Essays on transport economicsco2 emissions, values of travel time and inertia effect

  1. Ángel Simón Marrero Llinares
Supervised by:
  1. Rosa Marina González Marrero Director
  2. Gustavo A. Marrero Director

Defence university: Universidad de La Laguna

Year of defence: 2019

Department:
  1. Economía, Contabilidad y Finanzas

Type: Thesis

Abstract

Transport is a strategic sector for the economy and has a strong impact on economic growth and welfare, but also produces several negative externalities due to, among other causes, the excessive use of cars. Both the economic impact and the negative externalities associated with transport have generated an increased interest among researchers in the field of transport economics. In order to evaluate the balance between positive and negative effects of transport, policy evaluation studies are needed. This thesis focuses on the application of novel methods in transport demand analysis which are useful in the evaluation of transport policies. The thesis is divided in four chapters which contribute to the scientific development of the field. Chapter 1 focuses on the aggregated transport demand. Using alternative approaches, we examine the concepts of β, σ and club convergence in road transport CO2 emissions per capita of a sample of 23 European Union countries over the period 1990-2014. We also estimate dynamic panel data models with interaction terms in order to explain the factors determining the evolution of the emissions and the effect of a set of variables on the speed of convergence. Our results show, first, a reduction in the disparities of emission levels, and a conditional convergence process during the period under study; second, the evidence that this process is conditioned by factors such as economic activity, fuel price or annual average distance travelled by cars. Further, some of these variables appear to have a significant effect on the speed of convergence, a result that may have significant implications for the cross-country impact of the European policies on climate change currently in place. The next three chapters focus on the disaggregate transport demand, specifically on the individual travel mode choice, by using different applications of discrete choice models. We conduct surveys on Revealed Preferences (RP) and Stated Preferences (SP) and estimate different specifications of discrete choice models. The case study of Chapters 2 and 3 is a new tramline implementation in Tenerife, Canary Islands (Spain) where we analyse how the individual preferences change with the introduction of the new mode. We build a novel panel data with information about transport choices of the same group of individuals (college students). Just before the implementation of the tramline, we collect information about RP of transport mode choices and about SP in a simulated scenario with the tram as a hypothetical alternative. Two years after the tram started operating, we gather information about RP to ascertain the impact of the new tramline in the student mobility patterns. With this information, we estimate several panel mixed logit models with error components. The main objective of Chapter 2 is to evaluate the effect of using partial information on the estimation of the Values of Travel Time Savings (VTTS). We conclude that the estimation of the VTTS changes when comparing the results obtained with models that only consider information before or after the tramline implementation with that obtained with a panel data approach using all the information simultaneously. Further, we obtain a better statistical fit to data and, according to previous evidence in our study context, more reasonable values of travel time using a panel data approach. 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 changes in the user preferences. The purpose of Chapter 3 is to analyse the influence of past behaviour on the current transport mode choices. To do this, we examine the inertia effect, a factor usually not considered in discrete choice models of travel demand. Around the implementation of new transport modes, the majority of studies on inertia have relied on combining RP and SP obtained prior to the implementation and measuring the inertia as the effect that the real choices (RP) have on the choices in the hypothetical scenarios (SP). In our case, we find a significant inertia effect only between the previous and posterior implementation RP observations, which increases the probability of choosing the car once the tram starts running. However, we do not find inertia effect on the previous implementation RP-SP information, hence taking into account only this information might have led to wrong conclusions about the effect of the transport policy. Furthermore, we compare models with and without inertia and conclude that the models with inertia provide better fit to data, smaller direct car choice elasticities and increasing asymmetric effects between the car and public transport cross-choice elasticities. Lastly, Chapter 4 adopts a novel methodological approach to estimate the recreational value of a natural site. To calculate this value, estimations of the visitor values of travel time are needed. In the recreational demand literature, the most common approach for the calculation of the values of travel time has been the use of different proportions of the wage rate. However, criticisms of this method abound because in a recreational trip the relevant measure is the opportunity cost of leisure time rather than work time. In this chapter, we obtain the value of travel time through the trade-off between time and money considered by the tourist visitors when choosing the transport mode to access the natural site, and we present the first calculation of the recreational value of the Teide National Park. Specifically, using a revealed preference survey, we estimate mixed logit models accounting for random preference heterogeneity, derive travel time values and incorporate them into a zonal travel cost model. This approach allows us to estimate different time values depending on transport mode and stage of the trip and shows that the use of discrete choice models instead of the wage rate approach has a strong impact on the recreational value calculated.