Minimum Efficient Scale (MES) and preferred scale of container terminals

The decision on the scale of a port terminal affects the terminal’s managerial, operational and competitive position in all the phases of its life. It also affects competition structures in the port in which the terminal is operating, and has a potential impact on other terminals. Port authorities and terminal operators need to know the scale of the terminal when engaging in concession agreements. In economic theory the scale of a plant/firm is typically defined in relation to the Minimum Efficient Scale (MES), the long-run output where the internal economies of scale are fully exploited. However, there are a number of theoretical and empirical indications that in ports the scale of a terminal is commonly guided by a combination of the MES and other determining factors. The “preferred” scale is the result of a complex interaction between the MES, the port governance framework and objectives, the market size and structure, technological change and operational considerations, physical and geographical limitations, and the business patterns of shipping lines. This study analyses the factors resulting in a preferred container terminal scale that in most of the times is different from the MES. The analysis of the technical, market-related and governance-related factors is supported by theoretical and empirical insights that illustrate the presence of a range of actual ”preferred” scales of terminal concessions that usually are different, below or above, MES.     

Bayesian calibration of mechanistic aquatic biogeochemical models and benefits for environmental management

Aquatic biogeochemical models have been an indispensable tool for addressing pressing environmental issues, e.g., understanding oceanic response to climate change, elucidation of the interplay between plankton dynamics and atmospheric CO₂ levels, and examination of alternative management schemes for eutrophication control. Their ability to form the scientific basis for environmental management decisions can be undermined by the underlying structural and parametric uncertainty. In this study, we outline how we can attain realistic predictive links between management actions and ecosystem response through a probabilistic framework that accommodates rigorous uncertainty analysis of a variety of error sources, i.e., measurement error, parameter uncertainty, discrepancy between model and natural system. Because model uncertainty analysis essentially aims to quantify the joint probability distribution of model parameters and to make inference about this distribution, we believe that the iterative nature of Bayes' Theorem is a logical means to incorporate existing knowledge and update the joint distribution as new information becomes available. The statistical methodology begins with the characterization of parameter uncertainty in the form of probability distributions, then water quality data are used to update the distributions, and yield posterior parameter estimates along with predictive uncertainty bounds. Our illustration is based on a six state variable (nitrate, ammonium, dissolved organic nitrogen, phytoplankton, zooplankton, and bacteria) ecological model developed for gaining insight into the mechanisms that drive plankton dynamics in a coastal embayment; the Gulf of Gera, Island of Lesvos, Greece. The lack of analytical expressions for the posterior parameter distributions was overcome using Markov chain Monte Carlo simulations; a convenient way to obtain representative samples of parameter values. The Bayesian calibration resulted in realistic reproduction of the key temporal patterns of the system, offered insights into the degree of information the data contain about model inputs, and also allowed the quantification of the dependence structure among the parameter estimates. Finally, our study uses two synthetic datasets to examine the ability of the updated model to provide estimates of predictive uncertainty for water quality variables of environmental management interest.     

UK tonnage tax: subsidy or special case?

Following the introduction of tonnage tax for UK-based shipping companies in 2000, the industry now has the opportunity to opt for a tax regime that is beneficial in contrast to the corporation tax applied to most other sectors. This paper aims to summarize the tonnage tax scheme and then to go on to assess whether its introduction was appropriate. In doing so, it assesses whether the benefits it brings to the UK shipping industry are in fact fiscally justifiable in terms of the advantages that have been suggested recently by a number of commentators. These are claimed to derive from the comparative advantage of the UK shipping industry, the need for more skilled on-shore shipping staff with sea-going experience, and in terms of sustaining the maritime cluster typified by the maritime activities of the City of London.