Trends in Transportation Greenhouse Gas Emissions

H Christopher Frey

Introduction

Transportation accounted for approximately one-quarter of global CO, emissions in 2016. Global transport CO, emissions in 2016 were 71% higher than in 1990 (IEA, 2019a). Transport emissions have increased in all major regions of the world, as indicated in Table 1, with the largest increases occurring in Asia followed by the Americas. Globally, transport CO, emissions are primarily from on road vehicles. In 2016, on road transport contributed 74% of total transport CO, emissions (IEA, 2018a). Nearly two-thuds of global transport energy consumption is for passenger mobility, of which on road light duty vehicles were the largest energy consumer, followed by aircraft, buses, 2- and 3-wheel vehicles, and rail. On road freight trucks were the major energy consumer for height transport, followed by marine vessels, rail, and pipelines (EIA, 2016). Although accounting for only around 3% of global greenhouse gas (GHG) emissions, the GHG emissions from international aviation and shipping transport have approximately doubled from 1990 to 2016 (Olivier et al., 2017). Thus, the shares of different transportation modes with respect to total CO, and GHG emissions are changing with time.

Mobile emission sources include vehicles and infrastructure used for transportation of passengers and height, and non-transportation mobile sources (USDOT, 2010; Sims et al., 2014; Frey, 2018). Transportation modes include on road vehicles, aircraft, marine vessels, rail, and pipelines. Non- transportation mobile sources include construction, farm, and industrial equipment, lawn and garden equipment, logging equipment, and recreational vehicles. These non-transportation mobile sources are sometimes included in transportation emission inventories with other non-road sources, such as aircraft, marine vessels, and rail. However, not all organizations use the same categories of transportation sources for reported data or estimates of transport greenhouse gas emissions. Thus, there can be inconsistencies in the underlying scope of transportation greenhouse gas emissions reported by different agencies.

USA GHG emissions for transportation and other mobile sources in 2016 were 2.09 Tg CO, equivalent, of which 96.8% was CO, (EPA, 2018). These sources contributed 32% of total GHG emissions in the USA. On road light duty vehicles and on road medium and heavy-duty vehicles contributed 53% and 20%, respectively, of the total mobile source emissions. Commercial aviation was the only other source categoiy with more than 100 Tg CO, equivalent emissions. Marine vessels, rail, and pipelines each accounted for fewer than 50 Tg CO, equivalent. The total mobile emissions increased by 21.5 percent from 1990 to 2016. Most of this growth was from on road vehicles.

Table 1. Transport CO, emissions by global region from 1990 to 2016.

Year

Transport C02 emissions (GtCO,) by global region

Americas

Asia

Europe

Africa

Oceania

1990

1.84

0.78

1.16

0.11

0.07

2000

2.27

1.24

1.15

0,16

0.09

2010

2.38

1,87

1.25

0.26

0.10

2016

2.49

2.44

1.23

0.35

0.11

Source: International Energy Agency, https://www.iea.org/statistics/co2emissions/.

In Europe, transport GHG emissions, including international aviation but excluding marine shipping, account for one quarter of total GHG emissions and were 26.1% higher in 2016 than in 1990. Increasing demand for passenger and freight transport is offsetting improvements in vehicle efficiency. Road transport accounts for 82% of transport GHG emissions. To meet the 2050 goals of the European Commission will require the cutting of transport GHG emissions by more than two-thuds (EEA, 2018).

The global stock of on road vehicles grew from approximately 850 million in 2005 to 1.3 billion in 2015, with most of the growth occurring in Asia, as indicated in Figure 1 (OICA, 2019). The potential for growth is highly dependent on many factors, including the motorization rate, which is the number of vehicles per 1,000 people. The motorization rate and the national vehicle stock in 2015 is indicated for selected countries in Figure 2. The USA has the world’s largest domestic vehicle fleet and one of the highest rates of motorization, at 821 vehicles per 1.000 people. China, in contrast, has the second largest fleet but a motorization rate of only 118 vehicles per 1,000 people. With rapid economic development, the motorization rate in China will increase. The motorization rate depends on factors such as economic status but also land use patterns and access to other mobility options, such as public transportation. Approximately 10% of the global population accounts for 80 percent of motorized transportation activity. National transportation emissions typically increase as a country transitions from agricultural to industrial to sen-ice economies and as GDP per capita increases (Suns et al., 2014).

According to the reference scenario of the Energy Information Agency (EIA) in the USA, global transportation energy consumption could increase from a baseline of 104 quadrillion BTU (“quads”) in 2014 to 155 quads in 2040, with the vast majority of the projected increase occurring in countries outside of the Organization for Economic Cooperation and Development (OECD), especially in Asia (EIA, 2016). The use of liquid fossil transportation fuels is expected to increase, including increases of 13 quads for diesel. 10 quads for jet fuel, and 9 quads for gasoline. While the share of natural gas for transportation use is expected to increase fr om 3% in 2014 to 11% in 2040. the share of vehicles powered by electricity is expected to be only 1% in 2040, even accounting for some growth in the stock of plug-in electric vehicles (PEVs). Light duty vehicle energy consumption is estimated to increase by 15 quads, followed by 13 quads for trucks, 10 quads for aircraft, and 6 quads for marine vessels. The historical baseline and projected trend for energy use for on road transport by vehicle type is illustrated in Figure 3. The key implication of this scenario is that, without strategies to the contrary, large increases in fossil fuel consumption and increases in the associated GHG emissions are likely. According to Sims et al. (2014), GHG emissions from transport could increase at a faster rate than for any other energy end-use sector through 2050.

Strategies to reduce GHG emissions from transport can typically be categorized in terms of: (a) reducing the carbon intensity of transportation fuels and energy sources: (b) improving the energy efficiency of vehicles; (c) promotion of more efficient vehicle operation (e.g., routing, speeds); (d) shifting transportation activity to less GHG-emission intensive transport modes; (e) reducing the GHG emissions intensity embodied in transportation infrastructure: and (f) reducing travel demand. The feasibility and manner in which strategies under these categories har e been or could be implemented often differ substantially with regard to transportation mode. A detailed treatment of these issues is given by USDOT (2010), Sims et al. (2014) and others. This chapter provides a brief overview, based on recent

Number of operational on road vehicles globally, 2005 to 2015. Source

Figure 1. Number of operational on road vehicles globally, 2005 to 2015. Source: (OICA, 2019).

National motonzation rate (vehicles per 1,000 people) and vehicle stock (thousands) for selected countries

Figure 2. National motonzation rate (vehicles per 1,000 people) and vehicle stock (thousands) for selected countries

(OICA, 2019).

On-road vehicle energy consumption by vehicle type, 2010-2014 actual, 2015 to 2050 projected. Source

Figure 3. On-road vehicle energy consumption by vehicle type, 2010-2014 actual, 2015 to 2050 projected. Source: Energy

Information Agency (2017).

literature, regarding the current status, expected trends, and possible mitigation options for transportation and GHG emissions.

Transportation Demand and Mode Choice for Personal Mobility

Factors that encourage personal mobility mode choice in favor of private automobile travel include decreased street connectivity, lack of mixed laud use, a low local living score (i.e., not able to walk to amenities), low housing diversity score (e.g., residential and business areas are distinct and separate), low dwelling density, and lack of proximity to supermarkets. Factors that encourage more walking, cycling, or transit trips include more house diversity and greater dwelling density (Boulange et al., 2017). More compact communities are associated with less motorized travel. Car use is reduced as distance to transit is reduced (Ding et ah, 2017). Policies that promote walkability and transit, such as more investment in sidewalks and transit, could reduce automobile ownership per household (Shay and Khattak, 2012). More dense population and job density would reduce auto-dependent development (Kay et ah, 2014). Higher income levels are associated with higher car ownership, more use of cars for transport, and reduction in use of public transit (Shekarchian et ah. 2017). Transit-oriented development (TOD) would reduce demand for transport by personal automobile and promote modal shifts to buses or rail (Kay et ah, 2014). As an example, one of the wealthiest cities in the world, Hong Kong, has a very low motorization rate in large part because mass transit is ubiquitous, frequent, and inexpensive, and in part because laud is expensive and, therefore, the costs of parking a car are high.

Various pricing schemes can influence behaviors that affect energy consumption and emissions. Increasing the price of driving through various mechanisms may be more effective than only providing more access to transit (Kay et ah, 2014). CO, emissions pricing would be more effective than Vehicle Miles Traveled (VMT) pricing or a gas tax at reducing CO, emissions (Welch and Mishra. 2014). Other possible tax schemes focus on vehicle purchase, which would reduce the number of vehicles per household, or ownership, would which reduce the number of vehicles and YMT (Liu and Cirillo, 2016). Taxes on vehicle age (e.g., for an older SUV) might induce substitution of a newer vehicle that is lower emitting (Feng et ah, 2013). Singapore restricts vehicle ownership by issuing 10 year “certificates of entitlement” for a substantial fee to purchase a vehicle (Chu, 2015).

Other price schemes that might affect travel demand or encourage modal shifts include cordon pricing, congestion pricing, and parking pricing. Cordon pricing could be applied, for example, in a central business district in order to discourage use of private automobiles for work or shopping trips. In Singapore, “electronic road pricing” tolls that vary with traffic conditions are charged in a cordoned area with the goal of maintaining target speeds on expressways and arterial roads (Chu, 2015). Such policies might discourage discretionary trips more so than work trips. Although workers could switch to a transit mode, some may simply park outside of a cordon zone. Parking pricing may be less effective at modifying behavior than cordon pricing (Azari et ah, 2013).

Many schemes that might mitigate energy use and emissions may har e only marginal benefits. Based on comparisons using a sketch planning tool of the estimated effect of travel demand management strategies, such as flexible workliours. rideshare programs, and incentives to use transit, transit-oriented development, reduction in transit travel time or fares, and imposition of parking and mileage based fees, combinations of these policies were not likely to achieve CO, emissions reductions of more than 10% by 2050 (Mahendra et ah, 2012).

While built environment characteristics at both residential and job locations influence travel demand, the relative importance of these two factors may be different in China than in Western countries (Sun et ah, 2017). For example, “Danwei” compounds in China are co-located work and housing areas that were centrally planned. Laud use patterns in China are shifting to include housing that is market-driven, with more separation from job locations. These new housing patterns manifest the same relationships between land use and transport found in Western countries. However. Chinese cities differ from Western cities in being more populated and denser. Thus, they may not be likely to achieve the same motorization rates that are found in less dense Western cities. For example, traffic congestion in Beijing is already severe, with a car ownership level less than 50% of that in typical Western cities. Thus, perhaps mature motorization rates in China will not be as high as those in many Western countries (Wang and Zhou,

2017).

Other factors that could mitigate the need for motorized transport include sourcing localized products, prioritizing access for pedestrians and cyclists, and applying lessons learned from behavioral research regarding how and under what conditions people will choose to avoid making unnecessary motorized journals and making more use of new types of low-carbon transport (Suns et al., 2014).

 
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