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In Europe the transport sector accounts for more than 27% of total CO 2 emissions and, within this sector, road transport is by far the largest polluter. This fact has placed road transport emissions abatement firmly on the agenda of global alliances. In this paper, we examine the convergence in per capita road transport CO 2 emissions in a sample of 22 European Union (EU) countries over the 1990-2014 period. We find evidence that EU countries converge to one another but depending on certain structural factors (conditional convergence), and that the convergence speed has increased over time. In light of this evidence, we estimate a conditional convergence dynamic panel data model to examine the structural factors affecting the convergence process and its influence on the convergence speed. Because, in our sample, road transport CO 2 emissions depend almost exclusively on (fossil) fuel consumption, we focus on the determinants channelled through the use of energy in the sector. By using alternative econometric approaches (pooled-OLS, fixed-effects and instrumental variables), our results show that the convergence process is conditioned by factors such as economic activity and fuel prices and that some of these factors have a significant effect on the convergence speed. These results may entail policy implications with regards to the geographical impact of the EU policies on climate change currently in place.
Few studies have examined visitor preferences with regard to public bike-sharing inside national parks. Here, we present a case study of the Teide National Park (TNP), the most visited national park in Spain. The TNP is a clear example of a natural site suffering the effects of mass tourism, largely due to the fact that 70% of visitors access the TNP by car. This puts the park’s sustainability under considerable pressure, may well affect visitor enjoyment, and highlights the need to implement alternative transportation systems. The main aim of this paper is to assess the attitudes of visitors to the TNP towards the implementation of a public bike-sharing system. To do so, we combine information on revealed and stated preferences and estimate ordered logit models to establish the determinants of the propensity to choose the bicycle to move around the park. Our findings suggest that the bicycle has potential as a means of transport in this setting. The results have implications for the design of mobility management measures aiming to increase visit quality and reduce the negative externalities associated with mobility patterns in national parks.
In this study, discrete choice models that combine different behavioural rules are estimated to study the visitors’ preferences in relation to their travel mode choices to access a national park. Using a revealed preference survey conducted on visitors of Teide National Park (Tenerife, Spain), we present a hybrid model specification—with random parameters—in which we assume that some attributes are evaluated by the individuals under conventional random utility maximization (RUM) rules, whereas others are evaluated under random regret minimization (RRM) rules. We then compare the results obtained using exclusively a conventional RUM approach to those obtained using both RUM and RRM approaches, derive monetary valuations of the different components of travel time and calculate direct elasticity measures. Our results provide useful instruments to evaluate policies that promote the use of more sustainable modes of transport in natural sites. Such policies should be considered as priorities in many national parks, where negative transport externalities such as traffic congestion, pollution, noise and accidents are causing problems that jeopardize not only the sustainability of the sites, but also the quality of the visit.
Using a dataset with transport choices of the same set of individuals (college students from University of La Laguna), we built a novel three waves panel data around a tramline implementation in the Santa Cruz-La Laguna corridor in Tenerife, Spain. The first two waves were conducted in 2007, just before the tram implementation. They collect information about Revealed Preferences (RP) of actual transport mode choices (car, bus and walk) and about Stated Preferences (SP) in a simulated scenario considering a hypothetical binary choice between the tram and the transport mode currently chosen by the students. The third wave gathers information about RP in 2009, two years after the tram started operating. With this information, we estimate several multinomial logit models and panel mixed logit models with error components. The aim of this paper is to evaluate how the estimation of the Values of Travel Time Savings (VTTS) changes when comparing the results obtained with models that only consider information before or after the tram implementation with that obtained with a panel data approach using the three waves simultaneously (RP/SP in 2007 and RP in 2009). We obtain a better statistical fit to data and, according to our study context, more reasonable VTTS using a panel data approach combining before and after information and both revealed and stated preferences. Our results suggest that when a new transport mode is implemented, the VTTS obtained with models than only consider prior or later periods of time can be underestimated and hence lead to wrong valuations of the benefits associated with the new alternative, even when stated preferences are used to anticipate the change in the transport system.
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