Impact of Carbon Intensity Indicator on the Vessels’ Operation and Analysis of Onboard Operational Measures

Rauca, Livia; Batrinca, Ghiorghe

doi:10.3390/su151411387

Cited by 5 publications

(4 citation statements)

References 13 publications

(19 reference statements)

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“…To support and standardize a comparative metric for energy efficiency, the IMO has mandated the Carbon Intensity Indicator (CII), which classifies CO2 emissions per deadweight per nautical mile (IMO 2018). However, it is calculated on a yearly basis and most useful for ship owners or charterers (Rauca and Batrinca 2023). Yet it remains to be seen how well seafarers can integrate it into their estimations of a single route's efficiency.…”

Section: Challenges Of Maritime Energy Efficiencymentioning

confidence: 99%

Anchoring Autonomy: Understanding Seafarers’ Interaction with Energy Efficiency Decision Support Systems for Route Planning and the Role of Basic Psychological Needs

Zoubir,

Schwarz,

Heidinger

et al. 2024

Preprint

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Onboard Decision Support Systems (DSS) for energy-efficient maritime route planning can be critical in achieving global goals for carbon reduction and sus-tainable transportation. However, research has shown that seafarers’ fuel reduc-tion behaviors are hindered by goal conflicts and workload, reducing motivation and efficient use of automated supporting systems. Therefore, we examined sea-farers’ (N = 22) interactions with a DSS while planning routes in a high-fidelity bridge simulator. To gain a comprehensive picture, we assessed Usability, User Experience, Workload, Basic Psychological Need (BPN) fulfillment and Prefer-ences for Automation Types, and conducted interviews. Despite positive system evaluation, especially hedonic User Experience, our analysis of BPN data points towards a perception of reduced autonomy when using the DSS. Similarly, sea-farers frequently requested features increasing their autonomy (e.g. route editing). Our findings highlight the significance of considering BPNs, especially autono-my, in the human-centered design of DSS and automated tools for maritime transportation. Prioritizing these aspects can contribute to broader environmental and efficiency goals.

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Section: Challenges Of Maritime Energy Efficiencymentioning

confidence: 99%

Anchoring Autonomy: Understanding Seafarers’ Interaction with Energy Efficiency Decision Support Systems for Route Planning and the Role of Basic Psychological Needs

Zoubir,

Schwarz,

Heidinger

et al. 2024

Preprint

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show abstract

“…In terms of carbon intensity, previous studies are mainly focused on how to upgrade ships to make them more efficient and attractive to charterers and to increase their competitiveness in the market [16]. Wang analyzes the effectiveness of four current CII metrics and considers designing an average CII calculation method for shipping companies rather than individual vessels [17].…”

Section: Related Workmentioning

confidence: 99%

“…Finally, it is necessary to calculate the cumulative carbon intensity of the ships up to each point of the data, as well as the ratio of the total cumulative CO 2 mass of the ship at each moment in time to the total transport workload it carries out, by using the following formula [16]:…”

Section: Calculation Of Cumulative Carbon Intensity Of Shipsmentioning

confidence: 99%

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Research on Carbon Intensity Prediction Method for Ships Based on Sensors and Meteorological Data

Zhang,

Lu,

Wang

et al. 2023

JMSE

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The Carbon Intensity Index (CII) exerts a substantial impact on the operations and valuation of international shipping vessels. Accurately predicting the CII of ships could help ship operators dynamically evaluate the possible CII grate of a ship at the end of the year and choose appropriate methods to improve its CII grade to meet the IMO requirement with minimum cost. This study developed and compared five CII predicting models with multiple data sources. It integrates diverse data sources, including Automatic Identification System (AIS) data, sensor data, meteorological data, and sea state data from 2022, and extracts 21 relevant features for the vessel CII prediction. Five machine learning methods, including Artificial Neural Network (ANN), Support Vector Regression (SVR), Least Absolute Shrinkage and Selection Operator (LASSO), Extreme Gradient Boosting (XGBoost), and Random Forest (RF), are employed to construct the CII prediction model, which is then applied to a 2400 TEU container ship. Features such as the mean period of total swell, mean period of wind waves, and seawater temperature were considered for inclusion as inputs in the model. The results reveal significant correlations between cumulative carbon emissions intensity and features like cumulative distance, seawater temperature, wave period, and swell period. Among these, the strongest correlations are observed with cumulative distance and seawater temperature, having correlation coefficients of 0.45 and 0.34, respectively. Notably, the ANN model demonstrates the highest accuracy in CII prediction, with an average absolute error of 0.0336, whereas the LASSO model exhibits the highest error of 0.2817. Similarly, the ANN model provides more accurate annual CII ratings for the vessel. Consequently, the ANN model proves to be the most suitable choice for cumulative CII prediction.

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Operational cycles for maritime transportation: Consolidated methodology and assessments

Godet,

Panagakos,

Barfod

et al. 2024

Transportation Research Part D: Transport and Environment

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Impact of Carbon Intensity Indicator on the Vessels’ Operation and Analysis of Onboard Operational Measures

Cited by 5 publications

References 13 publications

Anchoring Autonomy: Understanding Seafarers’ Interaction with Energy Efficiency Decision Support Systems for Route Planning and the Role of Basic Psychological Needs

Anchoring Autonomy: Understanding Seafarers’ Interaction with Energy Efficiency Decision Support Systems for Route Planning and the Role of Basic Psychological Needs

Research on Carbon Intensity Prediction Method for Ships Based on Sensors and Meteorological Data

Operational cycles for maritime transportation: Consolidated methodology and assessments

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