Benefit, cost, and size of an emission control area: a simulation approach for spatial relationships

ABSTRACT

International Maritime Organization has established an international framework for emission control areas (ECAs) to reduce air pollutants from shipping and several countries have designated ECAs in their territories. The world’s ECAs seem to be molded eventually by demarcation lines according to the international laws of the sea, but are not set elastically solely based on a cost-benefit analysis. The existing literature does not pay careful attention to the relationship between ECA expansion and its costs and benefits. This study analyzes the spatial relationship between the net benefit and the size of ECA by using a numerical simulation that includes the decision-making required to alter ships’ locations in the sea as well as a dispersion model of air pollutant. The simulation provides the net benefit curve, the decomposition of benefit, marginal cost curve, and the moving average marginal benefit curve, which are intersected by three points. Changing principal parameters in the simulation, such as the value of statistical life, the population density, and the premium of low-sulfur fuel clarifies their relationships with ECA size. The results provide an evaluation of the distance of existing ECA boundaries from seashores and a guideline for setting ECAs.

KEYWORDS:

• Air pollution
• shipping
• emission control area (ECA)
• simulation
• benefit decomposition

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

1. ‘Benefit’ and ‘cost’ are defined in this study as follows. Benefit means reduction of the external cost of pollution due to implementation of the ECA, while cost means incremental cost from compliance with Annex VI for ships. ‘Net benefit’ is benefit less cost.

2. First, we set the boundary of the model for this study and suppose an origin-destination port, shipshape, technology for each ship, and fuel price. Each ship decides the routes to destination ports based on a cost function only. Similar to Fujita (1989), we make some assumptions about the spatial characteristic of the sea area: the city forms some part of the plain. The sea is a featureless plain adjacent to the city area, and all sea parcels are identical, for example weather conditions and hydrographic conditions are identical. In this context, the only spatial feature of each part of the sea that matters to ships is the distance from the seashore, because when ships choose a route to sail farther from the seashore, voyage distance from origin port to destination port becomes longer. This also affect the voyage cost of ships. Hence, the city and sea can be treated as if it were one-dimensional. Then, we can analyze spatial relationships between costs and benefits by the size of the ECA by linking the distance dependence of air pollutants from ships to the distance from the seashore in the linear model.

3. As this study adopts a linear sea model, the navigation routes that ships choose are equivalent to the chosen locations of navigation. See also footnote 2.

4. Considering that Wang, Corbett, and Winebrake (2007) defined low-sulfur fuel as marine fuel with sulfur content not exceeding 1.5 percent and Brynolf, Baldi, and Johnson (2016) defined it as marine fuel with sulfur content not exceeding 1.0 percent, this study uses the range of 1.0―1.5 percent. As ships have had to use low sulfur distillate fuels with sulfur content not exceeding 0.1 percent to enter SECAs since 2015, a result of the simulation based on the case of a 90 percent reduction of sulfur content in fuel is mentioned in Subsection 4.1.3. Chang, Roh, and Park (2014) reported almost the same percentage reduction if ships use 0.1 percent fuel.

5. Assuming some depth of a grid of the simulation, ships emit 47 kg per period in the simulation. When we set a period to one week, there is not much difference between the total yearly emissions in the simulation and the figure for ships’ emission amounts in some sea grids near Japan (MLIT 2012; Shirota et al. 2013).

6. It is noteworthy that the marginal cost and marginal benefit indicate the change of costs and benefits when the ECA range moves infinitesimally toward the distant sea. This definition differs from the usual use of these terms.

7. The MAMB is calculated from the average marginal benefit: $MAM{B}_k=1/5{\sum }_{j=-2}^2\hspace{0.17em}M{B}_{k+2j}$, where k (km) is the location in the sea area, MB is the difference of benefit by ECA at k and at $k-2$.

8. As the method of the moving average is employed, the marginal benefit curve randomly fluctuates, and the MAMB and marginal cost curves might intersect at many points. Then, we identify the intersection as follows. If the difference between the MAMB and marginal cost consecutively becomes negative for 6 kilometers from a point, the point is judged as the intersection. This is because the MAMB and marginal cost curves are diminishing and from the moving average method, marginal benefit is less than marginal cost if the difference is consecutively negative for 6 kilometers.

9. The model in this study, which covers a linear city, can be extended to a city with a featureless sea area, as in Kanemoto (1987). Hence, the results of the numerical simulation in this study can provide a rough guideline for setting the ECA boundary.

Additional information

Funding

This work was supported by the Japan Society for the Promotion of Science [KAKENHI grant numbers 24710052 and 17K00701].