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We develop a duopoly model to analyse the environmental impact of high-speed rail (HSR) introduction in a market for travel served by air transport. We take into account the effects on the environment of induced demand, schedule frequency and HSR speed simultaneously, and we show that competition between the two modes may be detrimental to the environment depending on the magnitude of the pollution level of HSR relative to air transport. We conduct a simulation study based on the London–Paris market and we find that the introduction of HSR increases local air pollution (LAP) but decreases greenhouse gases (GHG) emissions. Moreover, we perform a sensitivity analysis of our results towards the level of HSR and air transport emissions. We find that modal competition is more likely to be detrimental to the environment when HSR emissions are not significantly lower than air transport emissions. Furthermore, we find that modal competition is more likely to be detrimental to the environment in the case of a fully private HSR, than in the case of a mixed public/private-owned HSR that takes into account the surplus of consumers and the surplus that the air transport operator brings about. Finally, we provide an interpretive discussion of the results with respect to the different mitigation strategies available to the two transport modes and the EU policy measures for the environment – which might affect HSR and air transport emissions.

AbstractAs the rate of ice melt in the Arctic increases, the potential for shipping activities is also increasing. However, infrastructure along the northwest passage (NWP) in Canada's Arctic is almost nonexistent. This presents major challenges to any response efforts in the case of a natural disaster. Also, the Arctic is home to many indigenous communities, as well as flora and fauna. Thus, it is of vital importance to protect the livelihood of the rights holders in this area and the Arctic marine environment. To do this, it is necessary to develop a decision‐making tool to assess the potential risk of pollutants arising from increased shipping activity. Understanding such, this article assesses the impacts of a potential oil spill on communities in the Canadian Arctic. The consequences of risk are presented using a multiperiod model while the likelihood is analyzed using Bayesian Network. The output of the multiperiod model is incorporated into an influence diagram for risk assessment purposes. The Bayesian model benefits from expert elicitation from the crew aboard a research ship passing through the NWP. Information was also obtained from marine insurance companies, government representatives, and other Arctic specialized professionals. The risk‐based model is subsequently applied to the Canadian Arctic area, with the aim of evaluating the impact of a potential oil spill through shipping.

In this paper, we examine the development of the Chinese high-speed rail (HSR) network over the period 2003–2014. Our methodology relies on network analysis to identify changes over time in HSR network accessibility measured by node degree, strength, closeness, and betweenness. We show that sub-networks connecting regional centres with second-tier cities are first built. Once such sub-networks are mostly established, they are also connected with each other. Moreover, a simultaneous rather than a sequential schedule for multiple HSR segments is at place at any particular time. Finally, we show that the HSR network expands all the lower-elevation, more populous and prosperous regions. Although some clusters are critical politically, HSR network development is still mainly driven by economic factors, which is reflected from the fact that nodes in the more economically vibrant regions present a higher number of routes passing through and higher service quality (i.e., lower travel time). Finally, in order to illustrate the uniqueness of the Chinese HSR development pattern, we briefly discuss and compare the HSR development in other Asian markets, notably Taiwan, South Korea and Japan.

AbstractIt is difficult to assess the risk of ship‐source oil spills in Arctic waters for insurance purposes due to many unknowns and the lack of reliable data. However, maritime activities in the Arctic area continue to grow, indicating the urgent needs for the development of innovative methods to estimate loss from potential ship‐source oil spills in the Arctic area. To fill this gap, we develop a hybrid Bayesian‐loss function‐based method to assess ship‐source oil spill‐related loss and implement an illustrative test on Baffin Island, Nunavut in Canada. The results confirm that our method can accurately assess loss and, subsequently, develop reliable insurance premiums for shipping activities in the Arctic area. This enables governmental and nongovernmental organizations alike to use the method as a reliable loss estimation mechanism for ship‐source oil spills. Also, it is a valuable tool in designing measures for safer and more resilient Arctic shipping.