Journal of International Logistics and Trade
Jungseok Research Institute of International Logistics and Trade

Estimating socio-economic impact from ship emissions at the Port of Incheon

Young-Tae Chang1, Eunbee Kim1, Ahhyun Jo1, Hyosoo Park1,*
1Graduate School of Logistics, Inha University, Incheon, Korea
*Corresponding author: Graduate School of Logistics, Inha University, 100 Inha-ro, Nam-gu, Incheon, 22212, Korea Email:

© Copyright 2017 Jungseok Research Institute of International Logistics and Trade. This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Received: Mar 18, 2017; Accepted: Apr 02, 2017

Published Online: Apr 30, 2017


Ports create harmful effects on their adjacent population because ships discharge noxious gases like SOX, NOX, and particulate matter (PM). To tackle this problem, some ports started to control emission through regulations such as Emission Control Areas (ECA) and Reduced Speed Zone (RSZ). This paper estimates the social cost of ship emission and eco-efficiency at the Port of Incheon (POI). We further examine how the ECA and RSZ designation can reduce the social cost. The estimation is based on the activity-based approach, where ship type, engine, and movement are used to measure fuel consumption and then emission. Results suggest that the social cost of ship emission at the POI amounts to $90,805,478. The eco-efficiency of the POI, compared to the one at the Port of Las Palmas in another study, is substantially better. Under RSZ, the corresponding emission abatement values are $4,485,308, $2,642,009 and $21,932,435 from SO2, NOX and PM reduction, respectively. If 1.0% and 0.1% sulfur fuel are used complying with rules of the ECA, the social cost savings amount to $8,174,947 and $12,868,842 from SO2 reduction.

Keywords: Port; Vessel emission; Social cost; Eco-efficiency

1. Introduction

Ports play a critical role as an interface between land and maritime transportation. Shipping industry has developed significantly due to containerization, developing intermodal networks, shipping alliance, and increasing vessel size (Bae et al. 2013, Yap and Lam 2006). The resulting reduction in shipping costs led to surge in maritime traffic and international trade (Blonigen and Wilson 2013, Hummels 2007). This could not have been accommodated without corresponding advancement in and support from port operations. In addition, ports generate positive industrial chain effect and value added in regional economy (Chang et al. 2014b). This is why many governments consider ports as strategic nodes and intend to support ports in their jurisdiction to be hubs, and tremendous government subsidies are given to ports.

Still, ports generate harmful effects on their adjacent population because ships discharge many noxious gases like SOX, NOX, and particulate matter (PM). These gases can be dangerous to human, increasing respiratory and cardiovascular diseases. For example, Wang and Corbett (2007) found that PM emission from ships worldwide caused 60,000 death each year. Similarly, Tian et al. (2013) estimated that nickel contained in PM10 has increased emergency hospital visits for cardiovascular diseases by 1.25% in Hong Kong.

Realizing this problem, policy-makers devised regulations to tackle this problem. The International Maritime Organization (IMO), for instance, designated Emission Control Areas (ECA) in Baltic Sea, North Sea, and North America. Ships in ECAs should use fuel with less sulfur contents or equip scrubbers to filter sulfur in the fuel automatically. In North America, some ports introduced Reduced Speed Zone (RSZ), e.g., the Port of Los Angeles/Long Beach since 2001, the Port of New York/New Jersey since 2009. By requiring ship speed below 12 and 15 knots at these ports, ships consume less fuel and consequently less emission. Literature supports that ECA and RSZ could reduce emissions substantially (Chang et al. 2013, Wang and Corbett 2007).

Numerous studies estimated emission from ships in a port level, as reviewed in the next section. The significance of these papers lies on that measuring emission inventory and its associated social cost are vital to monitor pollution and serve as useful guidance for emission control legislation (Hammingh et al. 2007, Wang and Corbett 2007, Watanabe 2004). Relevant papers employed in this area have mostly used a bottom-up approach to improve estimation accuracy, where emission in an individual ship level is aggregated to obtain total emissions. To this end, they used ship movement, engine type, and fuel type data. Lately, some researchers went further to examine the social cost of ship emissions and measure ecological indicators like port revenue or vessel calls per emission (Maragkogianni and Papaefthimiou 2015, Song 2014, Tichavska and Tovar 2015). These papers, however, only focused on European ports not Asian ports. Asian governments in a potential ECA may be more interested in how emission regulations such as ECA or RSZ can decrease social cost.

Against this back drop, this paper estimates the social cost and eco-efficiency of ship emissions at a potential ECA, the Port of Incheon. This paper further examines how introducing ECA or RSZ can reduce the social cost or enhance eco-efficiency in the POI. This study extends Chang et al. (2014a), who estimated emissions in the POI by ship and activity type but did not measure social cost and eco-efficiency. Our main contribution to literature is twofold. First, we calculate the social cost and eco-efficiency of ship emissions in a port level through the bottom-up approach, which were conducted by few studies. Second, this paper assesses benefits of the ECA and RSZ in terms of the emission social cost, which none of existing studies did.

The rest of sections are organized as follows. Section 2 surveys the literature on shipping emission inventory. Section 3 explains methodology to obtain the social cost and eco-efficiency and describes data collection process. Section 4 reports emission estimates, and section 5 concludes.

2. Literature review

Studies that measured emission inventory can be divided into two categories. The first adopts top-down approach (also called fuel-based approach). The method measures emissions using macro-level fuel consumption data. For example, Tzannatos (2010a) examined emissions at Greek ports from both domestic and international shipping. Due to multiple years of port data, they used fuel sales statistics to estimate the emissions. Recently, more studies employed the bottom-up approach. Unlike the top-down method, the bottom-up one requires detailed ship characteristics, engine type, and ship movement data to capture fuel consumption and then emission level. Tzannatos (2010b) measured noxious gases emitted from passenger and cruise ships at the Port of Piraeus, Greece. The method requires shipping movements to be divided into several phases, e.g. maneuvering or at berth, to incorporate difference in fuel consumption and engine usage by phase. Another interesting application of this method is by Liao et al. (2010). They addressed a very specific policy question, ‘what would be the emission reduction benefit by repositioning the current transshipment port to the one closer to major cities in Taiwan?’ Overall reduction was notable under repositioning because it reduced trucking emission significantly. This study, however, overlooked the health damage inflicted to nearby population. Similarly, Park et al. (2007) estimated the emission reduction benefit of the Alameda Corridor, a railway connecting Port of Los Angeles and Port of Long Beach.

Chang et al. (2013) also performed a similar analysis for the Port of Incheon in South Korea. They estimated carbon emissions by different vessel movement phase, e.g., approaching to dock, maneuvering, and hoteling, and further by ship type. In an extension of the previous paper, Chang et al. (2014a) analyzed NOX, SO2 and PM2.5 emissions at the same port. Their main research question this time was not the emission inventory itself but estimating emission reduction potential from such policies as the ECA and RSZ. The findings are surprising: the RSZ could reduce overall emission by 67% and the ECA 93%. Different from the studies thus far, Geerings and Van Duin (2011) measured emissions in port terminals. Cargo movement and land-side emissions from equipment were calculated. Moreover, a counterfactual analysis of replacing low quality fuel with bio-diesel and electrical power is another interesting aspect of the study. These studies, however, only center on emission inventory per se. More important analysis should be to quantify the impact of vessel emission on society that includes human health impact.

An increasing number of studies estimated social cost from emission as well as emission inventory. Song (2014) used a ship movement data at Yangshan Port in China to estimate its social cost and eco-efficiency. To this end, he averaged the social cost estimates of pollutants from several studies. Berechman and Tseng (2012) calculated emissions at the Port of Kaoshiung, Taiwan. Diverse ship types, such as bulk, container, and general cargo ships, were examined. When estimating the social cost of total emission, they used BeTa database that calculates the emission cost factor (i.e., the social cost per emission) in numerous region. McArthur and Osland (2013) investigated ships at berth in Port of Bergen, Norway. They mostly followed the line of previous studies except that they collected the emission cost factor from several sources, including BeTa, and CAFE. Tichavska and Tovar (2015) did more sophisticated analysis on the Port of Las Palmas (PLP) using ‘Automatic Identification System (AIS)’ data. The AIS data enabled them to locate ship movement by minute and distance, undeniably providing most elaborate emission estimates. The cost factor, on the other hand, was taken from previous studies.

Reviewing existing studies, we find that the literature has several gaps. First, the social cost and eco-efficiency of ship emission is less studied. Even though some studies already analyzed them, these mostly focus on European ports. Second, more critically, none of the existing studies to the authors’ knowledge examined benefits of the ECA and RSZ through measuring the social cost of emission.

3. Theoretical model

3.1 Methodology

While we mostly adopted emission inventory results in Chang et al. (2014a), this section briefly summarizes their methodology to help readers’ understanding. Following Chang and Wang (2012), the amount of fuel consumption is measured by

F t r i p , k = { ( M F k S 1 k S 0 k + A F k ) t t r i p if  t r i p { c r u i s i n g , m a n e u v e r i n g } A F k t t r i p if  t r i p = h o t e l i n g

where Ftrip,k is the amount of fuel consumed by vessel k for each phase of trip ∈ {cruising, maneuvering, hoteling}, MFk average daily fuel consumption of a vessel’s main engine, AFk average daily fuel consumption of a vessel’s auxiliary engine, S0k the design speed of vessel k, S1k its operating speed, and ttrip the duration of a ship travel (days).

Next, total emissions can be obtained by multiplying fuel consumption and emission factor, and then summating emissions at each trip type.

E k p g f = t r i p ( F g f , t r i p E F p g f , t r i p )

where Ekpgf is emissions throughout a complete trip of vessel k (tons), Fgf,trip amount of fuel consumed by vessel k, EFpgfm emission factor. Subscript p is the pollutant type (PM, SO2, NOx), f the fuel type (bunker fuel, marine diesel oil/marine gas oil, gasoline), and g the engine type (e.g., slow-, medium-, and high-speed diesel, gas turbine, steam turbine). See Chang et al. (2014a) for detailed data descriptions on fuel type, engine type, and emission factor.

Next, we estimated the social cost of noxious gas emission SCkpgf by

S C k p g f = E k p g f v p

where vp is the cost inflicted by pollutant type p per ton. Then eco-efficiency is calculated through

E c o k p g f = E k p g f i n d i c a t o r

where indicator means divergent measures on port output such as the number of passengers, the number of vessels, and port revenue.

3.2 Social cost factor and eco-efficiency data

Most studies refer to the Clean Air for Europe (CAFE) (Holland et al. 2005, Amann et al. 2005), the New Energy Externalities Development for Sustainability (NEEDS) (Preiss and Klotz 2007) and the Benefits Table (BeTa) databases to obtain the social cost factor of emission. These sources, however, are based on European region and therefore can differ significantly from the actual cost factor in the POI region. As an alternative, we employ results by Lee et al. (2010), who estimated the external cost of emission in Taiwan. This can be justified for two reasons. First, Taiwan is close to Korea, which shares similar geographical characteristics. Second, previous studies, e.g., Preiss and Klotz (2008) and Dragović et al. (2015), used cost factors in other regions that share similar GDP per capita level. In our case, Taiwan and Korea have similar per capita GDP, $22,044 and $27,633, respectively (International Monetary Fund, 2016).

Table 1 summarizes employed external cost factors. SO2, NOX and PM10 cause social cost $13,960, $4,992 and $375,888 per ton respectively. Unfortunately, the factor was not available for PM2.5 from Lee et al. (2010). Thus, BeTa was used to calculate it: $594,042. In a ton basis, the most harmful noxious gas is PM2.5, causing $594,042 external cost per ton.

Table 1. External cost factor by emissions
Noxious gas type External cost factor ($/ton)
SO2 13,960
NOX 4,992
PM2.5 594,042
PM10 375,888
Download Excel Table

To calculate eco-efficiency, the revenue of the POI was obtained from its annual report. The ship emission data were available only from January to October in 2012 while port revenue covered the whole year. Hence, we approximated the revenue between January to October by multiplying the ship movement ratio to the annual revenue in 2012, where the ratio is the number of ships between January and October divided by the total number of ships entered.

4. Results

4.1 Total external cost

Table 2 shows external costs by ship activity stage and pollutants. Total external cost of Port of Incheon from January to October 2012 is $90,805,478. The most harmful pollutant in terms of social cost is the PM2.5 causing 42,414,627$ or 42% of total social cost. The most environmentally costly phase is ‘Passing thorough lock gate’ causing 52,142,427$accounting for 57% of total cost. Figure 1 shows external costs by vessel movement.

Table 2. External cost by vessel movement and pollutant (unit: $)
Pollutant Anchorage Maneuvering to lock gate Passing thorough lock gate Approaching to dock Docking Total
SO2 3,634 426,402 7,932,118 5,360,649 90,895 13,813,699
NOX 2,036 238,880 4,443,753 3,003,157 50,922 7,738,748
PM2.5 11,158 1,309,257 24,355,376 16,459,743 279,092 42,414,627
PM10 7,061 828,450 15,411,179 10,415,115 176,599 26,838,403
Total 23,889 2,802,989 52,142,427 35,238,664 597,509 90,805,478
Download Excel Table
Figure 1. External cost estimates by vessel movement (unit: $1,000)
Download Original Figure

The external costs by ship and pollutant type are listed in Table 3. International car ferry, full-container vessel, car carrier and general cargo vessel are the largest damage inflictors with social costs $30,343,305, $16,933,369, $12,243,705 and $9,339,728, respectively. Specifically, car carrier and international car ferry are the most expensive emitters, which is consistent with Chang et al. (2014a). This means that vessels that carry automobiles should be the main target of emission control. Figure 2 illustrates external costs by vessel type.

Table 3. External costs by ship type
Pollutant LNG carrier LPG carrier Towing tug ship International car ferry Fuel supplies ship Other tug ship Other chemical tanker
SO2 282,182 177,403 363,180 4,615,947 69,084 283,291 12,681
NOX 158,085 99,385 203,462 2,585,958 38,702 158,706 7,104
PM2.5 866,432 544,710 1,115,135 14,173,153 212,120 869,837 38,935
PM10 548,246 344,673 705,616 8,968,246 134,221 550,401 24,637
Total 1,854,944 1,166,171 2,387,393 30,343,305 454,127 1,862,235 83,357
Pollutant Other cargo ship Refrigerated cargo ship Sand carrier Dry bulk carrier Chemical tanker Semi-con. ship Cement carrier
SO2 27,751 4,332 47,440 419,322 562,237 66,989 141,994
NOX 15,547 2,427 26,577 234,914 314,978 37,529 79,548
PM2.5 85,209 13,302 145,664 1,287,518 1,726,336 205,688 435,988
PM10 53,917 8,417 92,171 814,694 1,092,361 130,152 275,877
Total 182,425 28,479 311,852 2,756,448 3,695,912 440,357 933,406
Pollutant Passenger ship Deep-sea fishing ship Crude oil carrier General cargo ship Car carrier Chemical prod. carrier Scrap carrier Full-con. ship
SO2 175,972 2,966 77,463 1,420,798 1,862,562 477,130 147,004 2,575,973
NOX 98,583 1,662 43,397 795,963 1,043,450 267,299 82,355 1,443,119
PM2.5 540,316 9,107 237,848 4,362,524 5,718,952 1,465,017 451,371 7,909,462
PM10 341,892 5,763 150,502 2,760,444 3,618,741 927,008 285,611 5,004,815
Total 1,156,763 19,498 509,209 9,339,728 12,243,705 3,136,455 966,341 16,933,369
Download Excel Table
Figure 2. External cost by ship type (unit: $1000)
Download Original Figure
4.2 Eco-efficiency

Eco-efficiency was obtained by using the ratio of social costs to port performance indices, i.e., the number of passenger, ship call, and revenue. This gives useful insights on emission from ships at the POI, providing measures of environmental and economic performance. To evaluate the performance of the POI, we compare its eco-efficiency to the Port of Las Palmas (PLP) (Tichavska and Tovar 2015). The eco-efficiencies of the POI and PLP are reported in Table 4 and 5, respectively.

Table 4. Eco-efficiency at the POI
Pollutant Total external cost ($) External cost per passenger ($/Pax) External cost per ton of cargo ($/1,000 tons) External cost per ship call ($/call) External cost per port revenue ($/1,000$)
SO2 13,813,699.2 8.3 140.7 998.9 24.8
NOX 7,738,748.2 4.4 78.8 559.6 139.4
PM2.5 42,414,627.2 25.5 431.9 3067.1 764.1
PM10 26,838,403.2 16.1 273.3 1940.7 483.5
Total 90,805,477.8 54.6 924.9 6566.4 163.0
Download Excel Table
Table 5. Eco-efficiency at the PLP
Pollutant Total external cost ($) External cost per passenger ($/Pax) External cost per ton of cargo ($/1,000 tons) External cost per ship call ($/call) External cost per port revenue ($/1,000$)
SO2 83,151,625.0 25.3 2,119.2 9,109.9 1,680.4
NOX 63,357,311.0 13.0 1,928.1 6,941.5 1,280.3
PM2.5 93,391,745.0 25.5 2,527.9 10,231.2 1,887.3
Total 239,900,681.0 73.0 6,114.1 26,283.0 4,848.1
Download Excel Table

Overall, the eco-efficiency of the POI is better than that of PLP. Inspecting external cost per passenger, one can see that the figures are much lower for the POI by half to four times relative to the PLP. For instance, every passenger carried at the POI, the social cost associated with SO2 emission at the port is 8.3, which is four times lower than the one at the PLP. The difference is even more drastic for external cost per ton of cargo: from as low as 5.86 times (SO2) to as high as 24.7 times (NOX). The similar argument holds for the external cost per ship call. The highest difference is observed for NOX emission (12.41 times) and the lowest one for PM2.5 (3.33 times). Lastly, external cost of SO2 per port revenue is significantly higher for the PLP than the POI.

4.3 Effects of the ECA and RSZ

Lastly, benefits of ECA and RSZ are examined. Note that we only report results of SO2 for the ECA, for the policy only regulates SO2 and the effect of the ECA on other pollutant type is not clear yet (Chang et al., 2014a). In Table 6, introducing RSZ can reduce approximately a third of total emissions. Even more drastic cut in emission is observable if the POI initiates ECA: 59% and 93% of SO2 emission reduction when the sulfur content per fuel is 1.0 and 0.1%, respectively. Using these estimates, the social benefits of the RSZ and ECA at the POI are listed in Table 6. Under RSZ, the corresponding emission abatement values are $4,485,308, $2,642,009 and $21,932,435 from SO2, NOX and PM reduction, respectively. If 1.0% and 0.1% sulfur fuel are used due to the ECA, the social cost savings amount to $8,174,947 and $12,868,842 from SO2 reduction.

Table 6. External cost and reduction percentage under various scenarios
Pollutant Status quo ($) RSZ ($) RSZ (%) ECA 1.0% ($) ECA 1.0% (%) ECA 0.1% ($) ECA 0.1% (%)
SO2 13,813,699 9,328,391 32.47 5,638,752 59.18 944,857 93.16
NOX 7,738,748 5,096,739 34.14
PM2.5/10 69,253,030 47,320,595 31.67
Download Excel Table

5. Conclusion

This paper measured the social cost and eco-efficiency of vessel emissions at the POI. To this end, we used emission data in Chang et al. (2014a), whose analysis adopted an activity-based approach incorporating ship engine, vessel type, and ship movement data. Findings suggest that the total social cost of ship emission at the POI amounts to $90,805,478. The eco-efficiency of the POI, compared to the one of the Port of Las Palmas in another study, is substantially better. For instance, external cost per ton of cargo including all pollutant types is lowered by eight times, external cost per ship call four times and external cost per $1000 revenue three times. Under RSZ, the corresponding emission abatement values are $4,485,308, $2,642,009 and $21,932,435 from SO2, NOx and PM reduction. If 1.0% and 0.1% sulfur fuel are used due to the ECA, the social cost savings amount to $8,174,947 and $12,868,842 from SO2 reduction.

The method employed in this study may be applied to other Korean ports. For example, according to Korea Ministry Oceans and Fisheries, container traffic at the Port of Busan and Gwangyang is greater than that of Incheon. Estimating social cost and eco-efficiency at these ports may help policy makers to measure benefits of introducing ECA and RSZ in the nearby area. Moreover, the tool can be used to assess emission from airplanes. Interested readers may refer to Kim et al. (2010).

This paper has room for improvement. First, using more sophisticated data from AIS can yield more accurate estimates. Second, one may employ the emission cost factor fine-tuned for an analyzed port (in our case, the region surrounding POI). Lastly, even though eco-efficiency suggests that the POI is much more environmentally efficient than the PLP, one need to caution that including other external factors is necessary. An example is life-cycle emission of port operation. Port operation involves constructing berth, positioning cranes, and also inland-side terminal operations. Neglecting emissions from these sources can possibly result in biased estimates of emission from ships.


This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2014S1A5A2A01012501).



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