Related work

Based on the previous studies, this section sets out the methodologies for transportation CO2 emission calculation, the relationship expected between socio-economic status, householdspatial distribution, urban form, and transportation CO2 emissions. These highlight the factors and characteristics which affect the CO2 from commuters and inform the study approach for
the two case study cities.

2.1 Transportation CO2 emission calculations

Transportation CO2 emissions are produced at various stages of the transportation development process, such as vehicle manufacturing, infrastructure construction and operations, traffic operations, and infrastructure maintenance. For road transportation, the fuel consumption during the traffic operation stage accounts for 95–98 % of the total fuel consumed in the infrastructure construction, operations, maintenance, and traffic operation stages (Araújo et al. 2014). For metro operating at design occupancy level, the CO2 emissions of the metro operation account for 98 % of the total CO2 emissions during the metro infrastructure and facility construction and metro operation (Zhang et al. 2014b). For buses operating at design occupancy level, the CO2 emissions of the buses during the traffic account for 99% of the total CO2 emissions in the stage of the bus facility construction and operations and bus vehicle operations (Zhang et al. 2014b). Currently, very few studies have been conducted with regards to the CO2 emissions at the vehicle or metro train manufacturing stage. CO2 emissions during the traffic and metro operating stage account for most of the emissions in the transportation. Hence, this study mainly considers the emissions during the traffic and metro operation stages. The common method for calculating transportation emissions was recommended in 1996 (IPCC 1997); the transportation CO2 emissions are equal to the amount of the energy consumed or the distance travelled for a given mobile source activity multiplied by the emission factor for a given fuel type, vehicle type, and the emission control. Since vehicle fuel consumption depends on transport level, operating characteristics (vehicle occupancy, travel speed, and engine size), emission control, maintenance procedures, and vehicle age (Redsell et al. 1988; Gover et al. 1994; Potter 1997; Anable et al. 1997), researchers haveconducted tests to investigate the range of fuel consumption and emissions for real-world operations (Liu and Hou 2009; Huo et al. 2011; Zhang et al. 2014a).

2.2 Socio-economic characteristics and transportation CO2 emissions

A number of studies have been conducted to examine the relationship between socioeconomiccharacteristics and transportation CO2 emissions in different cities and countries. It was found that people with higher income produced more transportation CO2 emissions (Carlsson-Kanyama and Lindén 1999; Brand and Boardman 2008;Weber and Matthews 2008; Susilo and Stead 2009; Brand and Preston 2010; Ko et al. 2011; Brand et al. 2013; Büchs and Schnepf 2013), people with full-time jobs produced more transportation CO2 emissions than those with part-time jobs (Carlsson-Kanyama and Lindén 1999; Susilo and Stead 2009; Ko
et al. 2011; Brand et al. 2013) and the unemployed (Brand and Boardman 2008), households with at least one car produced more transportation CO2 emissions than those without any cars (Ko et al. 2011; Brand et al. 2013), households with two or more cars produced more than twice transportation CO2 emissions of the households with only one car (Brand and Boardman 2008; Brand and Preston 2010), people with age of 36–65 produced more transportation CO2 emissions than those in other ages (Brand and Boardman 2008; Brand and Preston 2010; Brand et al. 2013), and people with higher education levels produced more transportation CO2 emissions than those with lower education levels (Büchs and Schnepf 2013).

2.3 Household locations and transportation CO2 emissions

The relationship between transportation CO2 emissions and household location has also been studied in the recent years. It was found that people located in the peri-urban areas produced the largest transportation CO2 emissions with 1000 kg/year/individual for daily travel and 700–800 kg/year/individual for long-distance travel (Nicolas and David 2009). In Seoul metropolitan area, people located at the edge of the metropolis produce more transportation CO2 emissions than those located in other parts of Seoul (Ko et al. 2011). It was also found that the transportation CO2 emissions produced by the neighborhoods located in the central district were less than those in the suburbs (Xiao et al. 2011; Liu et al. 2012), and whether the district was classed as a suburb or not was a strong indicator of the transportation CO2 emissions (Xiao et al. 2011). Büchs and Schnepf (2013) found that rural places were strongly associated with higher transportation CO2 emissions than urban households in
UK. The straight line distance from the zone to the central business district (CBD) has been found to be the most important factor in VKT per worker in the Greater Toronto Area. It can be interpreted as a measure of the effect of sprawl or decentralization. The VKT per worker increases by 0.25 km on average as a worker lives 1 km farther away from the CBD (Miller and Ibrahim 1998).

2.4 Urban form and transportation CO2 emissions

Low-density suburban development is more energy and GHG intensive than high-density urban development on a per capita basis (Norman et al. 2006). Increasing residential density can lead to a significant reduction in transportation emissions (Hong and Shen 2013). VKT declines as the compactness of subdivisions increases, and vehicles tend to be driven at lower average speed in more compact subdivision. The lower speed is not significant enough to offset the reduced VKT; therefore, total gasoline consumption and the associated CO2 emissions still tend to be lower in more compact developments (Emrath and Liu 2008). There exists a significant inverse relationship between the land use density, street connectivity (block density), and vehicle emissions while controlling for the effects of household size, vehicle ownership, and income (Frank et al. 2000). The type of the neighborhood is correlatedwith transportation CO2 emissions (Guo et al. 2014). For four types of neighborhoods (traditional, grid, enclave, and superblock) in Jinan of China, the superblock neighborhoods produce the highest emissions, which are related to the higher household annual income, whereas traditional neighborhoods produce the lowest emissions. It is also found that mixed random effects and instrumental variables was employed to control for spatial autocorrelation and self-selection. The results indicate that the effect of residential density on transportation emissions is influenced by spatial correlation and self-selection. Also, they found that increasing residential density led to a significant reduction in transportation emissions.