This thesis presents an agent based model of the dry bulk shipping sector. The model is highly disaggregated, representing all voyages and cargoes transported through to 2050, including approximately 500 shippers and 750 shipowners with a total fleet of greater than 1000 vessels. In multiple projection scenarios, 2700 trade flows are modelled. The purpose of the approach is to identify a high fidelity representation of the system to gain a greater understanding of how aggregate level properties, for example total fuel consumption, are generated from individual company based decisions such as when to transport cargo, what vessels to use, and what technology to invest in. Contracts of affreightment, the spot market and time charter market are represented within the model to create, where possible, a realistic representation of actual contractual conditions. The model is deployed to investigate the impact of climate change on the sector. Specifically, it investigated: physical impacts of climate change through the opening of Arctic sea routes; changing demand for commodities due to climate change and projected evolution of the global economy; changing fuel prices due to external projected changes in the shipping sector, and; effects of mitigation of climate change through carbon pricing and minimum standards on vessel efficiency. A key finding from the work is that endogeneous changes in the shipping system, through for example shipper preferences, create greater variability than those driven by external factors. This variability is reflected in the number of vessels in each of the size categories, the technology uptaken and the strategic approach of shippers in transporting their cargo. There remains a strong coupling of transport supply and emissions, with the regulations tested and available technology not resulting in significant improvements in energy efficiency. On the modelling of the dry bulk shipping system, clear computational and scope limits were identified. On computational limits, the system is constrained such that parallelisation is limited leading to long runtimes. To understand the effects of agents choices, the modelling of the individual voyages is necessary leading to large degrees of freedom. In addition, the work has highlighted the need for more validation data of greater granularity
