Strategy for reducing carbon dioxide emissions from maintenance and rehabilitation of highway pavement

Journal of Cleaner Production 209 (2019) 88e100

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Strategy for reducing carbon dioxide emissions from maintenance and rehabilitation of highway pavement Jae-ho Choi Dong-A Univ., Dept. of Civil Engrg., P4401, 550Bungil 37, Nakdong-Daero, Saha-Gu, Busan, 49315, South Korea

a r t i c l e i n f o

a b s t r a c t

Article history: Received 3 May 2018 Received in revised form 17 September 2018 Accepted 21 October 2018 Available online 22 October 2018

The operation and maintenance management environment of the national highway agency is expected to be further aggravated by increases in maintenance and rehabilitation costs and environmental costs due to aging road facilities. It is imperative to find a way to reduce life-cycle costs (LCC) and environmental costs (EC) associated with carbon dioxide emissions in road construction. A case study was conducted to select the maintenance and rehabilitation (M&R) scenario with the lowest cost by taking into account carbon dioxide emissions. This was done using a hybrid of LCC analysis (LCCA) and life-cycle assessment (LCA) methods with three representative M&R scenarios: repetitive patching works, single milling and overlay works, and combined works, respectively. The case study analysis indicates that the most economical scenario according to the LCC is scenario 1, but when considering the EC directly linked to carbon dioxide emissions, scenario 2 is the most economical choice for the national highway agency at a carbon trading price of 42.27 USD/ton and above. This case study is the first international research effort linking long-term pavement performance and carbon dioxide emissions to provide a decisionmaking framework for the most carbon-efficient M&R strategy for roads. © 2018 Elsevier Ltd. All rights reserved.

Keywords: Life-cycle cost Life-cycle assessment Pavement condition index Carbon dioxide emission Carbon price Maintenance strategy

1. Introduction Climate change is currently one of the greatest threats to human health, and greenhouse gases are recognized as the main cause, especially carbon dioxide. Among the various industrial sectors, the construction sectordparticularly road constructiondis one of the three main drivers of resource use and has a serious environmental impact. This impact includes one-third of global energy consumption, 40% of raw material consumption, and 30% of carbon emissions. In the republic of Korea, the road sector has been reported as the second largest source of greenhouse gas emissions since the 1970s, accounting for 17% of the national total (Barandica et al., 2013; Lee et al., 2012; Wang et al., 2015). Many roads are under construction or in operation all over the world, which represents a broad opportunity for environmental improvement (Barandica et al., 2013). Transportation agencies are investigating strategies for reducing carbon emissions in the construction and maintenance of road facilities. Globally, it is expected that there will be substantial road construction and budgetary input for road maintenance. The Federal Highway Administration

E-mail address: [email protected]. https://doi.org/10.1016/j.jclepro.2018.10.226 0959-6526/© 2018 Elsevier Ltd. All rights reserved.

(FHWA) of the U.S. Department of Transportation estimates that $65e83 billion (about 40% of the spent budget on all related expenses in 2010) will be used for the repair and maintenance of existing highways and bridges in the U.S. In planned infrastructure investments for 2014e2024, the Canadian province of Quebec has assigned 70% of the road network investment budget to the maintenance of roadways and structures in good condition. Roadway development and maintenance budgets are also increasing in Brazil, Korea, China, and some European countries such as Denmark (Azarijafari et al., 2016; Huang et al., 2016; Kim, 2016). In addition to increases in fuel consumption, increases in the number of vehicles and accelerating road deterioration are causing various types of socio-economic damages, such as traffic congestion, accidents, and time delays. They also negatively affect the environment and health due to the generation of greenhouses gases during frequent road maintenance and rehabilitation (M&R) activities (Seo and Kim, 2013a, b). M&R work for road facilities is done repeatedly to preserve the usability of roads at its serviceable condition, which generates carbon dioxide emissions. Carbon emissions are one of the essential factors that need to be considered in the selection of alternative pavement M&R strategies. Highway agencies are faced with two goals: reducing the life-cycle cost (LCC)

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of construction and maintenance costs, and reducing carbon emissions for sustainable pavement construction and maintenance to cope with climate change. These goals can be conflicting due to the varying carbon pricing rate, which is a tool used for transforming the carbon emissions into monetary terms. A suitable carbon pricing rate is very important for policymakers to consider the trade-off between environmental protection and economic development (Jin et al., 2018). The Effective Carbon Rate (ECR) is the sum of the carbon tax, emission permit price, and other taxes imposed on energy use expressed in euros per ton of CO2. The ECR has been used as an indicator when governments control the policy of carbon emissions from energy use. The ECR must be in line with the marginal cost of climate change (also called climate cost) to give an effective signal of carbon emissions. Carbon pricing provides a low-cost policy instrument to effectively and gradually reduce carbon emissions immediately. It is an essential part of climate change mitigation. However, carbon prices often are zero or very low, and 60% of carbon emissions from energy use are unpriced in the 41 countries that cover 80% of global energy use (including 34 OECD countries and 7 other countries) (OECD, 2016). Many studies have quantitatively investigated multiple aspects of the influences of carbon pricing for a region or country (Li and Su, 2017). Many studies on the carbon emissions of road facilities mainly focus on life-cycle assessment (LCA), which evaluates the environmental load of the road construction while considering the complexity of the construction project, site, and local variables. LCA is formulated by standards ISO 14040 and 44 of the International Organization for Standardization (ISO) and is composed of the following four steps: 1) goal and scope (G&S) definitions, 2) inventory analysis, and 3) environmental impact assessment (Azarijafari et al., 2016; Inyim et al., 2016). The sub-processes in the G&S definitions include defining study objectives, functional units (FU), and the system boundary (SB). FU is defined as a reference unit to indicate the quantified performance of a product system, such as lengths (e.g., kilometers), traffic volume (e.g., annual average daily traffic (AADT)), square meters of pavement, and structural capacity. The SB in a pavement LCA study usually implies the life-cycle analysis period, which is defined as “the time period over which the functional unit is evaluated” (Santero et al., 2011). It involves a list of activities and processes within the life-cycle phases. Phases included in the analysis period of an LCA study include materials production, construction, use, M&R, and end of life (EOL) phases (Azarijafari et al., 2016; Inyim et al., 2016; Wang et al., 2012). There is growing interest in evaluating the carbon footprints of pavements (Azarijafari et al., 2016; Santero et al., 2011; Wang et al., 2012). Numerous studies on pavement LCA have been conducted over the past 20 years. Santero et al. (2011) explains that previous studies on pavement LCA have contributed to building a framework for quantitatively understanding the environmental impact of the road construction, but failed to reach global conclusions in terms of material selection, maintenance strategies, design lives, and other best practice policies. They emphasized the need for further improvements such as standardizing FU, extending SB and study scopes, and enhancing data quality and reliability for comprehensive environmental impact quantification and effective sustainability guidance. Similarly, Inyim et al. (2016) presented the limitations of previous studies in selecting the most sustainable road pavement in terms of environmental aspects: the use of performance-adjusted FU, forecasting the impact of maintenance and EOL phases, uncertainty of road performance forecasting, and construction of regionspecific inventory data. They stated that there will be significant changes in LCA results when considering the environmental

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burden related to traffic information, vehicle emissions and pavement-vehicle interaction at the use phase. More specifically, Azarijafari et al. (2016) addressed important challenges and research opportunities for carrying out enhanced pavement LCA studies, including the following parameters such as pavement surface roughness, noise, lighting, albedo effect, carbonation and earthworks. Particularly, Santero et al. (2011) pointed out that only one of the existing studies related to the pavement LCA included the use phase. Wang et al. (2012) noted that the omission of the use phase was the biggest deficiency in the SB view. With regard to defining FU, Santero et al. (2011) pointed out that a major shortfall in pavement LCA studies is the lack of pavement conditions and performance. The estimation of the environmental impacts at the M&R phase is directly affected by the frequency and type of M&R activities (Inyim et al., 2016). By taking into account pavement conditions, a better comparison of alternatives can be performed because poorer performance or conditions of a pavement section will require a more maintenance, thus leading to greater environmental burden. However, research on pavement LCA rarely uses a prediction model for road conditions to evaluate the timing and €sser and type of M&R works (Batouli and Mostafavi, 2015; Gscho Wallbaum, 2013). Furthermore, no studies have used such a model that reflects the environment and traffic characteristics of different countries. Previous studies have also been limited by the measurement of environmental burden resulting from new materials in pavement construction, the use of recycled materials, the implementation of different designs and types of roads, and limited system boundaries (Azarijafari et al., 2016; Liu et al., 2014). LCC analysis (LCCA) in the pavement sector is a decision-making tool that has long been used for assessing the comprehensive and long-term economic efficiency of alternative pavement design options in an early phase of the project development cycle. According to Walls and Smith (1998), general LCC components of pavement include agency cost (initial construction and M&R activity costs), user cost (vehicle delay, vehicle operating, accident costs), and salvage value (residual value associated with remaining life). Vehicle delays and accident costs are unlikely to vary between alternative pavement designs during construction, maintenance and rehabilitation operations. The differential residual value between them is generally not very large and has little effect on the LCC results when discounted over 35 years. However, vehicle operating costs can vary due to the vehicle-pavement interaction such as HDM-4 and MOVES models during normal operations (EPA, 2014; Rodrigo, 2008). Most LCCA studies related to selecting an optimal pavement design are formulated to couple the initial construction cost with different M&R strategies during the life-cycle analysis period. In road designs with different initial construction costs, an optimal M&R strategy is determined for each road design in consideration of agency cost, user cost, and salvage value, which are all associated with the deterioration of pavement conditions over time. Therefore, the M&R strategy selected through the LCCA does not reflect the environmental cost in association with carbon emissions. For example, Wang and Wang (2017) found that the LCCs of major and minor overlay treatments also vary depending on the difference in pre-overlay surface distress index by conducting LCCA process. However, it did not reflect the environmental impact of the two treatments in the LCCA process. Wang et al. (2016) emphasized the consideration of environmental factors as well as economic factors when selecting the optimal design alternative for airport pavement which carries much heavier wheel loads than typical highway pavements. Chen and Wang (2018) conducted an LCA study on a runway rehabilitation project using recycled asphalt to

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reflect temporal (life-cycle) and environmental (changes in CO2 emission) aspects. Similarly, Shi et al. (2018) applied the economic input-output LCA approach to evaluate the economic and environmental benefits of portland cement concrete (PCC) using reclaimed asphalt pavement as an aggregate replacement for pavement applications. Therefore, LCA method considering environmental factors can have different results from LCCA and is well suited for selecting sustainable design alternatives in the pavement sector. One goal of this study is to reflect this point and measure the impact of the environmental cost and user cost components on the choice of the most optimal M&R strategy in terms of cost and carbon emissions under varying ECRs. To do so, a semi-hypothetical highway case in Korea is examined with three different long-term M&R strategies. The national highway agency needs to delve into how the two cost components affect the selection of an M&R strategy with varying carbon prices during the life-cycle analysis period to effectively mitigate carbon emissions in pavement construction and management. 2. Research method An extensive survey of LCA studies was done to use a hybrid of LCCA and LCA methods to determine the optimal M&R strategy. The ECR was varied for a life-cycle analysis period to examine alternative M&R strategies. The research flow is shown in Fig. 1 and was devised to determine an optimal M&R strategy for one case study setting by taking into account the major cost components of different M&R scenario plans (M&R cost, vehicle operating cost, time delay cost, accident cost). The major components are the LCC of each scenario plan and the environmental costs (EC). EC was calculated by applying ECR to carbon emissions and fuel

consumption from M&R scenario plans. Since all three M&R scenario plans were applied to the same pavement segment, the costs incurred in the construction phase and previous phases are not included in the LCC. User costs include vehicle operating costs (VOC) and traffic delay costs (TDC). ECR was applied to carbon emissions from equipment and materials used in maintenance operations and from vehicle operations during the life-cycle analysis period. Traffic accident costs (AC) are divided into human injury, property damage, and administrative costs. The present worth cost (PWC) method was used to determine an optimal M&R scenario plan while considering carbon emissions. The case study requires project inputs such as geometric data, road type, environmental data, traffic data, and two decision support models: a pavement deterioration model and M&R decision model. These are used to determine the LCC and produce an optimal M&R strategy, as shown in Fig. 1. Three specific M&R scenario plans are developed to achieve the study goal by using an M&R decision model, pavement deterioration model, and three M&R strategies that are generally applicable in pavement management practices. The pavement deterioration model is used to determine the timing of M&R activities by indicating pavement conditions (a triggering condition value). It is formulated by using the Korea Pavement Research Program (KPRP) pavement design software. The M&R decision model is used to recommend an appropriate M&R activity based on given pavement conditions. 3. Case study 3.1. Goal and scope definition During the G&S definitions in LCA study according to ISO 14040, the aim and scope of the LCA study must be defined. This includes

Variable

Fig. 1. The cost components of the proposed hybrid of LCCA and LCA approach and overall data flow.

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alternative scenario consideration, FU, and system boundary. The case study focuses on an existing highway section with three different potential M&R scenario plans with detailed M&R information (i.e., timing and method) to identify the most economical strategy. The Kyeongju-Guncheon highway section of the Kyungbu highway in Korea was selected for the study. This section serves as the main artery connecting the two major export ports of Incheon and Busan, the Seoul metropolitan area, and the Yeongnam industrial area. This section has a large carbon footprint due to the large portion of medium and large-sized vehicles that transport freight from Busan port, one of the top five major ports in the world. Table 1 shows the G&S definitions elements for the detailed highway pavement section. The table illustrates two FUs (the length and AADT) and the SB (40 years of pavement temporal scope) to compare the M&R scenario plans. This study assumes that over the next 40 years, the increasing rate of AADT from 2016 onward follows an exponential model, which was developed from AADT data of the last five years (2011e2015) provided by Kim et al. (2016). 3.2. Decision support models 3.2.1. M&R decision model To evaluate the pavement conditions of the national highway in Korea, the Korea Institute of Construction Technology (KICT) developed the National Highway Pavement Condition Index (NHPCI), as shown in Eq. (1).

NHPCI ¼

1 ð0:33 þ 0:003  XC þ 0:004  XR þ 0:0183  XIRI Þ2 (1)

where XC : Crack index (%), XR : Rutting index (mm) XIRI : International roughness index (m/km). The index is a function of three independent variables: cracks, permanent deformation, and the international roughness index (IRI). The index was developed through the statistical analysis of field data collected by a professional survey team consisting of representatives of the National Highway Administration, industry, and academic and research institutes (MLTM, 2011). The NHPCI is currently used for the maintenance of the expressways in Korea as a representative pavement performance model. The left two columns in Table 2 present the NHPCI level and the most representative maintenance works (Son et al., 2013), such as patching, milling, and overlay.

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3.2.2. Pavement deterioration model (KPRP) This study uses three pavement deterioration sub-models to determine the NHPCI: one for fatigue cracks, one for permanent deformation, and one for IRI, which all are outcomes of KPRP software (April 2016 version). Until 2010, the American Pavement Design Method (AASHTO Pavement Design Method) was widely used in Korea before its own pavement design method was developed. The Ministry of Land, Infrastructure, and Transport (MLIT) developed KPRP, which is software for pavement design and performance estimation that reflects domestic characteristics such as regional, materials, traffic, and environmental factors. In Korea, most related research has used HDM-4, which was developed by the World Road Association (PIARC) for pavement design and performance estimation. Han et al. (2007) conducted an LCC study that included maintenance costs, user costs, and socioenvironmental costs to suggest an optimum road pavement maintenance standard using HDM-4 and RealCost. Kim (2011) selected an optimal maintenance alternative based on an economic analysis and carbon emissions from vehicle operation using HDM-4 and confirmed the importance of environmental costs. This research is unique in that for the first time, KPRP logics were applied to model pavement deterioration and estimate carbon estimations, which eliminates the inconvenience of having to adjust coefficients to reflect domestic characteristics. The first design process of the asphalt concrete pavement using KPRP involves selecting a pavement section that meets the conditions of the construction site and then inputting variables related to traffic volume, environmental conditions, and material properties. The second process is calculating the structural behavior of the pavement section and established performance criteria of pavement performance models such as fatigue crack, permanent deformation, and IRI models using the structural analysis program module of KPRP. The performance analysis module can be used to estimate pavement conditions through performance models that are developed based on the accumulated damage over the entire design period. The process from input value selection to performance evaluation is repeated until the corresponding design section meets the performance criteria (MLTM, 2011). Fig. 2 (a) shows the predicted crack index, IRI, and permanent deformation over the next 40 years derived from the KPRP simulation given the G&S definitions for the KyeongjueGeoncheon highway pavement section (Table 1). The NHPCI estimation model in Fig. 2 (b) was developed using the three performance models in Fig. 2 (a) and Eq. (1). NHPCI can determine the type and timing of maintenance works over 40 years according to the criteria (the first and second columns) presented in Table 2. NHPCI reaches 9.18 when the three values of the variables are assumed to be zero (perfect pavement conditions). However, in a real situation, the

Table 1 G&S definitions for Kyeongju e Geoncheon highway pavement section. Highway Name

Kyungbu

Section (Location No.)

Kyeongju-Geoncheon (107)

Length (km)

10.4

Speed limit (km/h)

100

No. Lines

4

AADT by Year 2011

2012

2013

2014

2015

40,443

41,269

41,360

41,905

42,808

Table 2 € m, 2001). NHPCI levels in Korea expressway corporation (Son et al., 2013; Wallman and Åstro NHPCI level

Maintenance works (pavement condition)

Friction interval

Accident rate

9.18 ~ 6.0 6.0 ~ 5.5 5.5 ~ 4.0 4.0 ~ 0.0

No maintenance (very good) Preventive maintenance (good) - Patching Overlay (fair) - milling and overlay Rehabilitation (poor)

0.35e0.44 0.25e0.34 0.15e0.24 <0.15

0.2 0.25 0.55 0.8

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a) Crack index, IRI and permanent deformation models

b) NHPCI prediction model

Fig. 2. NHPCI prediction model by using three sub-models from KPRP.

most optimistic NHPCI value is 8.1 because the best or initial value of IRI is normally assumed to be 1.18 m/km. 3.3. M&R scenario plans As shown in Fig. 3, three M&R scenario plans with detailed timing information on M&R activities were derived using three representative M&R strategies that are currently practiced in Korea, as well as previously developed decision support models (M&R decision model and NHPCI prediction model). In this study, it is assumed that the performance of the road reaches the highest level

when two maintenance methods such as patching, milling and overlay are applied. The road life extension was determined through KPRP analysis. Plan 1 in Fig. 3(a) applies patching maintenance work in year 10, when the NHPCI deteriorates to 6.0 according to the NHPCI level information in Table 2. Another prediction simulation with KPRP is conducted to determine the next time point when other patching maintenance work is applied to the maintenance section. In the 19th year, when NHPCI reaches 6.0 points, the patching maintenance method is applied again. By repeating the procedure, plan 1 is an M&R strategy that applies patching maintenance work for a total of four times in years 10, 19,

(a) M&R scenario plan 1 –Patching at years 10, 19, 27,34.

(b) M&R scenario plan 2 – Milling and Overlay at year 30.

(c) M&R scenario plan 3 – Patching at year 10 and Milling and Overlay at year 35. Fig. 3. Three M&R scenario plans for the case study.

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27, and 34 throughout the 40-year period. Fig. 3(b) shows the second M&R scenario plan, which applies milling and overlay work once at year 30 when NHPCI drops to a terminal serviceability number (4.0) according to the Table 2. Fig. 3(c) shows the last M&R scenario plan, in which patching repair work is applied at year 10, and milling and overlaying is applied at year 35 through NHPCI prediction simulation with KPRP.

3.4. Carbon emission cost 3.4.1. Pavement conditions, vehicle speed, and energy consumption relationship To calculate the user cost and environmental cost, the relationship should be clarified between the change of the vehicle speeds according to the pavement conditions and the fuel consumption. This is necessary because the vehicle speed decreases and the fuel consumption changes as the pavement conditions deteriorate. This study used the fuel consumption study results proposed by the Ministry of Land Transport and Maritime Affairs (MLTM, 2010) to estimate user costs associated with carbon emissions, including VOC, TDC, AC, and environmental costs (EC). Fig. 4 shows the fuel consumption as a function of speed for various types of vehicle types (passenger, trucks, cargo, trailer). Overall, the fuel consumption decreases until the speed reaches around 50e70 km/ h, but the opposite tendency occurs when the speed is greater.

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3.4.2. Carbon emission estimation method The 2006 IPCC guidelines are a significant step forward in the production of high-quality estimates of emissions and removals of greenhouse gases. The emission sources are grouped into the following sectors: energy, industrial processes, product use, agriculture, land use and forestry, and waste. Estimation methods vary from Tier 1 to Tier 3 depending on the variables used and targeted accuracy. Tier 1 is the most basic method to use energy consumption and carbon emission factor with different fuel types, and Tier 2 uses an additional emission factor for different vehicle types and emission control technology in addition to fuel type. Tier 3 is a method of applying a vehicle emission factor according to the moving distance for different vehicle types (IPCC, 2006). The Tier 3 method was used to measure carbon emissions because it can reflect carbon emissions from road pavement conditions (Eq. (2)).

Emission ¼

X

Fuela;b;c;d  EFa;b;c;d

a;b;c;d

where Emission (Kg): Emission amount Fuel: Fuel consumption (TJ) EF: Emission factor (Kg/TJ) a: Fuel type (gasoline, light oil, LPG, etc.) b: Vehicle type

a) Passenger and small truck

b) Medium truck

c) Heavy truck, cargo, trailer Fig. 4. Fuel consumption according to vehicle type (MLTM, 2009).

(2)

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J.-h. Choi / Journal of Cleaner Production 209 (2019) 88e100

c: Discharge control technology (no control device, catalyst change device, etc.) d: Driving conditions (road type)

3.4.3. Effective carbon rate The ECR estimation is applied to fuel consumption and M&R scenario plans to convert carbon emissions into monetary terms. An accurate ECR is difficult to estimate due to uncertainties in climate change and economic forecasting. Alberici et al. (2014) suggested that the appropriate carbon price to cope with effective climate change is at least 30 euros (31.86 USD) to 50 euros (53.11 USD) per ton of CO2. Smith and Braathen (2015) also estimated the climate cost of 38 euros (50 USD) per ton of CO2. These studies imply that ECR must be set at 30 euros (53.11 USD) or more to be effective in reducing CO2 emissions. The ECR value is much higher than the average ECR (14.4 euros or 17.2 USD) of 41 countries and the ECR (14.9 euros or 17.87 USD) of Korea. The difference between the suggested ECR and current ECR used in the 41 countries indicates a carbon pricing gap that can be used for measuring the appropriateness of the current ECR level (Academic issues, 2017). 3.5. LCC calculation LCCA is the most commonly applied technique in civil engineering to determine the best alternative out of a small set of mutually exclusive alternatives. Present worth analysis is conducted for cash flow associated with each alternative, and the best project is chosen using the maximization of net benefits as a criterion. If all projects deliver the same benefit, the criterion is reduced to finding the alternative with the minimum PWC. To fairly compare and evaluate the various alternatives, care must be taken in setting the analysis period. There are many ways to determine the analysis period, but the most common method for the infrastructure is to keep the analysis period longer and to ignore the residual value, which has little effect on decision making because it is continuously discounted over such a long analysis period (Revelle et al., 2004). The FHWA's LCCA policy report explains that the analysis period used in LCCA should be long enough to capture the long-term difference of the discounted LCCs among competing alternatives and recommends an analysis period of at least 35 years (USDOT, 2002). Furthermore, most research papers covering the topic of pavement LCA used an analysis period of 40 years (Azarijafari et al., 2016). Thus, this study applies an LCCA approach (i.e., PWC determination) using a pavement life-cycle of 40 years to fairly compare LCCs among different M&R scenario plans. 3.5.1. Agency costs Agency costs are all costs incurred by the highway agency over the life-cycle of the highway project, including all the costs related to engineering, contracting, initial construction, supervision, M&R, and salvage (Walls and Smith, 1998). M&R costs (MRC) of the

highway during the M&R phase include machinery cost, labor cost, material cost, and others related to M&R activities, such as overlay and patching repair. The salvage value represents the remaining value at the end of the analysis period, which has little effect on the LCCA results when discounted over 35 years. Cost components related to engineering and construction are assumed to be identical among all M&R scenario plans and therefore cancel each another out in the LCC analysis results. Therefore, among agency cost components, only MRC is considered for determining the best carbon-efficient M&R strategy. Table 3 shows the costs of the patching method (1411.85 USD/50 m2) and milling and overlaying method (32,080.3 USD/2000 m2). These were determined using cost data published by the Korea Price Association (2011) and by applying the data on the national construction cost index, a monthly measure of construction cost movement published by KICT (2017). 3.5.2. User costs User costs (UC) are accrued by the users of a roadway section during maintenance or rehabilitation and everyday use of a roadway section including VOC, TDC, and AC. UC components correlate with pavement deterioration. The costs of vehicle operation are the costs incurred by driving vehicles, including fuel consumption and engine oil costs. This cost varies with the type, speed, and weight of the vehicle, the amount of traffic, and maintenance interventions. VOC is calculated from the total fuel consumption of the vehicle, which is determined by the varying fuel consumption (l/km/unit) (Fig. 4) multiplied by the section length (km) and the number of vehicles during the LCC analysis period. VOC decreases time as vehicle operation speed decreases due to deteriorating pavement conditions. However, VOC increases immediately after the completion of a maintenance activity. Fig. 5 shows VOC, TDC, AC, and UC, which is the sum of the three components for each M&R scenario plan for 40 years. In this study, the following assumptions were applied to map the pavement performance to varying vehicle speeds. 1) The actual maximum speed of the highway is 100 km/h, and the speed of the nearby vehicle is 65 km during the maintenance work. 2) When the road performance is poor (e.g., NHPCI 4.0e5.5), the vehicle speed deceleration is higher than when the performance is relatively good (e.g., NHPCI 6.0e9.18). And 3) there is more vehicle speed reduction before the second maintenance period than before the first maintenance period. Under these assumptions, the average vehicle speed change is appropriately calculated and applied every year. There are two types of traffic delay time resulting from either deteriorating pavement conditions or pavement maintenance interventions. The Korea Development Institute (KDI, 2008) calculated the value of time that is assessed based on the wage level of passengers, the route of use, the choice of means of transport (e.g., passenger cars, buses, and trucks), and the value of cargo time. Walls and Smith (1998) developed methods and formulas to calculate the time delay caused by deteriorated road conditions and maintenance work. This study also assumes that when one lane is

Table 3 Basic unit maintenance cost for pavement M&R activities. Section

Maintenance Volume (m3)

Asphalt Density (kg/m3)2)

Asphalt Volume Asphalt Price (USD/ Material Cost (Ton) Ton)2) (USD)

Equipment & Labor Cost (USD)

Unit Maintenance Cost (USD)

Patching (50m2)1) Milling & Overlay (2,000m2)1)

2.5 200

2243 2243

5.6 448.6

1068.6 4621.9

1411.85 32,080.30

Note: 1 Thickness of asphalt in pavement: Patching (0.05 m), Milling and Overlay (0.1 m). 2 Asphalt Density, Asphalt Price (Aqua-calc conversions and Calculation, 2017).

61.2 61.2

343.23 27,458.4

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a) Vehicle operating costs (VOC)

b) Traffic delay costs (TDC)

c) Accident costs (AC)

d) User costs (UC = VOC+TDC+AC)

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Fig. 5. User costs for the study area.

blocked due to maintenance work, the speed of the other lanes is 65 km/h. Fig. 5 (b) shows that as the road conditions deteriorate over time, the speed of the vehicles decreases while the cost of the time delay increases. During the M&R phase, the cost of time delay

P Accident CostðnÞ ¼

n N:

components: pavement section extension, annual average traffic volume, annual accident cost, and accident rates in relation to pavement conditions. This study proposes a formula that takes these factors into consideration:

 of accidentsðnÞa;b;c;d  Accident costðnÞa;b;c;d  Length  AHTVðnÞ  Accident rate 1; 000; 000

increases to the maximum level due to operations in work zones causing traffic jams. After the maintenance is completed, the time delay cost is reduced as the traffic state returns to normal. 3.5.3. Traffic accident costs This section summarizes the cost arising from traffic accidents based on statistics related to the AC estimation. Traffic accident costs are generally divided into human injury costs, material damage costs, and administrative costs (Sim et al., 2016). An analysis of the literature on traffic accident costs has led to a list of factors affecting accident cost estimation, including three cost

(3)

where N. of accidents(n)a, b, c, d: Number of deaths (a), serious injuries (b), minor injuries (c), and reported injuries (d) in year n Accident cost(n)a, b, c, d: Accident cost per person per 100 million driving distance (km) in year n Length: Extension of pavement section AHTV(n): Average hourly traffic volume (car/hour) at year n Accident rate: Number of traffic accidents/1,000,000 vehicles/ km.

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€ m (2001) With regard to accident rate, Wallman and Åstro classified pavement conditions into four levels by measuring the coefficient of friction of roads and accident rates through a statistical analysis of traffic accidents. The accident rate is number of personal injuries per million vehicle kilometers at different friction intervals. This study explains the difficulty of explaining the relationship between friction and accident rate because many other different factors e in addition to the friction e affect the driver behavior, which is of great importance for characterizing the accident rate. The author uses the relationship between four-level friction intervals and accident rates assuming that the highway performance NHPCI level is also directly related to the friction intervals. Table 2 shows the relationship between the pavement condition index (NHPCI) and traffic accident rate (number of traffic accidents/ 1,000,000 vehicles/km). AC was calculated by taking into account the accident rates in Table 2 and AC data provided by Sim et al. (2016). As an example, Fig. 3 (b) uses an accident rate of 0.2 for NHPCI levels of 9.18 to 6, 0.25 for 6.0 to 5.5, and 0.55 before the start of milling and overlay in a 30-year period when the milling and overlay starts. Fig. 5 (c) shows the calculated accident cost of the three M&R scenario plans over 40 years.

3.6. EC calculation EC includes carbon emissions from construction materials and equipment operations in maintenance works during the M&R phase and from vehicle operations for 40 years. The amount of carbon emissions produced from vehicle operations can be estimated using the total fuel consumption from the annual vehicle operation and the carbon emissions by vehicle type with Eq. (2). For the estimation of carbon emissions from construction equipment (road surface rippers, asphalt finishers, loaders, machine loaders, tire roller, etc.), information is required on M&R activities and

equipment-related factors, such as emissions factors, productivity, specifications, and operating hours. Table 4 shows the use of materials and carbon emissions from construction equipment operation for the two maintenance activities: patching repair (50 m2) and milling and overlay (2000 m2). The material and equipment used for patching repair and the milling and overlay emit 0.5939 tCO2-eq/year and 4.9529 tCO2-eq/ year, respectively. The carbon emissions from the use of materials in column 8 of Table 4 are determined based on the GHG emissions per unit area (T/m2) given by Chehovits and Galehouse (2010). This study assumes that all vehicle types are driven by diesel. A net diesel calorific value (Toe/lit) of 0.842 and the carbon emission factor of 0.837 (tCO2/Toe) are applied, as suggested in the IPCC guidelines (IPCC, 2006). The replacement area of the patching repair works was assumed to be 20% of the entire road section (10.4 km  3.6 m  4 lines ¼ 149,760 m2) in alternative 1. Once the patching repair work is carried out, the carbon emissions will be 149,760 m2  0.2 percent  0.5939 tCO2/50 m2 ¼ 355.77 tCO2-eq. Likewise, the milling and overlay work emits 149,760 m2  4.9529 tCO2/ 2000 m2 ¼ 370.87 tCO2-eq. Thus, the unit carbon emission intensity per maintenance method was estimated to be 11.878 kgCO2-eq/m2 for patching repair work and 2.476 kgCO2-eq/m2 for milling and overlay work. Fig. 6 shows carbon emissions from vehicle operations and the effects of construction in years 10, 19, 27, and 34 for plan 1 (97,637.3 tCO2-eq at year 10, 114,253.6 tCO2-eq at year 19, 124,125.2 tCO2-eq/ year at year 27, and 135,265.3 tCO2-eq at year 34). For plan 2, 128,265.5 tCO2-eq was calculated by considering the effects of milling and overlay work at year 30 and carbon emissions from vehicle operation. For plan 3, 97,637.3 tCO2-eq was calculated from patching work at year 10 and 137,632.2 tCO2-eq was calculated from milling and overlaying work at year 35. The carbon emission trends for each scenario show a sharp increase in the projection

Table 4 Basic unit of carbon emissions from material and equipment in Milling and Overlay (2,000 m2) and Patching (50 m2). Section

Equip. and materials

Milling and Overlay Surface ripper (2,000m2)1) Loader (tire) Asphalt finisher Macadam roller Tire type roller Tandem roller Sprinkler truck Asphalt

Patching (50m2)1)

Standard Fuel Performance (l/h)2)

Time Total Maintenance work (h) Fuel (l) Area (m2)

GHG Emissions for Asphalt (T/m2)

Emission Factor Net Heating Value (t CO/Toe) Factor (Toe/lit)

CO₂ Emissions (tCO2)

2.0M

109

8

872

e

e

0.837

0.842

2.253

0.57 m3 3m

7.6 13.2

8 8

60.8 105.6

e e

e e

0.837 0.837

0.842 0.842

0.157 0.272

10-12 9.5 TON 8-15 TON 11.7

8

76

e

e

0.837

0.842

0.196

8

93.6

e

e

0.837

0.842

0.2418

5e8 TON 9.2

8

73.6

e

e

0.837

0.842

0.1901

16,000l

8

94.4

e

e

0.837

0.842

0.243

2000

0.0007

e

e

1.4

e

e

0.837

0.842

4.9529 0.031

e

e

0.837

0.842

0.2976

e e 50

e e 0.00014

0.837 0.837 e

0.842 0.842 e

0.1343 0.124 0.007

11.8

Hot Mix e e e Asphalt Unit CO₂emissions (tCO2-eq) of Milling and Overlay Plate 1.5 TON 1.5 8 12 compactor Vibrating 0.7 TON 14.4 8 115.2 roller 3 6.5 8 52 Loader (tire) 0.57 m Dump truck 2.5 TON 6 8 48 Asphalt Hot Mix e e e Asphalt Unit CO₂emissions (tCO2-eq) of Patching

Note: 1 Thickness of asphalt in pavement: Milling and Overlay (0.1 m), Patching (0.05 m). 2 Fuel: diesel.

0.5939

J.-h. Choi / Journal of Cleaner Production 209 (2019) 88e100

97

the three scenarios over the 40 years was 0.15% for MRC, 71.85% for VOC, 13.28% for TDC, 11.12% for AC, and 3.66% for EC. Fig. 8 (a) shows the PWC for each scenario plan, and Fig. 8 (b) shows the increasing trend of PWC with varying carbon trading prices. As shown in Fig. 8 (a), the PWC of scenario 1 is the smallest, followed by scenario 2 and scenario 3. In Fig. 8 (b), the PWC of the scenario 1 is smaller than that of scenario 2 in the range where the carbon trading price is less than 42.27 USD, but the relationship is reversed within the range larger than the transaction price, as shown in Fig. 8 (b). The most economical scenario from the LCC point of view is scenario 1, but when considering the EC, as shown in Fig. 8 (b), scenario 2 is the most economical choice for the national highway agency at a carbon trading price of 42.27 USD/ton and above. 4. Discussion and conclusion Fig. 6. Carbon emissions for use and M&R phases.

curves, which is mainly due to the addition of carbon emissions from construction materials and equipment operation in each maintenance work applied during the M&R phase. The cumulative carbon emissions of each scenario plan over the 40 years are 4,113,625 tCO2-eq for scenario 1, 3,150,236 tCO2-eq for scenario 2, and 3,340,536 tCO2-eq for scenario 3. 3.7. PWC of LCC and carbon emissions The OECD proposes that the appropriate carbon trading price range to effectively control carbon emissions is from 31.86 to 53.11 USD per ton of CO2. Table 5 shows the PWC including AC, UC, and EC per scenario during the 40-year life-cycle analysis period for the Kyeongju-Guncheon highway section. The PWC was determined using a discount rate of 4.5%, which was recently adjusted from 5.5% to meet the changes in economic and financial conditions of low growth and low interest rates in Korea (MOEF, 2017). As mentioned earlier, carbon emissions are expressed as monetary values by multiplying the carbon trading value by the quantity of carbon emissions. The carbon trading price in Korea is 17.87 USD/ ton as of Jan 19, 2018. Fig. 7 shows the cost items for each alternative during use and M&R phases at a carbon trading price of USD 31.86. Scenario 1 has the highest MRC (0.18%), VOC (83.02%), and EC (4.14%). In scenario 2, TDC (18.65%) and AC (13%) are the highest, while other cost components are the lowest among the alternatives. Scenario 3 shows intermediate cost levels across all cost components among the alternatives. In scenario 1, the performance of the pavement is kept in good conditions at all times because short-term patching repair works are conducted repeatedly. In scenario 2, milling and overlay work is carried out at 30 years, and there is high TDC and AC due to low pavement performance for a long period of time, although VOC is low. The VOC of each scenario is inversely related to the TDC and is proportional to the EC. The average cost of each of

The future management environment of the national highway agency is expected to be further aggravated by the increase in maintenance costs and environmental costs due to aging infrastructure over 30 years. Finding ways to reduce LCC and EC is a major mission for the national highway agency. The main goal of this study was to select the best alternative through a hybrid of LCCA and LCA calculations for three representative M&R strategies at the O&M phase. This was done using KPRP pavement design software, which reflects Korea's environmental conditions and vehicle characteristics. With regard to the estimation of the EC of road pavement, many studies have been conducted on calculating the emissions from material production, vehicle operation, construction, and operation of equipment during the construction phase (Kwak et al. 2012, 2015; Huang et al., 2016; Ma et al., 2016; Meneses and Ferreira, 2013; Seo and Kim, 2013a, b; Seo et al., 2016). However, there is a significant lack of research on the carbon emission determination in relation to different M&R strategies in the pavement management domain. In particular, there are very few studies that take into account EC for carbon emissions and the impact of EC on the economics of road pavement maintenance. The results of this study are as follows. 1) The unit carbon emission intensity per maintenance method was 11.878 kgCO2-eq/m2 for patching repair work and 2.476 kgCO2-eq/m2 for milling and overlay works. 2) The cumulative carbon emissions of each scenario over 40 years were 4,113,625 tCO2-eq for scenario 1, 3,150,236 tCO2-eq for scenario 2, and 3,340,536 tCO2-eq for scenario 3. Scenario 2 involved milling and overlay maintenance activity at year 30 and had the lowest cumulative carbon footprint. 3) With the emission price at 31.86 USD/ton, scenario 1 had the lowest PWC and was the best M&R strategy. However, if future climate change becomes even more serious and the government increases carbon dioxide trading costs to curb carbon dioxide emissions, the second scenario becomes the best alternative.

Table 5 Cost components and PWCs for alternatives (CO2 price 31.86 USD, discount rate 4.5%, analysis period 40 years) (unit: million USD). M&R Scenarios

Agency costs (AC)

User costs (UC)

Maintenance & Rehab. costs (MRC)

Vehicle operating costs (VOC)

Traffic delay costs Accident costs Material, equipment (TDC) (AC) emission costs

Vehicle emission @31.86 costs USD/ton

@ 53.11 USD/ton

2007.4 2011.1 2047.3 2021.9

93.06 128.1 136.6 118.9

129.3 102.8 108.4 113.5

442.07 440.18 448.43 e

Scenario 1 3.56 Scenario 2 2.30 Scenario 3 3.19 Average 3.02

Environment costs (EC)

154.9 256.5 220.4 220.4

0.045 0.011 0.024 0.031

Present worth costs (PWC)

423.84 425.66 433.14 e

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J.-h. Choi / Journal of Cleaner Production 209 (2019) 88e100

a) M&R Scenario 1

c) M&R Scenario 3

b) M&R Scenario 2

d) Average Cost Composition

Fig. 7. Cost ratio of each M&R scenario plan during use and M&R phases.

a)

PWCs of life-cycle cost for each scenario (@17.87 USD/ton)

b) PWCs of LCC plus EC for each scenario

Fig. 8. PWCs of each M&R scenarios over varying emission price.

The EC increases in direct proportion to the price of carbon credits, and the lowest occurs in scenario 2. Scenario plan 2 is therefore considered to be better than the other plans in terms of LCC when considering EC as the price of emission credits increases. However, it has a disadvantage in that user satisfaction could decrease because low-performance road conditions last for a long time. Therefore, it is necessary to develop a different M&R strategy that can reduce the EC while maximizing user satisfaction. This study is also meaningful in that it has developed a hybrid approach of using LCCA and LCA to determine the optimal M&R strategies while considering EC related to carbon emissions in road facilities. The method could be applied to derive the most carbon efficient maintenance strategy for other types of infrastructure.

The limitation of this study is that it didn't consider the uncertainties related to highway performance prediction and future traffic estimation. This study presented the results of LCCA and LCA using an exponential traffic estimation model. At the same time, the results of applying linear traffic estimation model, which was not described in this article, shows no much difference, but there was a slight difference in the accumulated carbon amount and the LCA value per scenario plan. It is advisable to conduct a sensitivity analysis, including Monte Carlo analysis, on the varying pavement performance and traffic estimation values to improve decision making when determining the optimal M&A strategy. In addition, carbon price alone may not be enough to indicate an irreversible environmental degradation due to carbon dioxide emissions. However, as carbon prices have risen in recent years particularly in Korea, many companies are struggling to operate

J.-h. Choi / Journal of Cleaner Production 209 (2019) 88e100

their businesses by investing more in modernizing facilities and securing carbon credits. From the point of view of the national highway agency and the private investment road management company, there is a need to make various efforts to reduce carbon emissions associated with the M&R work for the financial stability of the company. This study is the first international research effort linking long-term pavement performance and carbon emissions to provide a framework for determining carbon-efficient road-maintenance strategies. Acknowledgement This research was supported and funded by the Basic Science Research Program through the National Research Foundation of Korea and funded by the Ministry of Education (2016R1A6A1A03012812). References Academic issue, 2017. OECD Announcement of the Report on the Appropriateness of Carbon Rates. http://www.energy.or.kr/web/kem_home_new/energy_issue/ mail_vol54/pdf/issue_157_01.pdf (accessed by: 09/2017). Alberici, S., Boeve, S., Breevoort, P.V., et al., 2014. Subsidies and Costs of EU Energy. Final Report and Annex 3. Ecofys. https://ec.europa.eu/energy/sites/ener/files/ documents/ECOFYS%202014%20Subsidies%20and%20costs%20of%20EU% 20energy_11_Nov.pdf (accessed by: 05/2018). Aqua-calc Conversions and Calculation, 2017. Weight to Volume Conversion. http:// www.aquacalc.com/page/density-table/substance/concrete-coma-and-blankasphalt (accessed by: 01/2018). Azarijafari, H., Yahia, A., Amor, M.B., 2016. Life cycle assessment of pavements: reviewing research challenges and opportunities. J. Clean. Prod. 112, 2187e2197.  et al., 2013. Applying life cycle nchez, G., Berzosa, A., Barandica, J.M., Fern andez-Sa thinking to reduce greenhouse gas emissions from road projects. J. Clean. Prod. 57, 79e91. https://doi.org/10.1016/j.jclepro.2013.05.036 (accessed by: 05/2017). Batouli, M., Mostafavi, A., 2015. Assessment of network-level environmental sustainability in infrastructure systems using service and performance adjusted lifecycle analysis. In: Canadian Society for Civil Engineering's 5th International/ 11th Construction Specialty Conference. Vancouver, Canada. Chehovits, J., Galehouse, L., 2010. Energy usage and greenhouse gas emissions of pavement preservation processes for asphalt concrete pavements. In: Compendium of Papers from the First International Conference on Pavement Preservation, pp. 27e41. Chen, X., Wang, H., 2018. Life cycle assessment of asphalt pavement recycling for greenhouse gas emission with temporal aspect. J. Clean. Prod. 187, 148e157. EPA, 2014. Greenhouse Gas and Energy Consumption Rates for On-road Vehicles: Updates for MOVES2014. https://nepis.epa.gov/Exe/ZyPDF.cgi? Dockey¼P100NNUQ.pdf (accessed by: Sep. 2018). €sser, F., Wallbaum, H., 2013. Life cycle assessment of representative Swiss Gscho road pavements for national roads with an accompanying life cycle cost analysis. Environ. Sci. Technol. 47, 8453e8461. Han, D.S., Do, M.S., Kim, S.H., Kim, J.H., 2007. Life cycle cost analysis of pavement maintenance standard considering user and Socio-environmental cost. J. Kor. Soc. Civ. Eng. 27, 727e740. Huang, Y., Ning, Y., Zhang, T., Wu, J., 2016. Measuring carbon emissions of pavement construction in China. Sustainability 8, 723. Inyim, P., Pereyra, J., Bienvenu, M., Mostafavi, A., 2016. Environmental assessment of pavement infrastructure: a systematic review. J. Environ. Manag. 176, 128e138. IPCC, 2006. IPCC Guidelines for National Greenhouse Gas Inventories, Chapter 4. https://www.ipcc-nggip.iges.or.jp/public/2006gl/vol2.html (accessed by: Dec 2018). ISO 14040, 2006. Environmental Management-life-cycle Assessment-principles and Framework. International Organization for Standardization: Gevena, Switzerland. ISO 14044, 2006. Environmental Management-life-cycle Assessment-requirements and Guidelines. International Organization for Standardization: Gevena, Switzerland. Jin, M.J., Shi, X., Emrouznejad, A., Yang, F., 2018. Determining the optimal carbon tax rate based on data envelopment analysis. J. Clean. Prod. 172, 900e908. KDI, 2008. Modification and Supplementary Study of Preliminary Feasibility Study Standard Guidelines for Road and Railway Sector Projects. Korea Development Institute. KICT, 2017. Construction Cost Index on Nov, 2017. http://cost.kict.re.kr/Notice/NC02/ (accessed by: 12/17). Kim, K., 2011. Economic Analysis and CO₂Emission with HDM-4 Road Maintenance Method. Hanbat National University Library. Kim, J.Y., 2016. Future transportation SOC maintenance and rehabilitation cost estimation. Mag. Kor. Soc. Road Eng. 18 (4), 10e13. Kim, J.Y., Park, J.H., Oh, J.H., Cho, J.S., 2016. Forecasting Future Transport Demand toward 2050. Basic-rr-16-01. Korea Transport Institute.

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Nomenclature AADT: annual average daily traffic AHTV: average hourly traffic volume

100 AC: agency cost, million USD EC: environmental cost, million USD ECR: effective carbon rate, euro/tCO2 EOL: end of life FU: functional unit, kilometers GHG: greenhouse gases G&S: goal and scope IRI: international roughness index, m/km KPRP: Korea pavement research program LCA: life-cycle assessment

J.-h. Choi / Journal of Cleaner Production 209 (2019) 88e100 LCC: life-cycle cost LCCA: life-cycle cost analysis M&R: maintenance and rehabilitation MRC: M&R cost, million USD NHPCI: national highway performance condition index (0.0e9.18) PWC: present worth cost, million USD SB: system boundary, years TDC: traffic delay cost, million USD UC: user cost, million USD VOC: vehicle operating cost, million USD