

HA

Maintenance management HA

Maintenance

Operation Administrative costs incl. taxes Utility & insurance HA HA

HA

SA

A

HA

HA

HA

HA

HA

Services management A

SA

A

HA

HA

HA

HA

HA

A

Repairs & replacement A

NA

A

A

HA

A

A

A

A

Cleaning SA

SA

HA

SA

A

NA

SA

A

HA

External works A

HA

A

NA

A

NA

SA

A

HA

Fabric cost A

NA

A

NA

NA

NA

NA

SA

HA

Decorations & finishes A

3.65

3.97

4.44

4.82

4.85

4.88

4.94

3.68

WMR 4.59

23

19

16

10

8

6

4

22

Rank 15

Other factors Number of occupants Function of the building Legislative constraints

HA

HA

A

SA

HA

A

HA

HA

HA

SA

HA

HA

NA

HA

SA

A

HA

HA

A

HA

HA

NA

HA

A

NA

NA

NA

NA

NA

A

4.82

4.94

4.97

10

4

2

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According to the data, the majority of building designs impact the LCC of buildings. Particularly, building lifespan, degree of circulation, plan shape, service availability, and total building height significantly impact the LCC of residential apartments. LCC is significantly influenced by climatic conditions, the number of residents, and the building function. In contrast, wall-to-floor ratio, resource availability, and physical conditions have the least impact on the LCC of residential apartments in high-rise buildings. Similarly, the operation cost of the building is heavily influenced by the building’s lifespan, service availability, the number of occupants, the building’s function, and its grouping with other buildings and is least influenced by the building’s physical aspect and condition. In contrast, the maintenance cost is heavily influenced by the degree of circulation, plan size, total height, and lifespan of the building. It is the least affected by physical state, wall-to-floor ratio, resource availability, and location. Most factors affect maintenance management, service maintenance, administration, and repair and replacement costs. The fabric cost is impacted less by building design characteristics than other elements. Similarly, maintenance management is highly affected by the nature of the site. The nature of the site least impacts administrative costs and fabric costs compared to other elements. Thus, most building characteristics highly affect maintenance management and service maintenance. Next, most building characteristics impact repairs and replacement, utility costs, decoration costs, external works, and administrative costs.

5 Discussions Colombo high-rise residential apartment construction costs average between 26% and 30% of the LCC. In contrast, operating and maintenance expenses represent 47–51% and 18–24%, respectively, of LCC. Significant replacement contributes 1% to the LCC in AP2 but less in the other two residences. In the designated apartments, operating costs range between 65% and 70% of RC, while maintenance costs account for 30–35%. Included in the operational expenses are insurance, utilities, administrative expenditures, and taxes. Maintenance costs include service maintenance, maintenance management, external works, fabric, repairs and replacements, cleaning, and decorating. The Pareto analysis indicates that nine of the 36 sub-elements account for 80% of RC. Under service management, five of the nine elements are operation cost elements, while the remaining two are maintenance elements. The Pareto analysis revealed the following factors to be significant: utility costs, service attendant costs, maintenance management, service management, and repairs and replacements. Nyayiemi (2013) asserts, based on research findings, that the plan design and tenure of LCCs are substantially impacted. Plan shape and duration were ranked first and second, respectively, by respondents. Moreover, Ali et al. (2010) and Meng et al. (2009) reported that the LCC is affected by plan geometry, building dimension (GFA), wall-to-floor ratio, degree of circulation space, storey height, total building

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height, and building aggregation. Location, physical condition, the availability of services and resources, and climate have a direct effect on construction and maintenance expenses (Cunningham, 2013). According to Nyayiemi (2013), the availability of resources affects the utility, decoration, and maintenance costs of a building. In addition, as stated by Bari et al. (2012), Nyayiemi (2013), and Meng et al. (2009) in the literature, technical factors such as the technology used, craftsmanship, the quality of materials and equipment used, and the durability of the materials and equipment used have a direct effect on the cost of service maintenance, construction, repairs and replacement, and maintenance management. According to Ali et al. (2010), the cost of decoration was affected by the technology, workmanship, and durability of materials and equipment. Construction, administration, decoration, fabric, cleaning, external works, repair and replacement, and maintenance management costs are directly affected by tenant expectations (Che-Ghani et al., 2016; Cunningham, 2013; Meng et al., 2009). According to Ali et al. (2010), the function of the building affected the preponderance of LCC cost components. The number of occupants influences utility, administration, service maintenance, repair and replacement, and maintenance management costs (Ali et al., 2010; Cunningham, 2013; Nyayemi, 2013). As a result, the number of occupants influences utility, administration, service maintenance, repair and replacement, and maintenance management costs. In addition, this factor affected construction, maintenance, and external expenses. Administrative costs are affected by legislative restrictions (Krstic & Marenjak, 2012). According to the survey results, the legislative changes affected construction, utility, administration, cleaning, repair and replacement, and maintenance management costs. The survey results validate the conclusion of the literature review. Several factors can impact the LCC of a building, such as the building’s design, the character of the site, technical factors, and tenant factors. This study focuses on the effects of these variables on the LCC of high-rise apartment buildings. The building design includes the lifetime, plan shape, size of the building, degree of circulation space, the total height of the building, storey heights, wall-to-floor ratio, aesthetical appearance, and grouping of buildings, whereas the nature of the site includes the location, physical condition, services availability, resources availability, climate condition, and other characteristics; the number of occupants and function of the building. The majority of design and technical elements have a substantial effect on construction costs. Additionally, location, physical condition, tenant expectations, and building function have a significant effect on construction costs. Utilities account for 16% of RC and are an essential component of operational expenditures. Gas, electricity, fuel oil, water rates, and effluent and drainage fees are the other utilities. Among other variables, the utility costs of a building are significantly impacted by its design, location, and service availability. Approximately 45% of RC is comprised of administrative expenses. Administrative expenses include service attendants, security, trash removal, and property management. With the exception of building dimensions, storey height, aesthetic appearance, and physical condition, administrative costs had a significant impact on all building characteristics.

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Maintenance management, service maintenance, administrative, and repair and replacement costs are influenced by the preponderance of building design factors relative to other elements. Maintenance management accounts for 16–18% of RC. The RC is significantly influenced by the geometry of the floor plan, the degree of circulation, the climatic conditions, and the duration of the building. In contrast, the effects of physical condition, resource accessibility, and wall-to-floor ratio appear to be negligible. Service maintenance is a significant component of maintenance expenditures, comprising between 12% and 16% of RC. In addition to the arrangement of elements, climatic conditions, use of property, and purpose of the structure, the design of the building has a significant impact on service maintenance. It does not appear that building classification and service maintenance are correlated. Repair and replacement costs account for 2–3% of buildings with more than 10 years of occupancy, but only 1% of buildings with less than 3 years. In addition, the tenure of a structure has a substantial effect on restorations and replacements. Furthermore, approximately 1% of RC is allocated to cleaning and textiles. In addition, the cost of these components depends heavily on the building’s design and aesthetic appeal. In a conventional building, external labour contributes less than 2% of the RC, whereas in a green building, the same work contributes roughly 10% of the RC. Climate conditions and the function of the building have a significant impact on the exterior works. Exterior works are largely unaffected by size, wall-to-floor ratio, and physical condition.

6 Conclusions and Implications The building, operation, and maintenance expenses, which each make up 28%, 50%, and 22% of the LCC for high-rise residential units in Sri Lanka, were found to be the three major components of LCC. The annual LCC per GFA is about Rs. 31,000.00 to 36,600.00 (approx. US$ 100). The four elements of operational costs account for 70% of RC, while seven elements of maintenance cost account for 30% of RC. Compared to maintenance cost, the operation cost plays a vital role in the RC of high-rise apartments. Based on Pareto analysis, nine of the 36 sub-elements account for 80% of RC. Five are operational expense components. The elements belong to utility cost, service attendant cost, maintenance management, service management, and repairs and replacement. Security accounts for 16% of RC, while each electricity and service attendant’s cost accounts for 14% of RC. Property management accounts for 13% of RC, whereas lifts and escalators account for 9% of RC, insurance accounts for 6% of RC, and maintenance management account for 5% of RC. The remaining elements contribute less than 2%. Administrative costs, utilities, and insurance are the significant elements of operational costs. The administrative cost accounts for 45% of RC and is highly affected by building lifetime, resource availability, climatic conditions, the number of occupants, and building function. Next to administrative costs, utilities are a significant element in the operational cost, accounting for nearly 16% of RC and

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including five sub-elements. All the building design characteristics significantly impact utility cost, except aesthetical appearance. Considering the nature of the site, utility cost is significantly impacted by service availability and moderately impacted by location. Similar to administrative costs, the other characteristics significantly impact the utility cost. Maintenance management, service maintenance, external works, and repairs and replacements are the most expensive components of the maintenance costs of these high-rise buildings. Elevators and escalators account for more than 55% of maintenance costs for services. Building upkeep and repair costs have gone up over time, it should be mentioned. In a similar vein, the extra expenditures for landscaping, repair, and ornamentation translate into much higher exterior maintenance costs for green buildings. The cost of maintaining a building’s components is significantly influenced by its design features. The building design has a substantial effect on service management and maintenance management. Similarly, climatic conditions, the number of occupants, and the purpose of the building have a significant impact on the majority of maintenance cost factors. The LCC of apartments decreased as the discount factor increased, and it increased as the life duration of the building increased. Thus, designers, investors, and occupants would be able to optimise the LCC of high-rise residential buildings during the design and operation phases if they paid close attention to factors influencing LCC components. The study was limited to residential apartments above 30 floors in the Colombo district and considered specific building characteristics. Further, the residual value of the apartments was not considered for the study as the contribution of residual value to the LCC is minimal due to the lack of data.

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Correction to: Life-Cycle Cost Assessment of Various Wave Energy Converters (Based on Energy and Carbon Intensity) Mohammad Hossein Jahangir and Shahrzad Bahrizadeh

Correction to: Chapter 5 in: S. S. Muthu (ed.), Life Cycle Costing, Environmental Footprints and Eco-design of Products and Processes, https://doi.org/10.1007/978-3-031-40993-6_5 The original version of the chapter “Life-Cycle Cost Assessment of Various Wave Energy Converters (Based on Energy and Carbon Intensity)” was inadvertently published with incomplete affiliation. The complete affiliation details have been updated in the chapter.

The updated version of this chapter can be found at https://doi.org/10.1007/978-3-031-40993-6_5 © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. S. Muthu (ed.), Life Cycle Costing, Environmental Footprints and Eco-design of Products and Processes, https://doi.org/10.1007/978-3-031-40993-6_7

C1

Index

A Adjusted Internal Rate of Return (AIRR), 18–20, 30, 39, 40

B Barriers, 5, 27, 43–44, 51–74 Bio-chemical, 78, 83–85, 87, 95–98, 106, 108 Building characteristics, 64, 157, 176, 177, 179 Building energy performance, 1–23 Building sector, 1, 2, 27, 29, 36, 44, 51

D Discount rate, 7, 8, 12, 18–21, 23, 30, 33, 35, 38, 40, 41, 43, 44, 52, 53, 55, 70, 86, 88, 89, 103, 105, 107, 153, 158, 161, 162, 164, 169–171

I Indicators, 11, 18, 21, 30, 31, 33–36, 39–45, 86–89, 103, 106, 146, 147 Inflation rate, 7, 9, 10, 18, 37, 41, 43, 44, 69, 70, 74, 155, 164

Life cycle costing (LCC), 1–23, 27–45, 51–74, 77–108, 117, 118, 134–147, 153–158, 161, 162, 164–166, 170, 171, 173, 176–179 Life cycle costing (LCC) assessment, 145–149

M Maintenance costs, 2, 10, 15, 17, 52, 56, 59, 60, 62, 63, 67–74, 89, 90, 95, 135, 153–156, 158, 162–164, 170, 172, 176–179 Mega Roller, 118, 129, 139–141, 144, 147 Methodologies, 27–45, 57, 78, 161

N Nearly zero energy buildings (nZEBs), 27–45 Net saving (NS), 12, 18, 19, 30, 39, 40, 86, 91, 158

O Operation costs, 40, 60, 62, 63, 90, 154, 162, 164, 167, 176, 178

P Pelamis P1, 118, 147 L LCC applications, 1–23, 28, 52, 55, 65–73, 77–108, 156 LCC calculation software, 15, 22

R Residential apartments, 153–179

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. S. Muthu (ed.), Life Cycle Costing, Environmental Footprints and Eco-design of Products and Processes, https://doi.org/10.1007/978-3-031-40993-6

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184 S Savings-Investment Ratio (SIR), 18–20, 30, 39–43 Strategies, 22, 29, 51–74, 78, 87, 98, 103, 105–107, 144, 149, 155

T Thermo-chemical, 78–83, 87, 89–95, 106–108

Index W Waste-to-energy (WtE), 77–108 Wave dragon, 118, 129, 133, 142–145, 147 Wave energy converter (WEC), 117, 118, 127, 128, 130, 133, 136–138, 140–149 Wave energy converter’s (WECs) supply chain, 117, 134