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High congestion and pollution costs in urban areas across the globe.
Today, 10 million trips occur in urban areas every day. Traffic congestion is an issue for large agglomerations around the world. Urban road congestion arises mainly during peak commuting hours. As economic activities are spatially concentrated due to agglomeration economies, workers gather in cities close to their workplace. These economies depend on the traditional trade-off between scale economies and transportation costs (Brueckner, 2011). Congestion costs matter as households and firms waste time and fuel. Firms lose both productivity and efficiency based on their location and their business sector, whether their workers arrive late for work. Furthermore, logistics businesses suffer from delivery delays due to extra travel time within a city.
Congestion costs. The economic costs are relatively high. Vehicle owners have direct costs related to purchase, maintenance, value depreciation, fuel consumption and parking. In addition, there are indirect costs, such as congestion costs, which are measured as wasted time and fuel in traffic jams. In 2001, Americans spent 161 minutes on average per day in a private vehicle (Duranton and Turner, 2011). There was a decline in travel time due to the economic crisis from 2011 to 2013 (CEBR, 2014). However, economic activity recovered. Today, American cities remain among the most congested in the world. For instance, a commuter living in Los Angeles spends 102 minutes a day in rush hour traffic (Cookson, 2018). In France, 37.2 million vehicles were in circulation according to the survey “Transport et d´eplacements 2008” (CGDD, 2010). Daily travel time has reached 56.4 minutes in France in 2008 (CGDD, 2010). The distance traveled by each French person (active and mobile) grew by 8 kilometers between 1982 and 2008, which is an increase of 45% in 26 years. A densely populated area such as Paris saw home-to-work travel times hit 75 minutes on average in 2008, despite the fact that traveled distances are shorter than they were in 1982. Paris’ commuters wasted 55 hours on average in traffic for home-to-work trips in 2013 (CEBR, 2014). We can observe the discrepancy between travel times for work-related trips during peak and off-peak hours for the city of Lyon in 2014. Travel times increase with the distance from the CBD. It is straightforward to area with respect to the distance from Central Business Districts (CBD) in 2014. check that peak hour travel times are higher than off-peak. A driver spends 26 minutes for an average round trip in off-peak hours, while it takes 32.4 minutes during peak hours. That is a 25% increase. In addition, traffic congestion induces carbon dioxide emissions stemming from idling vehicles.
Pollution costs. The growth of GHG emissions due to the transportation sector is driven especially by the increasing use of private cars and declining densities in urban areas since the mid-1950s (OECD 2014a). More than one billion motor vehicles (private cars, buses and trucks) have been in circulation since 2010, and the demand for private vehicles, driven by emerging countries, will increase in the future (Rode et al., 2014).
According to a “business as usual” scenario, 800 million additional vehicles will complete the global vehicle fleet by 2050 (UNEP, 2011). The scientists of the Intergovernmental Panel on Energy and Climate Change (IPCC) anticipate a doubling of GHG emissions by 2050. This increase is mainly due to the rapid urbanization of emerging and developing countries (UN, 2014)7. Transportation is responsible for a quarter of carbon dioxide (CO2) emissions from energy consumption in all sectors. We mainly take into account the CO2 because this gas contributes the most to greenhouse effect, according to the International Energy Agency (IEA, 2012). In addition, overall energy demand from the transport sector will grow by an average of 1.3% per year by 2035. This pollution stems from the increased use of private cars affecting megapolises in emerging countries (IEA, 2012). Health effects are dramatic. Air pollution by various greenhouse gases and fine particles cause premature deaths of 3.2 million people each year (OECD, 2014b). Developed and developing countries alike are both affected.
Policies aiming to lower GHG emissions and congestion in urban areas.
Urban policies may efficiently curb GHG emissions by mobilizing numerous actors such as local, regional and national public authorities (OECD, 2014a). Development of compact cities providing efficient public transport service and improved infrastructure related to soft (i.e., non-motorized) modes of transport is promoted by the IPCC. Indeed, the higher the share is of transport modes that use less carbon for trips involving people and goods, the lower the CO2 emissions per capita (Bongardt et al., 2013). A mix of land policies and urban planning oriented toward a limited space for private cars and a proximity of residential and business locations enable a city’s carbon footprint to be reduced (Suzuki et al., 2013). Policies of congestion management through tolls arise only in a few cities worldwide (London, Singapore, Stockholm, Trondheim, Oslo, etc.) as pointed out by Fosgerau and De Palma (2013). Political concerns and electoral cost of unpopular initiatives drive this lack of tax implementation (Fosgerau and De Palma, 2013).
Taxation. Taxation (positive or negative)8 is a means at the disposal of city council members to achieve the objective of urban sustainability. It enables consumers’ behavior to be changed by influencing them to purchase and/or use virtuous carbon transport modes. A “carbon” tax or an urban toll aims to charge for the negative externalities (congestion, accidents, air pollution) that come from purchasing vehicles that do not meet certain environmental standards by individuals who impose them on the community. However, this strategy remains ineffective if the tax does not correspond to the marginal cost as with fuel taxes (Rode et al., 2014). Implementing congestion charging schemes is often advocated as a solution to internalize the social costs of transport (GAO, 2012; Eliasson and Mattsson, 2006). These congestion charges have two objectives: (i) curbing the quantity of negative external effects (congestion, pollution, GHG emissions), and (ii) providing financial resources in order to develop an alternative supply of public transport. Singapore, Oslo, Stockholm and London have implemented urban tolls and pursue anticongestion policies. In addition, public transport is promoted. Since the introduction of an urban toll in London in 2003, traffic has declined in the zone where commuters incur a tax (Santos, 2005). Toll revenues have been used to finance improvements in the public transport system (frequency, servicing, etc.). Norway has set up cordon tolls in Trondheim, Oslo and Bergen, for example. This system has also made it possible to reduce traffic in city centers but also to finance large-capitalization infrastructure such as tunnels and bridges. Singapore is a city-state that implemented its urban toll in 1975 to manage traffic flow while promoting public transport, carpooling and soft modes of transportation (e.g., cycling and walking). The ERP system in Singapore is a time-varying toll charging commuters more during peak hours in the morning (7-8 am) and in late afternoon (6 pm). These urban toll policies are rare and have reduced traffic flows and CO2 emissions in the areas concerned (Fosgerau et De Palma, 2013). However, they have perverse effects such as shifting congestion to other roads that are free of charge (Santos, 2005). In addition, the increase in housing prices in the areas subject to urban tolls affect the well-being of tenants living in those areas (Tikoudis et al., 2015; Segal and Steinmeier, 1980). Spatial allocation is not optimal in the short run for given location when taxes are implemented. Firms and households location adjustments in the long run matter.
Economic and environmental implications of different urban spatial structures.
Compact cities bring people physically closer to their workplace and economic activities between them creating agglomeration economies (Fujita and Thisse, 2013). However, con- gestion and pollution externalities do exist in urban areas and can reduce the interest of densification policies. The level of infrastructure dedicated to public transport is in competition with the space used by private vehicles. In a study on several cities worldwide (e.g., London, Los Angeles, and Hong Kong), Newman and Kenworthy (1989) demonstrate a negative relationship between population density level and energy used stemming from transport flows. Indeed, the use of low-emission transport modes (cycling, public transport, walking) has increased in densely populated areas (CGDD, 2010). Sparse cities with dispersed urban functions such as Houston or Los Angeles require ownership of private vehicles. This need is also explained by the low cost of transportation for commuting by car (Glaeser and Kahn, 2004). Therefore, the urban space in central business districts is occupied mainly by parking spaces (Manville and Shoup, 2004). In addition, an increase in road infrastructure investment allowing for the construction of new lanes in urban areas leads to a relocation of city dwellers to the outskirts of metropolitan areas (Baum-Snow, 2007). These effects must be taken into account by urban planners because this infrastructure is built to last, while job and household relocations can be faster. Therefore, land use decisions must be analyzed in relation to transport systems within an urban spatial structure. The decentralization of jobs in monocentric cities has ambiguous effects on average traveled distances and home-to-work travel times. Several studies show that the location of jobs in secondary business districts reduces distances and travel times against the CBD gathering the predominant economic activities (Giuliano et Small, 1993; Veneri, 2010; Alpkokin et al., 2008). In Germany, Gutz et al. (2009) compare the cities Frankfurt and Stuttgart, which have a polycentric form, with Munich and Hamburg, which are monocentric. The authors observe shorter traveled distances for commuters living in Frankfurt and Stuttgart. However, opposite effects are also observed (Naess et Sandberg, 1996). The dispersion of jobs led to mismatching problems in Paris, Lyon and Marseilles from 1990 to 1999, increasing the average traveled distances (Aguil´era, 2005). In addition, progressive decentralization of employment has also led to an increase in home-to-work distances as jobs were relocated to low-density areas from 1986 to 1996 in Barcelona. Scholars have noted an absence of a regional policy for the development of residential land, and high car-use dependency has led to this result (Mu˜niz et Galindo, 2005).
Impact of anti-congestion policies on urban forms and environment.
In discussing the stakes of the new economic geography (NEG), Fujita and Krugman (2004) agreed that transportation costs are substantial to explain location decision of firms and households. Raising urban costs to internalize externalities of congestion may improve the allocation of resources but also have an impact on location decisions in the long run.
First, economists have assessed short-term consequences of implementing an urban toll, economists have focused on traffic, welfare effects, modal choice conditions and pricing equity (Eliasson and Mattson, 2006; Raux and Souche, 2004). They have also studied long-term effects regarding transport preferences and GHG emissions (Bhatt, 2011).
Short-term consequences. In the seminal work of Solow (1972), the author determines a rent profile that is more convex in the case in which congestion is taken into account. This means that rents become more expensive close to the CBD and relatively less at the edge of the city. Accordingly, households locate in smaller housing spaces near their workplace than at points farther away. Solow obtains a similar result in his subsequent paper in which he slightly changes the model (Solow, 1973). When congestion costs are introduced, land rents rise at all locations for a given city size. However, when housing density adjusts the city size expands. In the monocentric model, seven cases are analyzed where the pure distance costs and the congestion costs vary in the annual transportation cost function with the help of numerical simulations. The fraction of income spent on housing space after travel costs and the fraction of land allotted to residential use shift as well. When congestion costs are high, the diameter of the monocentric circular city increases gently. Some simulations are performed in which the fraction of land allotted to housing is low and increases from the CBD to the periphery. The simulations yield a flattening effect of the rent profile and a sprawling effect of the city boundary (Solow, 1972). Solow (1972) is also concerned with the optimal fraction of land that can be allotted to the road system. In his seminal paper, he demonstrates that the best share between roadsand housing is a fraction of land allocated to residential use yielding a minimum to the rent at the edge of the CBD. But his answer does not lead to an analytical solution, and he therefore leaves it for further research. Accordingly, in a subsequent study he carries out a cost-benefit analysis comparing shifts in the fraction of land use with the help of numerical simulations (Solow, 1973). Since no congestion tolls are implemented, land values represent diverse private transport costs. Households face a unique trade-off between rent and travel costs and do not take into account the social cost imposed to other families. Numerical simulations are performed to compare three distinct planning decisions regarding the fraction of land devoted to housing and its effects on average annual rent, travel costs and welfare per household. The cost-benefit analysis leads to roads’ overcapacity within the city, especially close to CBD since market land values are distorted through the lack of a road-pricing scheme (Solow, 1973).
Regarding land use regulation in an urban model, a city planner is commonly used as a municipality for Solow (1972) or a manager for Arnott (1979). The former must choose the best allocation of land between roads and housing, and the latter must minimize its variable resource costs to supply the whole population in lot size, transportation and other goods consumed by each resident. Arnott (1979) demonstrates that transport improvement such as a road widening induces indirect costs that should be considered even though Solow (1973) ignored them. Indeed, it causes larger traffic flows than what existed previously and avoids an over-evaluation of land for both road and residential use. Transport upgrades in an unpriced road-scheme create a distorted urban economy in a long-run competitive equilibrium. Accordingly, indirect costs must be taken into account carefully in the cost-benefit analysis as it relates to optimal capacity allocated to roads.
Table of contents :
General introduction
1 Motivation
1.1 High congestion and pollution costs in urban areas across the globe.
1.2 Policies aiming to lower GHG emissions and congestion in urban areas.
1.3 Economic and environmental implications of different urban spatial structures
2 Literature review
2.1 Modeling the polycentric city
2.2 Impact of anti-congestion policies on urban forms and environment.
3 Outline
3.1 Chapter 1: Urban spatial structure, transport-related emissions and welfare
3.2 Chapter 2: Efficiency of road pricing schemes with endogenous workplaces in polycentric city
3.3 Chapter 3: Commuting and urban forms: case study of French municipality areas
4 References
1 Urban spatial structure, transport-related emissions and welfare.
1 Introduction
2 A simple model
3 The monocentric city
4 The polycentric city
5 Discussion
5.1 Extending the city vertically
5.2 Endogenous wage
5.3 Role of modal choice and congestion
6 Conclusion
7 Appendix
2 Efficiency of road pricing schemes with endogenous workplaces in poly- centric city.
1 Introduction
2 The model
2.1 The city
2.2 Households
2.3 Congestion costs and transport infrastructure
2.4 Urban toll
2.5 Wages
2.6 Welfare
3 The monocentric city
4 Decentralization of jobs and welfare
4.1 The polycentric city
4.2 Equilibrium allocation and optimal location of jobs
5 Polycentric city and road pricing schemes
6 Comparisons between road pricing schemes
6.1 Efficiency of the three road pricing schemes in the polycentric city .
7 Discussion
7.1 Incidence of modal choice on congestion and urban structure .
8 Conclusion
3 Mobilit´es pendulaires et formes urbaines: cas des aires urbaines fran¸caises m´etropolitaines.
1 Introduction
2 Donn´ees sur les d´eplacements domicile-travail et les aires urbaines
2.1 D´eplacements domicile-travail
2.2 La mesure des formes urbaines
2.3 Les variables de contrˆole
3 ´Evolution des distances moyennes domicile-travail parcourues et formes urbaines
4 Temps de trajet `a l’heure de pointe et formes urbaines en 2014
4.1 Influence de l’aire urbaine
4.2 Niveau communal
5 Conclusion
6 Annexe
General conclusion
List of tables
List of figures
