Understanding the Linkages between Traffic Congestion and Environmental degradation: What does the theory tell us?

Background and Significance

Transport is a major global, networked industry facilitating globalisation and integration of the world economy. It contributes to 5-6 % of GDP in India and makes significant indirect contribution to GDP through logistic-intensive sectors as well as employs a large number of people. Transport generates wider economic benefits (WEBs) by facilitating agglomeration economies, enhancing competition, widening labour market choices, promoting density and causing land use changes. Agglomeration externalities are not captured in the standard cost-benefit appraisal of transport investment projects. There are certain productivity gains occurring as a result of improvements in transport that arise through city size. These “wider economic benefits” should be included in the cost-benefit appraisal of transportation project.

It is now well recognized that traffic congestion as a form of negative urban externality is a pressing reality. It is a major cause of concern for both developed and developing nations. Reportedly in India, national highways were expanding at the annual growth rate of 7.3 percent during 2010-11 to 2015-16. The total number of personal vehicles was reported to be 230 million in 2016, of which 51 million-plus cities accounted for 70.93 million of registered motor vehicles. The implication is that the growth of traffic outpaces the provision of road capacity and there is a modal shift from public to private vehicles. TERI, 2014) provides three reasons for this shift to growing ownership of motor vehicles. These include (a) savings in travel time in motor vehicles, (b) the comfort and the convenience associated with motorized modes of transport, and (c) a perception that motorized vehicles are safer compared to non-motorized modes of transport (e.g., bicycles). Moreover, congestion with high land rent induces outward growth of cities near the city edges. The standard economic principle of Transport correctly suggests that urban congestion occurs when there is an excess of demand over supply for transport, resulting in an egregious social outcome.

In Indian cities, congestion occurs primarily as a result of poorly constructed roads, improper land use, poor lane discipline, poor implementation of urban planning schemes and haphazard parking. Rao and Rao (2012) posited that urban congestion originates both at ‘micro’ and ‘macro’ levels. At the ‘micro’ level, congestion occurs as a result of too many vehicles running at the same time in limited road space. At the ‘macro’ level, congestion occurs due to faulty land use patterns, car ownership, and other such dynamics. In India, congestion occurs primarily as a result of poorly constructed roads, improper land use, poor lane discipline, haphazard parking etc. This calls to attention the need for a dual approach to enlarge the road capacity at ‘micro’ level and congestion pricing at the ‘macro level’. It is equally important to note that the idling of vehicles at traffic signals and decreasing speeds not only causes economic burden on commuters but also negatively impacts Environment and can have serious health implications for commuters.

Firstly, the economic burden as can be understood from the ‘costs’ of travel, which is comprised of two components, i.e., monetary and time cost. Research suggests that monetary costs largely depends on “local economics, traffic policy and management”, whereas time cost depends more on “network structures, traffic control and demand-supply fluctuation” (Lu, 2013). A considerable quantum of studies undertaken in the past unequivocally view the monetary and time cost value associated with congestion externality to be significant. For instance, a study done in the national capital of New Delhi by (Davis, Joseph, Raina, & Jagannathan, 2017) estimates the monetary costs associated with traffic congestion to be close to Rs. 54,000 crores in 2013, which is projected to increase to US$ 14658 million by 2030. The implementation of odd-even scheme in Delhi achieved huge feat in terms of traffic congestion reduction and resulted in an approximate reduction of 9-10% in travel time. In 2020, Delhi’s Master Plan 2021 aims to attract 80% of road travel to public transport by 2020 in New Delhi. Another set of studies conducted in Bengaluru, Mumbai, Pune and Kolkata exhibit similar monetary and time costs associated with traffic congestion. A panel data analysis of 2000 samples suggest that the average speed in the Bangalore was 10.9 miles per hour and it took 3.41 minutes to advance one kilometre (Kreindler, 2018).

Secondly, the impacts of affordability of private vehicles, increase in vehicular ownership and use, and increased distances travelled per capita and per vehicle have significantly contributed to urban air pollution and global warming. International Energy Agency (2019) reports that transportation alone account for 24% of direct CO2 emissions. The combustion of fuel in automobile vehicles such as cars, buses and trucks contributes to ? of total transport emissions (IEA, 2019).

Thirdly, research has shown a wide range of health implications of traffic congestion. This include but not limited to (a) the physical ailment (from dirt, noise and pollution), (b) road accidents, (c) emotional stress and anxiety. A study by (J. I. Levy, J. J. Buonocore, & K. von Stackelberg, 2010) evaluates the harmful impact of exposure to fine particulate matter PM2.5) in 83 individual urban areas using the traffic demand models. The finding suggests the monetized value of PM2.5-related mortality to be approximately $31 billion, which is projected to oscillate between $13 billion and $17 billion between 2020 and 2030.

Image: Roadway congestion in urban roads (source: ?Xi Zhang/Dreamstime.com)

What does the theory say?

The methodological developments in transportation economics began as early as mid-19th century. The pioneering studies by (Dupuit, 1844), (Pigou, 1920) and (Vickrey, 1959, 1963) on price settings in transport infrastructures have enriched our understanding of the present day congestion reduction schemes. For instance, the idea of congestion pricing comes from the standard economic theory of efficiency and externalities as proposed by Pigou (1920). Congestion pricing in present day world is widely accepted as the most efficient approach to put a cap on traffic congestion. The main operating schemes of congestion pricing at the international level include: London congestion charge (2003), Stockholm cordon charge (2007), High Occupancy Toll (HOT) lane facilities in the US, and Singapore’s Electronic Road Pricing system. Many studies have attempted to do a cost benefit analyses of these schemes and argue in favor of the implementation as it significantly reduces congestion from city centers. Studies suggests ‘London Congestion Pricing’ has resulted in a net benefit of $177 million/year post its implementation in 2003 and Stockholm’s congestion charge has resulted in net benefit of $94 million/year. Moreover, Tokyo’s Roadway Special Fund—comprising earmarked gasoline charges and vehicle registration fees—financed one-third of transit-related bridge and underpass construction to reduce traffic congestion and upgrade station facilities to improve local feeder access, pedestrian circulation, and street amenities, along with the land readjustment and urban redevelopment schemes.

Congestion pricing scheme is grounded in economic theory. Vickrey (1994) suggests when demand is relatively high during peak-time travel, higher congestion toll is required. Contrarily, during the off-peak travel, demand is relatively low thus low congestion toll will suffice. This means congestion charges should be based on the marginal social cost of trips by riders and be imposed on all vehicles. This can be understood more clearly with aid of a figure (to be updated later).

Lindsey and Verhoef (2000) suggested that the first best pricing rule of congestion pricing takes into account cost imposed directly by the user on other users, infrastructure providers, and society as a whole. However, this produces socially inefficient outcomes as the road users would pay more than the total costs of provision of the road system of the congestion affected areas but less than the total cost outside the congested areas. The second best pricing rule entails tolling roads based on two-parallel-routes network, drivers’ values of time and trip-timing preferences, speeds of vehicles through fine tolls, cordon tolls and/or step tolls.

As far as the road capacity increment is concerned, Mohring’s self-financing theorem states that the fare revenue with congestion will cover the construction cost exactly, i.e. congestion pricing breaks even. Therefore, an optimally designed and priced road would be self-financing and would effectively mitigate congestion. This however is dependent upon neutral scale economies, constant returns to scale and perfectly divisible capacity.

The literature of congestion pricing is also rife with many contradictions and repudiation in terms of the political viability of such schemes. Feitelson and Solomon (2004) suggest that bureaucracies and local politicians have little incentive to move to road user charging as they will face opposition from interest groups, users negatively affected (potential losers) but get little support from non-user or user beneficiaries (winners). In assessing the political acceptability of congestion pricing, Giliano (1992) argues that the main reason for non-introduction of road pricing are public scepticism about outcomes (i.e. the effectiveness of the schemes), concerns on impact of business and resistance to charges for what is free (i.e. conflicts with prior experience). Levinson (2000) suggests road pricing is a necessary prerequisite to congestion pricing, which once tolls are in place, is not nearly as difficult problem as placing tolls on untolled roads in the first place. And tolls are a rational financing mechanism for a sufficiently small jurisdiction, particularly with the advent of electronic toll collection systems. Road charging initiatives are more likely to get political support where non-local tax payers pay a large proportion of the tax. This is easier where the tax authority is small.

Concluding Remarks

Urban transportation in cities indisputably have always remained problematic. Specifically, India’s traffic congestion situation has been a grim challenge that must be acknowledged, confronted, and for which we must debate solutions. Regardless of existence of many strategies dealing with demand side issues of urban transportation, the fact remains that we cannot implement such congestion reduction strategy unless we first carry out research understanding commuters’ preferences. The preferences for varying travel modes, routes and service qualities/attributes matters for policymakers as there exists differences in (a) socio-economic and demographic characteristics, (b) travel habits and needs, (c) service delivery arrangements, (d) expectations of service levels, and (e) experiences with existing service providers (Pandit & Das, 2013).

There is an increasing focus on refining the development pathway to minimize the taxing tradeoff between development and climate change and determine the sweet spot to lift people out of poverty while reducing greenhouse emissions, especially in a developing country like India. In this context, the road pricing alone should be seen only as an element of a larger package of fiscal measures which alongside appropriate command-and-control instruments, is necessary to ensure a more optimal use of urban transport infrastructure. The major alternative ways to limit congestion is to deploy motor vehicle taxation, registration fees, cap-and-trade systems, and Pigouvian “green” taxes. Another crucially allied step is to craft policies conducive to public transit subsidy which will not only make private car users switch over to public transport, but also reduce emission. The need is for restructuring and finding the optimal sizes of buses and carrier vehicles so that it consumes less road space. In this context, there is a need for integrated transport policies to address problems of urban transport.

Additional Notes: When we look at transport, we see other forms of tradeoff as well. Suppose, we want to know which mode or which route a commuter chooses for his work trips. This can be modelled in the standard microeconomic utilization maximization framework. In microeconomic terminology, there exists a number of choices or alternatives with varying attributes and levels to be chosen by an individual for a given activity. The question that arises is which route would a commuter choose? The one with the shortest travel time but involves higher costs (in the form of tolls), or the one with higher travel times with relatively lower costs (either lower tolls or no tolls)? Essentially, this is a tradeoff problem of money and time. This was the topic of discussion for my M.Phil Dissertation.

References

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