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on Transport Economics |
| By: | Michel Noussan (Fondazione Eni Enrico Mattei) |
| Abstract: | The transport sector has rarely seen disruptive evolutions after the diffusion of the internal combustion engine, and today the European mobility is still heavily relying on oil derivates and on private cars. However, there is a significant push in cities towards more sustainable mobility paradigms, and digital technologies are playing a major role in unleashing possible alternatives to a car- and fossil-based mobility. Three major digital trends can be highlighted, with different levels of maturity and some potential synergies among them: Mobility as a Service, Shared Mobility and Autonomous Vehicles. The effects of these trends are also related to the strong push towards electric mobility, which currently appears as the most supported solution by companies and regulators to decarbonize the transport sector. This working paper discusses an investigation of the potential effects of digital transition, by means of a data-driven model for the calculation of the impacts of mobility demand in Europe in terms of primary energy consumption and CO2 emissions. The results show that digitalization may have a positive effect on energy consumption and CO2 emissions for passenger transport, given the strong efficiency improvements expected by technological development in the vehicles powertrains. The benefits are maximized if digital technologies are used towards a collective optimization, by increasing the share of available mobility options. Conversely, if digital technologies are limited to increase the quality of private mobility, the environmental benefits will likely remain very limited. Thus, there is a need of tailored policies supporting the right mobility models to fully exploit the potential benefits of digitalization. |
| Keywords: | Digitalization, Transport, Energy Consumption, Energy Modelling |
| JEL: | L91 O33 Q4 R41 |
| Date: | 2019–02 |
| URL: | http://d.repec.org/n?u=RePEc:fem:femwpa:2019.01&r=all |
| By: | Yue Zhang (China University of Petroleum); Qi Zhang (China University of Petroleum); Arash Farnoosh (IFPEN - IFP Energies nouvelles - IFPEN - IFP Energies nouvelles); Siyuan Chen (China University of Petroleum); Yan Li (China University of Petroleum) |
| Abstract: | The rapid development of electric vehicles can greatly alleviate the environmental problems and energy tension. However, the lack of public supporting facilities has become the biggest problem hinders its development. How to reasonably plan the placement of charging stations to meet the needs of electric vehicles has become an urgent situation in China. Different from private charging piles, charging station could help to break the limitation of short range. It also has a special dual attribute of public service and high investment. Therefore, a mathematically optimal model with two objective functions is developed to analyze the relationship between upfront investments and operating costs and service coverage of charging station system and it was solved by Particle Swarm Optimization. Besides, we take into account the conveniences of stations for charging vehicles and their influences on the loads of the power grid. Geographic Information System is used to overlay the traffic system diagram on power system diagram to find the alternative construction sites. In this study, a district in Beijing is analyzed using the proposed method and model. And the following suggestions are given: government should lead the construction of charging station; service ability needs to be enhanced; it is better to make more investment at earlier stage; constructions of charging stations can facilitate EV's development. |
| Keywords: | Multi-objective particle swarm optimization,GIS,Electric vehicle,Charging station |
| Date: | 2019–02–15 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-02009151&r=all |
| By: | Donna, Javier D. |
| Abstract: | This paper develops a structural model of urban travel to estimate long-run price elasticities. A dynamic discrete choice demand model with switching costs is estimated, using a panel dataset with public market-level data on automobile and public transit use for Chicago. The estimated model shows that long-run own- (automobile) and cross- (transit) price elasticities are more elastic than short-run elasticities, and that elasticity estimates from static and myopic models are downward biased. The estimated model is used to evaluate the response to a gasoline tax. Static and myopic models mismeasure long-run substitution patterns, and could lead to incorrect policy decisions. |
| Keywords: | Long-run price elasticities, Dynamic demand travel, Hysteresis |
| JEL: | L71 L91 L98 |
| Date: | 2018–11–05 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:92233&r=all |
| By: | Kai Li; Cheryl Long; Wei Wan |
| Abstract: | The study is motivated by two seemingly contradictory patterns observed in China’s airline industry with prevalent airfare caps. On the one hand, airfares tend to be higher and thus airfare caps are more likely to be binding in routes operated by fewer airlines; but on the other hand, it is in these same routes where airfare caps are more likely to be deregulated. To explain this apparent paradox, we build a simple theoretical model, which derives distinctive predictions from the public interest theory versus the capture theory of regulation. When empirically testing the model, we find more support for the public interest theory. In particular, Chinese regulators seem to genuinely care about public interests when making deregulation decisions, and their concern with market exit and the consequent failure to serve certain markets is the main reason for removing airfare caps in routes served by fewer airlines. In contrast to most studies that investigate consequences of deregulation in the airline industry, we explore the motivation for deregulation. We also contribute to the literature by emphasizing the role of universal service obligations in understanding regulatory reforms in developing countries. |
| Keywords: | Regulatory Capture; Public Interest; Airfare Cap; Airline Deregulation |
| JEL: | D7 L5 L9 |
| Date: | 2019–02–19 |
| URL: | http://d.repec.org/n?u=RePEc:wyi:wpaper:002399&r=all |
| By: | Thierry Mayer (Département d'économie); Keith Head (Sauder School of Business (Columbia University)) |
| Abstract: | This paper estimates the role of country/variety comparative advantage in the decision to offshore assembly of more than 2000 models of 197 car brands headquartered in 23 countries. While offshoring in the car industry has risen from 2000 to 2016, the top five offshoring brands account for half the car assembly relocated to low wage countries. We show that the decision to offshore a particular car model depends on two types of cost (dis)advantage of the home country relative to foreign locations. The first type, the assembly costs common to all models, is estimated via a structural triadic gravity equation. The second effect, model-level comparative advantage, is an interaction between proxies for the model's skill and capital intensity and headquarter country's abundance in these factors. |
| Keywords: | Offshoring; Gravity; Cars |
| JEL: | F1 |
| Date: | 2018–05 |
| URL: | http://d.repec.org/n?u=RePEc:spo:wpmain:info:hdl:2441/283ebth88t9q891sj72go9n0bv&r=all |
| By: | Mehdi Farajallah (ESC Rennes School of Business - ESC Rennes School of Business); Robert Hammond (NCSU - North Carolina State University [Raleigh]); Thierry Pénard (CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR1 - Université de Rennes 1 - UNIV-RENNES - Université de Rennes - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | We examine how price and demand are determined on peer-to-peer platforms and whether experience and reputation have the same impact as in traditional markets. We use data from the world's leading intercity carsharing platform, BlaBlaCar, which connects drivers with empty seats to riders. We find that pricing decisions evolve as drivers gain experience with the platform. More-experienced drivers set lower prices and, controlling for price, sell more seats. Our interpretation is that more-experienced drivers on BlaBlaCar learn to lower their prices as they gain experience; accordingly, more-experienced drivers earn more revenue per trip. In total, our results suggest that peer-to-peer markets such as BlaBlaCar share some characteristics with other types of peer-to-peer markets such as eBay but remain a unique and rich setting in which there are many new insights to be gained. |
| Keywords: | blablacar,intercity carsharing platform,peer-to-peer market |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:halshs-02012097&r=all |