Global steam coal supply costs in the face of Chinese infrastructure investment decisions

In this work we demonstrate the effects of different Chinese transport infrastructure investment strategies on long run marginal costs of steam coal supply in Europe. Increasing Chinese demand for steam coal will lead to a growing need for additional domestic infrastructure in China as production hubs and demand centers are spatially separated. If domestic transport capacity is only available at elevated costs, Chinese power generators could turn to the global trade markets and increase steam coal imports. Increased Chinese imports could significantly influence global trade market price levels which would especially affect nations mainly relying on imports, like for example Europe. We analyze the scope of this effect under different assumptions for Chinese transport infrastructure developments. For this purpose, we develop a spatial equilibrium model for the global steam coal market. For our assumption regarding production and transport cost evolutions, we rely on an input factor-based cost calculation methodology. We found out that the investigated Chinese infrastructure decisions have a modest impact on long run marginal costs of supply for Europe and the US but significant effects for China.Steam coal; MCP; non-linear optimization; China; Europe; transport infrastructure

Analysis and forecast of the capesize bulk carriers shipping market using Artificial Neural Networks

Thesis (S.M. in Ocean Systems Management)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2006.Includes bibliographical references (leaves 188-189).Investing in the bulk carrier market constitutes a rather risky investment due to the volatility of the bulk carrier freight rates. In this study it is attempted to uncover the benefits of using Artificial Neural Networks (ANNs) in forecasting the Capesize Ore Voyage Rates from Tubarao to Rotterdam with a 145,000 dwt Bulk carrier. Initially, market analysis allows the assessment of the relation of some parameters of the dry bulk market with the evolution of freight rates. Subsequently, ANNs with an appropriate architecture are constructed and sufficient data, in terms of quantity and quality, are collected and organized so as to establish both the training and the testing data sets. The use of ANNs along with genetic algorithms allows the prediction of bulk freight rates with considerable accuracy for as long as eighteen months ahead and this is quantified by calculating the relative and absolute errors. It is concluded that ANNs offer a promising approach to forecasting the bulk market when coupled with efficient market modeling.by Athanasios V. Voudris.S.M.in Ocean Systems Managemen