| dc.description.abstract | The shipping industry faces a number of challenges regarding its share of total
anthropogenic emissions worldwide. A range of measures have been initiated, both
by official and private parties. Nevertheless, there are indications that the variety
of approaches and the lack of recognized industry standards are creating confusion,
resulting in ineffective action against the significant problem of climate change. The
growing sense of urgency in relation to global warming, as well as disappointment in the
International Maritime Organizations as the main regulatory body for global shipping,
have created uncertainty and made it more difficult for decision makers to predict the
future of the industry. At the same time, recent advances in data analysis mean that
decision makers are able to produce better empiric modeling of emissions estimations,
potentially improving their operations, regulations, or policies. In this thesis, we propose
a machine learning approach for estimating the environmental performance of vessels.
The theoretical framework established by Gibson et al. (2019) serves as a foundation
for our research. Accordingly, we establish a transparent and quantitative approach for
estimating environmental sustainability impact of vessel exhaust gases. Our approach
uses predicted operational data derived from the Gradient boosting method, together
with the best available measurements of emissions impacts, to portray the complexity of
environmental sustainability. Our findings show the value that empirical modeling, in
the form of machine learning, can provide to internal and external decision makers who
compute and apply emissions estimates, both in the short and long term.
Keywords – Machine learning, Vessel environmental performance, Shipping,
Sustainability | en_US |
