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on Transport Economics |
| By: | Huwe, Vera; Gessner, Johannes |
| Abstract: | Widespread electric vehicle adoption is considered a major policy goal in order to decarbonize the transport sector. However, potential rebound effects both in terms of vehicle ownership and distance traveled might nullify the environmental edge of electric vehicles. Using cross-sectional household-level microdata from Germany, we identify rebound effects of electric vehicle adoption on both margins for specific subgroups of electric vehicle owners. As our data is cross-sectional, we resort to data-driven methods which are not yet commonly used in the economic literature. For the identification of changes in the number of cars owned after electric vehicle adoption, we predict counterfactual car ownership using a supervised learning approach. Furthermore, we investigate the effect of electric vehicle adoption on household mileage based on a genetic matching of households owning electric vehicles to similar owners of conventional cars. For the selection of covariates for matching, we contrast ad hoc variable selection based on the available literature with a data-driven variable selection method (double LASSO). We cannot verify asignificant increase in the number of cars owned for households with one electric and one conventional vehicle. For the subgroup of households who substitute the electric car for a conventional vehicle, electric vehicle ownership is associated with a significant reduction in annual mileage of -23% of the sample mean. The result indicates a strive for behavior consistent with the environmentally-friendly car choice rather than a rebound effect. Our results are subgroup-specific and may not generalize to the overall population. Methodologically, we find that data-driven variable selection identifies a refined set of covariates and changes the magnitude of the estimation results substantially. It may thus be considered a useful complement, especially in settings with limited theoretical or empirical knowledge established. |
| Keywords: | Rebound Effect,Electric Vehicle Adoption,Variable Selection |
| JEL: | R41 Q55 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:zewdip:20048&r=all |
| By: | Miquel-Àngel Garcia-López (Department of Applied Economics, Universidad Autónoma de Barcelona, 08193, Bellaterra, Spain); Ilias Pasidis (Barcelona Institute of Economics (IEB), Universidad de Barcelona 08034, Barcelona, Spain); Elisabet Viladecans-Marsal (Department of Economics, Universidad de Barcelona 08034, Barcelona, Spain) |
| Abstract: | Using data from the 545 largest European cities, we study whether the expansion of their highway capacity provides a solution to the problem of traffic congestion. Our results confirm that in the long run, and in line with the ’fundamental law of highway congestion’, the expansion in cities of lane kilometers causes an increase in vehicle traffic that does not solve urban congestion. We disentangle the increase in traffic due to the increases in coverage and in capacity. We further introduce road pricing and public transit policies in order to test whether they moderate congestion. Our findings confirm that the induced demand is considerably smaller in cities with road pricing schemes, and that congestion decreases with the expansion of public transportation. |
| Keywords: | congestion, highways, Europe, cities |
| JEL: | R41 R48 |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:uab:wprdea:wpdea2011&r=all |
| By: | Yoo, Sunbin; Koh, Kyung Woong; Yoshida, Yoshikuni |
| Abstract: | We investigate whether the contrasting set of transportation policies in Korea---reductions in fuel taxes and increases in diesel automobile prices---has decreased emissions. Using a random-coefficient discrete choice model and hypothetical policy sets, we estimate the automobile demand of consumers, the market share of cars by fuel type, and total emissions, assuming that consumer preferences for driving costs change over time. Then, we separately analyze the effect of each policy set on automobile sales and emissions, particularly carbon dioxide, nitrogen oxide, and particulate matter. Our analyses reveal that Korean consumers have become more sensitive toward fuel costs over time and that the emission consequences of Korean policies depend on consumer preferences. |
| Keywords: | Discrete Choice, Demand Estimation, Emissions, Transportation, Fuel Cost |
| JEL: | D1 D12 R4 R41 |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:103311&r=all |
| By: | Yihan Yan |
| Abstract: | In June 2014, Tesla, a leading manufacturer of electric vehicles, announced it would make its software and hardware available for free to other automakers. This paper analyzes the effect of Tesla's open source initiative on the plug-in hybrid and electric vehicle (PHEV) industry in the US. On the one hand, open source allows PHEV manufacturers to use the advanced technology of Tesla, which could lead to lower investment costs and a higher incentive to invest. Open source also partially removes the entry barriers and could attract more entrants and induce economies of scale, leading to decreased manufacturing costs. On the other hand, underinvestment of Tesla's rivals may occur as a result of free riding, which could result in slower quality improvements in the industry. I quantify these impacts by estimating a dynamic structural model, where players make investment and entry decisions to maximize discounted future returns. My results show that Tesla's initiative was beneficial for the industry and Tesla. I find a 60% drop in investment cost, and a decrease of 100 million in entry cost into the PHEV industry. Counterfactual analysis shows that, had Tesla not provided open source, the industry would have had 33% fewer PHEVs and Tesla would have had one billion less in profit. |
| Keywords: | Open Source, Dynamics, Quality, Differentiated Products, Discrete Choice, Automobile Industry |
| JEL: | L11 L15 L62 |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:bon:boncrc:crctr224_2020_218&r=all |
| By: | Comello, Stephen; Glenk, Gunther; Reichelstein, Stefan |
| Abstract: | Comprehensive global decarbonization will require that transportation services cease to rely on fossil fuels. Here we develop a generic life-cycle cost model to address two closely related questions central to the emergence of sustainable transportation: (i) the utilization rates (hours of operation) that rank-order alternative drivetrains in terms of their cost, and (ii) the cost-efficient share of clean energy drivetrains in a vehicle fleet of competing drivetrains. Calibrating our model framework in the context of urban transit buses, we examine how the comparison between diesel and battery-electric buses varies with the specifics of the duty cycle (route). We find that even for less favorable duty cycles, battery-electric buses will entail lower life-cycle costs once utilization rates exceed 20% of the annual hours. Yet, the current economics of that particular application still calls for a one-third share of diesel drivetrains in a cost-efficient fleet. |
| Keywords: | clean energy vehicles,transportation services,life-cycle cost,fleet optimization |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:zewdip:20054&r=all |
| By: | Pike, Susan PhD; Kazemian, Sara |
| Abstract: | Ridehail services such as Uber and Lyft present new, flexible travel options. Integrating these services with existing public transit could reduce costs, facilitate more transit use, and improve access. To realize these benefits, a growing number of transit agencies are exploring partnerships with ridehail and other shared-use mobility companies, such as bikesharing and carsharing services. Under such partnerships transit agencies typically subsidize shareduse mobility services for passengers connecting to transit stations or traveling when transit service is limited or unavailable. If successful, these partnerships could serve as part of a new model of environmentally sustainable, costeffective, and equitable public transportation. However, only a few jurisdictions have implemented successful partnerships. Transit agencies that have not pursued these partnerships have expressed concerns about cost, liability, regulatory issues, and data sharing. Little is known about what prompts some transit providers to pursue these partnerships while others do not. Researchers at UC Davis surveyed 37 transit agencies and interviewed seven transit agency professionals over two years to better understand why transit agencies pursue shared-use mobility partnerships, the factors that influence partnership formation, and barriers that prevent or slow the formation of partnerships. |
| Keywords: | Social and Behavioral Sciences |
| Date: | 2020–10–23 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt3jt98530&r=all |
| By: | Boeing, Geoff (Northeastern University) |
| Abstract: | This morphological study identifies and measures recent nationwide trends in American street network design. Historically, orthogonal street grids provided the interconnectivity and density that researchers identify as important factors for reducing vehicular travel and emissions and increasing road safety and physical activity. During the 20th century, griddedness declined in planning practice alongside declines in urban form compactness, density, and connectivity as urbanization sprawled around automobile dependence. But less is known about comprehensive empirical trends across US neighborhoods, especially in recent years. This study uses public and open data to examine tract-level street networks across the entire US. It develops theoretical and measurement frameworks for a quality of street networks defined here as griddedness. It measures how griddedness, orientation order, straightness, 4-way intersections, and intersection density declined from 1940 through the 1990s while dead-ends and block lengths increased. However, since 2000, these trends have rebounded, shifting back toward historical design patterns. Yet, despite this rebound, when controlling for topography and built environment factors all decades post-1939 are associated with lower griddedness than pre-1940. Higher griddedness is associated with less car ownership—which itself has a well-established relationship with vehicle kilometers traveled and greenhouse gas emissions—while controlling for density, home and household size, income, jobs proximity, street network grain, and local topography. Interconnected grid-like street networks offer practitioners an important tool for curbing car dependence and emissions. Once established, street patterns determine urban spatial structure for centuries, so proactive planning is essential. |
| Date: | 2020–10–09 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:t9um6&r=all |
| By: | Ángel Marrero (Universidad de La Laguna); Gustavo A. Marrero (Universidad de La Laguna); Marina González (Universidad de La Laguna); Jesús Rodríguez-López (U. Pablo de Olavide) |
| Abstract: | In Europe the transport sector accounts for more than 27% of total CO2 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 CO2 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 CO2 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. |
| Keywords: | Road transport; CO2 emissions; Convergence; Dynamic panel data models. |
| JEL: | Q43 C23 Q54 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:pab:wpaper:20.06&r=all |
| By: | Liliana de Abreu (Department of Politics and Public Administration, University of Konstanz); Anke Hoeffler (Department of Politics and Public Administration, University of Konstanz) |
| Abstract: | In this paper we determine the cost of fatalities resulting from road traffic injuries (RTIs) in South Africa. We express the costs in terms of reduced life expectancy and years of potential life lost (YPLL). Our main data source is the Injury Mortality Survey, a retrospective descriptive study carried out in 2009 in South Africa. Using the mortality rates by sex and age from the mortuary data we calculate actual life expectancy at birth. In our counterfactual analysis we assume a reduction of 15% reduction per year in road fatalities for a period of 10 years. A comparison of the estimated actual and counterfactual life expectancies suggests that the average gain in life expectancy at birth would be 0.58 years. Since the overwhelming majority of road traffic fatalities are male (75.7%), there is a considerable gender difference. Men would gain 0.85 years while women would gain 0.30 years in life expectancy, closing the gender gap in life expectancy by about 14%. We then discuss how a reduction in RTIs could be achieved. South Africa’s legislation already covers all important aspects of road safety (e.g. seat belt use, drink driving restrictions, speed limits, infrastructure improvements), however, enforcement is relatively weak and should be improved. There are a raft of measures that have been well researched in other countries, most interventions aim to modify the behaviour of road users and have been found to be cost effective. In addition to stricter enforcement, evidence from social science suggests that compliance could be increased through a change in social norms regarding road usage. |
| Keywords: | road traffic fatalities, road safety, life expectancy, South Africa |
| JEL: | I15 I18 J19 O55 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:sza:wpaper:wpapers353&r=all |
| By: | Behram Wali; Asad Khattak |
| Abstract: | With the advent of seemingly unstructured big data, and through seamless integration of computation and physical components, cyber-physical systems (CPS) provide an innovative way to enhance safety and resiliency of transport infrastructure. This study focuses on real world microscopic driving behavior and its relevance to school zone safety expanding the capability, usability, and safety of dynamic physical systems through data analytics. Driving behavior and school zone safety is a public health concern. The sequence of instantaneous driving decisions and its variations prior to involvement in safety critical events, defined as driving volatility, can be a leading indicator of safety. By harnessing unique naturalistic data on more than 41,000 normal, crash, and near-crash events featuring over 9.4 million temporal samples of real-world driving, a characterization of volatility in microscopic driving decisions is sought at school and non-school zone locations. A big data analytic methodology is proposed for quantifying driving volatility in microscopic real-world driving decisions. Eight different volatility measures are then linked with detailed event specific characteristics, health history, driving history, experience, and other factors to examine crash propensity at school zones. A comprehensive yet fully flexible state-of-the-art generalized mixed logit framework is employed to fully account for distinct yet related methodological issues of scale and random heterogeneity, containing multinomial logit, random parameter logit, scaled logit, hierarchical scaled logit, and hierarchical generalized mixed logit as special cases. The results reveal that both for school and non-school locations, drivers exhibited greater intentional volatility prior to safety-critical events... ... |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2010.12017&r=all |
| By: | Foster, Gillian Joanne; Lamura, Maddalena Josefin; Hackel, Jakob |
| Abstract: | The report ”Kultur-Token Sustainable Business Model:Visualizing, Tokenizing, and Rewarding Mobility Behavior in Vienna, Austria” is the result of an ongoing scientific collaboration between the Research Institute for Cryptoeconomics and the City of Vienna. This case study uses business modeling to understand the project Kultur-Token and serves as a strategic tool for both the management team as well as external stakeholders. The report documents the process of development of the Kultur-Token, describes it’s purpose and features, the goals and stakeholders involved until the suspension of the test phase at the end of march 2020, as the Covid-19 pandemic restricted both mobility and cultural activities. |
| Keywords: | Kultur-Token, App, mobility, sustainable business model, tokenizing mobility behavior, Vienna, cryptoeconomics, low carbon mobility, token, culture, rewards, mobility tracking, public transport, mobility behavior goal, City of Vienna |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:wiw:wus051:7777&r=all |
| By: | Dickinson, Jeffrey |
| Abstract: | This paper expands on our understanding of the lights-income relationship by linking the newest generation of nighttime satellite images derived from the Visible Infrared Imaging Radiometry Suite, VIIRS, to nationwide, panel data on population and income from 2012-2018 for both Brazil and the United States including 3,104 US counties, and 5,570 munic\'ipios. I leverage the quality and frequency of those data sources and the VIIRS lights images to decompose the links between population changes, GDP changes, and nighttime lights changes at the county and munic\'ipio level. I find decreasing marginal effects of GDP on nighttime light as well as decreasing marginal effects of population on nighttime light, a result which holds across many specifications and that is robust to sub-sample analysis and placebo tests. Interactions among controls also appear to be present. Using sub-sample analysis, I also find that nighttime light does a poor job of capturing less-wealthy areas. Finally, I use a between-county estimator to identify the effects of time-invariant infrastructure features on night-time light. Roads, rail, ports, airports, and border crossings I find to be strong contributors to increases in light. |
| Keywords: | night-time light, GDP, population, infrastructure, regional development |
| JEL: | C82 O51 R10 R11 R12 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:103504&r=all |
| By: | Morimoto, Yu |
| Abstract: | In this research, it is investigated whether passengers living in a city with a local public airport have attachment to the airport and tend to use it. Focusing on the Greater Kansai area with three airports and Kobe City that owns Kobe Airport as an example, an empirical analysis is conducted by Nested logit model using micro data. The result of the basic model shows that passengers living in Kobe city prefer Kobe Airport compared to other passengers. Additional analysis based on a questionnaire survey revealed that passengers who are attached to Kobe Airport choose it because they love it, which means that the non-economic factor of attachment influences passengers’ decisions. The results of this research suggest that enhancing attachment to the airport might be a possible idea for policy makers of airport cities to increase passengers of it. |
| Keywords: | Airport choice, Multiple airport region, Airport city, Attachment, Nested logit model |
| JEL: | L93 |
| Date: | 2020–09–12 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:103442&r=all |
| By: | Clive Bell |
| Abstract: | In the presence of agglomeration economies, the effects of a rural roads programme depend not only on the reduction in transportation costs, but also on the form of labour mobility. When financed by a poll tax on rural households, the wage will rise, accompanied by some return migration, provided both cross-price effects in production and consumption and agglomeration economies are sufficiently small. With empirically plausible elasticities of agglomeration economies, urban households may be worse off. |
| Keywords: | Rural roads, Profitability, Transportation, Agglomeration |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:unu:wpaper:wp-2020-137&r=all |