# How COVID-19 Affected GHG Emissions of Ferries in Europe

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## Abstract

**:**

## 1. Introduction

## 2. Literature Review and Research Questions

#### 2.1. Research Questions

- Q1.
- Did the heterogeneity of the ferry fleet influence the outcome and how?
- Q2.
- Was there any specific geographical pattern across the European sea basins?
- Q3.
- Was the change related to the way the vessels were operated, in particular their number of port calls?

## 3. Data and Preprocessing

#### 3.1. Datasets and Variables

`Etot`, and the CO${}_{2}$ emitted while the ship is at berth at ports under an EU member state’s jurisdiction,

`Eber`, cf. Table 1. Throughout the paper, the terms “per-ship” or “unitary” are used interchangeably with reference to emissions.

#### 3.2. Preprocessing of THETIS

_{2}emissions was extracted from the THETIS dataset. According to the EU-MRV regulation, the annual emission reports of each ship in the previous calendar year are published by EMSA from 30 June. However, information for all ships is not always provided by this date. Furthermore, already published data for some ships may be reviewed by the companies. In either case (ships added or data reviewed), a new version of the dataset for that specific monitoring year is generated by THETIS and then published. We therefore considered the latest version available at the time we began our research for each monitoring year. The THETIS versions selected for our study are reported in Table 2.

`Etot`<

`Eber`;

`Etot`lower than emissions from all voyages between ports under a member state’s jurisdiction; an annual total time spent at sea that exceeded the number of hours in a year). Then, only ships of the “Ro-pax” type were selected, thus matching the IHS dataset. This class also includes high-speed craft (HSC), which exhibit very different speed and propulsion characteristics than displacement vessels. In terms of their GHG emissions, it has been proposed that HSC should be assessed separately from other ferries [46]. We therefore removed them from the “Ro-Pax ship”. To this end, a condition suggested by [47] on a scaled service speed (the Froude number

`Fn`) was applied:

`maxV`and ship length

`LOA`used for evaluating Equation (1) were taken from specific variables in the IHS dataset as reported in Table 1. The gravity acceleration was ${g}_{0}=9.8$ m/s${}^{2}$.

#### 3.3. Preprocessing of IHS

`VType`was introduced to describe the vessel type. This was coded as an integer value of between 0 and 15, as given by

`VType`= 0 therefore consists of low main-engine power, low passenger-carrying capacity, short length and old ferries. This is the reference class for the subsequent inferential analysis conducted in Section 6. Other classification criteria would in principle be possible, depending on ferry data availability. E.g., annual revenues, lane-meter capacity, or bed capacity could also be considered [14].

`VType`.

`Dom`categorical variable with the values of Baltic, Mediterranean, or North Sea) based on the location of its ports of call. These were obtained from the corresponding IHS database. A vessel was assigned to the domain where it made most of its calls in a specific year. The Black Sea and Atlantic Ocean were discarded due to a limited number of vessels sailing in those domains.

`nCalls`variable.

## 4. Preliminary Analysis

`Fn`= 0.4 suggested by [47] is effective in identifying the HSC cluster. For non-HSC vessels, the main engine power

`Pme`involves a power–law dependence on the Froude number

`Fn`. However, for any given

`Fn`,

`Pme`tends to increase with

`VType`as defined by Equation (2). Thus, newer and larger vessels (tendency toward “jumboizing”, [14]) are generally powered by larger main engines. In addition, for a given

`Pme`, newer vessels can sail at a larger

`Fn`.

`Paux`on average scales as $\sqrt{\mathtt{Pme}}$. The fuel consumption rate (i.e., the mass of fuel burned per hour,

`FuelC`), however, is only weakly related to either

`Pme`or

`Paux`. This is likely due to the role played by the mass of fuel burned per work unit or a specific fuel consumption [48]. The CO${}_{2}$ emission rate is then proportional to the fuel consumption.

`Etot`in 2020, which is broader than in previous years and includes a fatter low-emissions tail.

`Etot`falling by nearly 15% and

`Eber`by 6% (Table 4). The number

`nCalls`of per-ship annual port calls was stable from 2018 to 2019 but then decreased by about 7% in 2020, which is highly significant. The values from Table 4 are per-ship median figures for non-HSC only, in contrast with those reported in [8], which is likely an average value at the Ro-Pax fleet level.

## 5. Methods

`Etot`over the three years 2018–2020 is about 0.9. Such a high correlation would make both standard errors (SE) and p-values unreliable. Therefore, a panel data approach using LME models was adopted [36,41], which can handle both ship-specific effects and interactions among predictors [37,38]. LME models not only allow us to address the time correlation of the data but also to reduce the residual variability, while accounting for the large heterogeneity of the fleet. Moreover, considering interaction terms in the LME models, we take advantage of the detailed description of the fleet structure given by the IHS dataset and can assess the impact of COVID from the perspective of research questions Q1–Q3 of Section 2.1.

`Etot`or

`Eber`, let ${y}_{i,t}$ denote the generic CO${}_{2}$ emissions of ship $i=1,\dots ,{n}_{t}$ in year $t=2018,\text{}2019,\text{}2020$. Thus, the model setup is given by:

`COVID`,

`VType`,

`Dom`,

`nCalls`) and their interactions. The vector $\mathbf{\beta}$ contains the corresponding fixed effects, which are estimated using maximum likelihood. In addition, ${a}_{i},i=1,\dots ,{n}_{t}$ are the vessel-specific random intercepts given by independent, normal random variables with zero mean and a common variance. They account for the unobserved heterogeneity of the fleet and the correlation among the three years we consider. The residual error $\epsilon $ is assumed to be a Gaussian white noise with zero mean and constant variance independent of the random intercepts. This is checked later in Section 6.1.

`VType`as a predictor.

`VType`further improves these scores.

`VType`= 0 vessels of the Baltic Sea in pre-COVID years (i.e., 2018 and 2019). This follows from the fact that, by default, the R language uses the alpha-numerically first category as a reference, which can be changed, as shown in the code in Appendix A.2.

`VType`or

`Dom`) can be computed as

`COVID`= 1. The uncertainties of the coefficients can be estimated through the SE. It is a quadratic form of the variance–covariance matrix of $\mathbf{\beta}$:

`COVID`and

`COVID:z`terms. The uncertainty on ${\mathsf{\Delta}}_{z}$ is thus expressed as SE$/{\overline{y}}_{z}$.

## 6. Results

#### 6.1. Selected Model

`Etot`or

`Eber`. The first term denotes the vessel-specific random intercept. The remaining three summands represent linear terms in each of the predictors and in the

`COVID`dummy variable and all interactions between

`COVID`and the other predictors.

`COVID`are provided for both

`Etot`and

`Eber`. This table covers Q1-Q2-Q3 research questions by means of impact estimates and p-values as discussed below in Section 6.2. The table also shows that

`VType`= 1 and 14 are only weakly represented due to conflicting parameters (such as low power and large hull). Full details of model #30, inclusive of the terms that do not interact with

`COVID`, are provided in the Supplementary Materials (S3).

`Etot`, it leads to an AIC of less than 0.1% off the minimum of all of the 40 tested models and a RMSEvC of less than 1.5% off the minimum. In addition, considering

`Eber`, model #30 is less than 0.1% off the minimum AIC among all the 40 tested models and less than 2% off the minimum RMSEvC.

`Etot`and

`Eber`. In addition, the skewness is mildly different from zero, particularly for

`Eber`, and the kurtosis is larger than three before COVID. Thus, a moderate non-normality and a moderate heteroskedasticity characterize the residuals. Nevertheless, we believe the sample size is large enough for an approximated asymptotic normality of the estimates and a correct interpretation of p-values.

#### 6.2. Research Questions Answered

`COVID`term in Table 6 shows, an overall reduction of CO${}_{2}$ emissions in 2020 for all vessels is confirmed by LME model #30. A statistically significant change of −4169 (1620) t per ship is estimated for

`Etot`, and a change is also observed for

`Eber`, but this is not statistically significant. The specific changes in vessel type, sea domains, and ship activity (port calls) due to COVID are presented in the following three subsections.

#### 6.2.1. Role of Vessel Type (Q1)

`Etot`with respect to the reference category, i.e.,

`VType`= 2, 3, 6, 7, and 15. In addition to the reference (

`VType`= 0), the categories 15, 2, and 7 included the greatest number of ships. These are all high-passenger capacity ferries. Two ferry types (7 and 15) experienced highly significant emission reductions. Using Equations (4) and (5), we found (cf. the Supplementary Materials, S3) that their total changes exceeded −31 (5)% (

`VType`= 7, ships built up to 1999) and −14 (2)% (

`VType`= 15, new builds) compared to pre-COVID mean values.

`Eber`, Table 6 shows that only

`VType`= 11 (large power, high capacity, short hull, new builds) deviated significantly with respect to the reference category, which represented a reduction of 1199 (484) t in unitary CO${}_{2}$ emissions. A slightly significant increase with respect to the reference occurred for

`VType`= 15, which differs from the previous class only in terms of longer hulls. The unitary emission changes of these two ferry types were −34 (12) and +6 (5)%, respectively. Most of the other types of ferries increased their unitary emissions at berth.

`Etot`and

`Eber`, the relative changes with respect to the reference category are reported in Figure 5. The changes are associated to the vessel types via radarplots. We can also observe how the uncertainty in the statistical estimates relates to the number of vessels in each class.

#### 6.2.2. Role of Sea Basin (Q2)

`Etot`with respect to the reference category, reaching a total change ${\mathsf{\Delta}}_{C:NOR}$ of −18 (5)%.

`Eber`, Mediterranean and North Sea ferries behaved differently from the reference category of the Baltic Sea (i.e.,

`VType`= 0 ferries of pre-COVID years). In the North Sea, emissions slightly increased, but this was not statistically significant, while in the Mediterranean Sea the unitary emissions changed by −17 (6)%, which was slightly significant. This drop can mainly be ascribed to the emission reductions from vessels of

`VType`= 11 (cf. Section 6.2.1), which were all sailing in the Mediterranean Sea.

`Etot`in the North Sea compared to the reduction of

`Eber`in the Mediterranean is mirrored by the size of the uncertainties as illustrated in Figure 6.

#### 6.2.3. Role of Port Calls (Q3)

`Eber`, do not by definition include the port call operations. Their dependence on

`nCalls`is consistently found to be null within the uncertainty.

## 7. Discussion

#### 7.1. Data Availability

#### 7.2. Emissions at Berth

`VType`= 11) decreased by 34%, while in longer vessels (

`VType`= 15) they increased by 6% compared to the reference category. In our analysis, these two categories are only differentiated by

`LOA`/

`nPax`, which can be regarded as a proxy of the space available per passenger.

`LOA`/

`nPax`ferries (

`VType`= 11) underwent a decrease of emissions at berth is consistent with a cold lay-up. These ferries could have had less of a commercial appeal due to inferior services (less space available per person) and higher operational costs (high fuel consumption). Thus, their owners may have decided to lay them up cold to mitigate their economic losses during the pandemic. More data are needed to confirm this hypothesis, though.

## 8. Conclusions

## Supplementary Materials

`Etot`; S2, The 40 LME models for

`Eber`; S3, Featured results for just LME model #30, for both

`Etot Eber`.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

AIC | Akaike’s information criterium |

AIS | Automatic Identification System |

CO${}_{2}$ | carbon dioxide |

COVID | coronavirus disease of 2019 |

EMSA | European Maritime Safety Agency |

EU | European Union |

EU-MRV | EU Monitoring Reporting Verification |

GHG | greenhouse gas(es) |

GT | Gross Tonnes (a vessel’s size metric) |

HSC | high speed craft |

IMO | International Maritime Organization |

LME | linear-mixed effects |

RMSEvC | conditional root mean square error of the validation set |

Ro-Pax | roll-on/roll-off passenger ship |

SE | standard error |

## Appendix A

#### Appendix A.1. Input Dataset

#### Appendix A.2. Source Code

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**Figure 1.**Data availability and causal graph of the CO${}_{2}$ ferry emissions. Blue boxes represent data available at the yearly level; brown elliptical nodes denote voyage-level data not available for this study; the green box represents available data resolved at the level of individual voyages; the transparent box refers to constant parameters. The arrows link causes (tail) and effects (head).

**Figure 2.**Main engine power

`Pme`of the EEA Ro-Pax ships: (

**a**) vs. Froude number, with vessel type Equation (2) portrayed as marker feature (see inset), and the

`Pme`threshold of Table 3 is given as a horizontal dashed line (HSC lay in the region to the right of the vertical dashed line); (

**b**) vs. auxiliary engine power

`Paux`, only for non-HSC, with marker colours indicating fuel consumption rates. Both panels are at the log-log scale and the slant dashed line is identified via least-square fits.

**Figure 3.**(

**a**) Ports called at by ferries in 2018–2020 (markers) and the geographical regions considered (coloured areas); (

**b**) Violin plots of

`Eber`and

`Etot`during the three years. The black cord at the edge of each half-violin spans the 95% confidence interval of the median, which is represented by the white dot.

**Figure 4.**Scores of the 40 candidate models for: (

**a**)

`Etot`; and (

**b**)

`Eber`. The chosen model (#30) is highlighted with a vertical dashed line. The conditional RMSE in the validation set and the AIC are shown as diamonds and circles, respectively. Their minimum values are indicated by filled markers.

**Figure 5.**Per-ship emission changes due to COVID with respect to the reference category (which includes

`VType`= 0) for model #30: (

**a**)

`Etot`and

`Eber`shown as dark and empty bars, respectively, with the lines centered at the top of the columns representing the 95% confidence intervals, and level of significance of ${\widehat{\beta}}_{\mathtt{VType}}$ represented as symbols (cf. Table 5); (

**b**) number of vessels in each class (light grey bars), with

`VType`decoded by the radar plots. Their legend is provided in the top-right corner of (

**a**).

**Figure 6.**Per-ship emission changes due to COVID by

`Dom`, for model #30. MED and NOR refer to the Mediterranean and North Sea, respectively, while the Baltic Sea (BAL) is part of the reference category. The shadings, lines, and symbols are as in Figure 5.

Dataset | Source Variable | Acronym | Description | Units/Type |
---|---|---|---|---|

THETIS | IMO number | IMOn | Vessel unique identifier | - |

Total ${\mathrm{CO}}_{2}$ emissions | Etot | Per-ship total ${\mathrm{CO}}_{2}$ emissions | [ton] | |

CO${}_{2}$ emissions which occurred within ports under a MS jurisdiction at berth | Eber | Per-ship ${\mathrm{CO}}_{2}$ emissions at berth | [ton] | |

IHS | Speedservice | maxV | Service speed | [kts] |

ConsumptionValue1 | FuelC | Fuel consumption rate | [ton/h] | |

TotalKilowattsofMainEngines | Pme | Total power of main engines | [kW] | |

TotalPowerOfAuxiliaryEngines | Paux | Power of auxiliary engines | [kW] | |

PassengerCapacity | nPax | Passenger carrying capacity | - | |

LengthOverallLOA | LOA | Length over all | [m] | |

YearOfBuild | yearB | Year of building | - | |

IHS-derived | several | VType | Vessel type of Equation (2) | [categorical] |

Port Latitude/Longitude Decimal | Dom | Sea basin (BAL, MED, NOR) | [categorical] | |

Call ID | nCalls | Per-ship number of port calls | - | |

- | COVID | Dummy variable for year 2020 | [categorical] |

**Table 2.**CO${}_{2}$ emissions in the THETIS dataset. The $\mathsf{\Sigma}$ in front of the variables indicates that they are integrated across the fleet. The changes in emissions $\mathsf{\Delta}$ refer to the previous year. Units are Mt. The number N of vessels in each dataset and the number of obvious outliers (cf. Section 3.2) are also given.

Subset | All | Non-HSC | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|

$\mathsf{\Sigma}$Etot | $\mathsf{\Sigma}$Eber | $\mathit{N}$ | $\mathsf{\Sigma}$Etot | $\mathsf{\Delta}$ | $\mathsf{\Sigma}$Eber | $\mathsf{\Delta}$ | $\mathsf{\Sigma}$nCalls | $\mathsf{\Delta}$ | ${N}$ | Pruned | |

2018-v217 | 142.19 | 8.71 | 12,059 | 13.03 | 0.96 | 231,332 | 321 | 4 | |||

2019-v191 | 146.3 | 9.21 | 12,336 | 13.53 | 0.5 | 1.01 | 0.05 | 265,193 | 33,861 | 345 | 8 |

2020-v62 | 125.83 | 8.09 | 11,676 | 10.95 | −2.58 | 0.94 | −0.07 | 230,626 | −34,567 | 325 | 4 |

**Table 3.**Threshold values for the predictors in

`VType`, i.e., arguments of the Heaviside function in Equation (2).

k | ${\mathit{\phi}}_{\mathit{k}}$ | ${\mathit{\phi}}_{\mathit{k}0}$ | Units |
---|---|---|---|

0 | Pme | 21,600 | kW |

1 | nPax | 1250 | - |

2 | LOA | 174 | m |

3 | yearB | 1999 | - |

**Table 4.**Per-ship values of emissions and number of port calls. The first three lines report the medians and the following two the median changes. The levels of significance of unidirectional tests are expressed via symbols in Table 5 in the “SignL” column. Wilkoxon’s unilateral tests were also conducted.

Etot | nCalls | Eber | |||||||
---|---|---|---|---|---|---|---|---|---|

[ton] | [%] | SignL | [# per Ship] | [%] | SignL | [ton] | [%] | SignL | |

2018 | 37,482 | 467 | 2779 | ||||||

2019 | 37,432 | 475 | 2621 | ||||||

2020 | 30,182 | 402 | 2474 | ||||||

2019 vs. 2018 | −480 | −1.7 | ∘ | 0 | 0 | −30 | −1.4 | ||

2020 vs. 2019 | −4418 | −15.4 | $\u2022\u2022\u2022$ | −33 | −6.8 | $\u2022\u2022\u2022$ | −104 | −5.9 | ∘ |

**Table 5.**Upper threshold p-values for either bi- or unidirectional tests, with corresponding symbols and predicates.

Symbol | p | Predicate | |
---|---|---|---|

bi | uni | ||

∘ | 0.08 | 0.04 | Nearly significant |

• | 0.05 | 0.0025 | Slightly significant |

$\u2022\u2022$ | 0.01 | 0.005 | Significant |

$\u2022\u2022\u2022$ | 0.001 | 0.0005 | Highly significant |

**Table 6.**Model #30: Fixed effects estimates for the interaction terms with

`COVID`. For

`VType`, the > symbol indicates that the corresponding variable is above the threshold of Table 3 and thus different from that in the reference category (

`VType`= 0). The ${n}_{20}$ column gives the non-HSC counts in 2020. Full details of model #30 are reported in the Supplementary Materials (S3).

Etot | ${n}_{20}$ | Eber | |||||||||

$\widehat{\beta}$ [t] | SE [t] | signL | $\widehat{\beta}$ [t] | SE [t] | signL | ||||||

−4169 | 1620 | • | COVID | 308 | −180 | 216 | |||||

COVID : VType | |||||||||||

$\widehat{\beta}$ [t] | SE [t] | signL | VType | Pme | nPax | LOA | yearB | $\widehat{\beta}$ [t] | SE [t] | signL | |

- | - | - | 0 | - | - | - | - | 56 | - | - | - |

4214 | 7526 | 1 | > | - | - | - | 2 | −37.4 | 1,010 | ||

−3514 | 1955 | ∘ | 2 | - | > | - | - | 38 | 458.2 | 261 | |

−9698 | 3249 | $\u2022\u2022$ | 3 | > | > | - | - | 9 | 382.7 | 433 | |

−552 | 3057 | 4 | - | - | > | - | 11 | 97.9 | 409 | ||

1568 | 2976 | 5 | > | - | > | - | 11 | 481.3 | 399 | ||

−8165 | 4139 | • | 6 | - | > | > | - | 6 | −827.4 | 553 | |

−9602 | 2296 | $\u2022\u2022\u2022$ | 7 | > | > | > | - | 22 | 220.7 | 308 | |

1450 | 2565 | 8 | - | - | - | > | 17 | 98.4 | 343 | ||

−5116 | 3413 | 9 | > | - | - | > | 9 | 275.1 | 457 | ||

−919 | 3210 | 10 | - | > | - | > | 10 | 255.5 | 429 | ||

−5513 | 3624 | 11 | > | > | - | > | 7 | −1199 | 484 | • | |

−2956 | 2342 | 12 | - | - | > | > | 23 | 55.3 | 313 | ||

2843 | 2147 | 13 | > | - | > | > | 27 | 228.3 | 288 | ||

14 | - | > | > | > | 0 | ||||||

−6143 | 1719 | $\u2022\u2022\u2022$ | 15 | > | > | > | > | 60 | 447.5 | 230 | ∘ |

Total | 308 | ||||||||||

$\widehat{\beta}$ [t] | SE [t] | signL | COVID : Dom | $\widehat{\beta}$ [t] | SE [t] | signL | |||||

- | - | - | BAL | 91 | - | - | - | ||||

−666 | 1327 | MED | 145 | −332 | 178 | ∘ | |||||

−3514 | 1590 | • | NOR | 72 | 181 | 212 | |||||

$\widehat{\beta}$ [t] | SE [t] | signL | $\widehat{\beta}$ [t] | SE [t] | signL | ||||||

1.1 | 0.6 | COVID : nCalls | 308 | 0 | 0.1 |

**Table 7.**Statistics of the conditional residuals of the LME model #30 of Section 6 for vessels sailing during either 2018–2019 (ref.) or 2020 (COVID). The full dataset is used as learning data.

Etot | Eber | ||||

units | ref. | COVID | ref. | COVID | |

nsamples | - | 648 | 308 | 648 | 308 |

std | $\left[ton\right]$ | 5667 | 6672 | 793 | 896 |

skewness | - | 0.2 | −0.3 | 1 | 0.7 |

kurtosis | - | 5.0 | 2.0 | 6.2 | 2.1 |

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## Share and Cite

**MDPI and ACS Style**

Mannarini, G.; Salinas, M.L.; Carelli, L.; Fassò, A. How COVID-19 Affected GHG Emissions of Ferries in Europe. *Sustainability* **2022**, *14*, 5287.
https://doi.org/10.3390/su14095287

**AMA Style**

Mannarini G, Salinas ML, Carelli L, Fassò A. How COVID-19 Affected GHG Emissions of Ferries in Europe. *Sustainability*. 2022; 14(9):5287.
https://doi.org/10.3390/su14095287

**Chicago/Turabian Style**

Mannarini, Gianandrea, Mario Leonardo Salinas, Lorenzo Carelli, and Alessandro Fassò. 2022. "How COVID-19 Affected GHG Emissions of Ferries in Europe" *Sustainability* 14, no. 9: 5287.
https://doi.org/10.3390/su14095287