1. Introduction
The transport sector accounted for 23% of global CO
2 emissions in 2010, and its share of emissions is expected to increase at a higher rate than that of other sectors towards 2050 [
1]. Road transport is a dominant source of emissions, accounting for 70% of transport sector emissions. Furthermore, 80% of road transport emissions come from passenger transport [
2]. Consequently, substantial reductions in CO
2 emissions from passenger cars are required, and various countermeasures are expected to reduce these emissions. The countermeasures include reducing carbon intensity [
3,
4], improving the energy efficiency of vehicles [
5,
6,
7,
8], electrification of vehicles [
9,
10], and reducing vehicle weights [
11], as well as demand management measures, such as traffic control and modal shift policies [
12,
13,
14,
15] and urban compaction [
16,
17,
18,
19,
20,
21].
The technological measures are expected to be effective everywhere if the technologies are diffused effectively in the market, whereas the impact of demand management measures varies depending on the regional conditions. The International Energy Agency (IEA) estimated that technological measures would reduce greenhouse gas (GHG) emissions in the transport sector by 54% from 2015 to 2060 under the “2 degree scenario” and by 83% for the same period in the “beyond 2 degree scenario” [
22], in which a very ambitious technology deployment rate is assumed. In regard to demand management, Cuenot et al. [
23] estimated the effect of a modal shift that reduced automobile and air traffic by 25% globally by 2050. Their analysis was based on national level statistics and did not consider subnational conditions. Using Japanese statistics for 1980–2005, Matsuhashi and Ariga [
24] clarified that an area with a higher population density emits less CO
2 from automobiles. On the basis of this statistical relationship, they estimated CO
2 emissions from automobiles for whole municipalities in Japan in 2030. They estimated that urban compaction would reduce CO
2 emissions by 5% compared with a sprawled urban pattern. However, they did not consider the improvement of automobile technologies.
Studies analyzing mitigation impacts on a global scale usually simplify the transport demand conditions, and most do not consider spatial policies such as urban compaction. Meanwhile, the studies analyzing the impact of spatial policies usually do not consider vehicle technologies. To estimate the future potential to reduce CO2 emissions, we need to incorporate both technology and demand management. In this study, we estimate the CO2 emissions from passenger cars in 1727 municipalities in Japan by 2050 and analyze the factors affecting the emissions, considering both automobile technologies and travel demand. On the basis of this analysis, we clarify the impact of the scenarios on automobile CO2 emissions quantitatively and discuss the possible measures for substantial emissions reductions. The impact of the factors influencing emissions under depopulation, demographic change, and spatial shift of activities is not clarified sufficiently in the existing literature. Our research advances the CO2 mitigation studies in the transport sector by considering automobile technologies, demography, and spatial feature of regions simultaneously.
2. Analytical Framework and Method
Our analytical framework to estimate CO
2 emissions is based on breaking down emissions into activity (A), modal structure (S), modal energy intensity (I), and carbon content of fuels (F), known as the ASIF approach [
25,
26]. This approach analyzes the emissions reduction potential of each factor and the interaction among the factors. In this study, we consider the energy sources as gasoline, diesel, electricity, and hydrogen. The well-to-wheel carbon contents of these sources are given by scenarios based on the literature. The energy efficiencies of vehicles by energy sources are also given by the scenarios. In the real world, emissions are affected by road speed. We quantify the regional road speed distribution based on traffic volumes and road supply. Traffic volumes are quantified based on demography and urban policies. To estimate the travel demand, we formulate the go out rate and the net trip rate by gender and age classes. Furthermore, we estimate the average occupancy rate of a vehicle and the average car trip length. The explanatory variables of these models are the urban conditions, including population and transportation facilities. We assume that there are three transport modes: cars, public transport, and walking/cycling. The modal shares of each are formulated using the explanatory variables for the urban conditions. The analytical framework is depicted in
Figure 1.
We denote CO
2 emissions from passenger cars in municipality
i in year
t as
, which is formulated as follows:
where
is the attribute vector of the municipality,
is the average real-world emissions factor of in-use vehicles,
is the trip length of passenger cars,
is the occupancy ratio, and
is the number of trips of a user of attribute
k.
is expressed in the form:
where
is the total population,
is the population share of people with attribute
k,
is the go-out rate,
is net trips per person, and
is the modal share of passenger cars.
These functions and emission factors are determined in
Section 4 using the data explained in the next section. As formulated in Equation (2), we use individual projection for the car travel demand. Some studies projected car travel demand using household projection because the purchase and the usage of cars are usually decisions by households, not individuals. The probable effect of using the individual projection instead of household projection is addressed in
Section 6.
5. Results
Using the above model, we estimate the total CO
2 emissions in 2010 to be 104 MTCO
2. Comparing this with the observed volume of 106 MTCO
2, our result underestimates emissions by around 1.6%. Although there are no official statistics on CO
2 emissions from passenger cars by municipality, the Ministry of Environment (MoE) of Japan provides an estimate for 2010 [
37]. For the 1555 municipalities that appear both in our data and in the MoE estimate, we find that the correlation between our and the MoE’s estimates is 0.976. Thus, despite the approaches differing, the estimates are highly correlated. Given the high accuracy of the total CO
2 emissions estimation, we can say that our approach provides an adequate estimation of CO
2 emissions from passenger cars.
Figure 9 shows the estimated national total CO
2 emission from 2010 to 2050 and the contribution of various factors to emission changes. In the figure, the red line shows the trajectory of estimated total CO
2 emissions, which begin at 104 MTCO
2 in 2010 and are estimated to fall to 32.7 MTCO
2 in 2050, a 64% reduction. The technology factor contributes most to this decline, accounting for 43.6% of the reduction in emissions in 2050. The second largest contributor is the total population, which accounts for 19.7% of the emissions reduction, followed by demography, which contributes 3.4%. The urban structure factor, shown by the green line, is estimated to result in a 4.3% increase in total emissions. This means that, under the baseline scenario, suburbanization induces longer trips and greater car usage, both of which increase emissions. The dark gray area indicates the speed factor, which, by reducing congestion, decreases emissions by 1.9%.
Figure 10 shows the estimated CO
2 emissions and the contribution of the factors under the compact scenario. The total CO
2 emissions are estimated at 30.6 MTCO
2 in 2050, which is a 70% reduction from the 2010 level. The reduction rate under the compact scenario is 6% higher than that of the baseline scenario. In 2050, the technology factor contributes the largest share of this reduction at 40.6%, followed by the total population factor (17.8%), the urban structure (6.6%), the demography (3.1%), and the road speed factor (2.6%). Under the baseline scenario, the urban structure results in a 4.3% increase in emissions and, thus, the urban compaction scenario results in a substantial reduction in the emissions caused by the urban structure factor. Furthermore, the road speed factor reduces emissions more under the compact scenario than under the baseline scenario. In this study, urban compaction reduces the share of car usage and the vehicle travel length, while the road provision is assumed to be fixed. Therefore, there is less average lane traffic and a higher road speed under the compact scenario. It should be noted that the result would differ if urban compaction resulted in road traffic concentrating at certain links and inducing congestion.
Figure 11 shows the change in the spatial distribution of CO
2 emissions under the baseline scenario between 2010 and 2050. The darker color indicates that the reduction in emissions is higher in the figure. The large cities and the small cities are shown to achieve large reductions in total CO
2 emissions, whereas municipalities in mountain areas have small reductions.
The municipalities in mountain areas have smaller populations and emit fewer emissions compared with urban municipalities in 2010, and the total volumes of emissions reduction is smaller in the future. The per capita reductions in volume of CO
2 emissions are smaller in large cities and larger in small cities. In small cities, the share of car usage is high, making the impact of emission reduction technologies high. Conversely, in large cities where the modal share of public transport is relatively high, the impact of automobile emission reduction technologies on per capita CO
2 emissions is relatively small. The volume of CO
2 emission reductions per vehicle-km is high in large cities. The road speed in large cities is relatively slow as a result of congestion and, therefore, technological improvements have a stronger effect in terms of reducing the absolute volume of emissions. At the same time, road speed will be faster and the emissions factor will be improved when traffic is reduced. Combined with these factors, the CO
2 emission per vehicle-km is reduced more in large cities than in small cities. In summary, the impact of CO
2 emission reduction differs by region, and different indices of emission changes correspond to different regional features. Details of these regional differences are provided in the
Supplementary Materials.
Figure 12 shows the differences in the three indices between baseline and compact scenarios in 2050. The compact scenario derives lower CO
2 emissions than does the baseline scenario, and its impact is higher in large cities and lower in small cities. In other words, urban compaction is effective in reducing CO
2 emissions in urban areas but less effective in rural areas. The effect of urban compaction in reducing CO
2 emissions per capita is relatively weak at the center of large cities, but it is strong in local cities and in the suburbs of large cities. Population density in the center of large cities is already high, and the effect of urban compaction may be limited there. Local cities or suburbs of large cities have more capacity to reduce emissions per capita by urban compaction. The difference of emissions per vehicle-km indicates that urban compaction boosts reductions in emissions. In this study, urban compaction reduces the traffic volume by more than the baseline scenario and, as a result, traffic congestion is alleviated further. Therefore, the emissions per vehicle-km are reduced more in a compact scenario.
6. Discussion and Conclusions
In this study, we estimated that CO
2 emissions from passenger cars in 2050 are reduced by 64% and 70% under baseline and compact scenarios, respectively, compared with 2010 emissions. In 2016, the Japanese government set a long-term target of an 80% reduction in GHG emissions by 2050 in the Plan for Global Warming Countermeasures [
34]. In this plan, the sectoral reduction targets are not determined. However, if we apply this target to passenger cars, an additional 10–14% reduction in emissions is required. Depopulation and aging in Japan reduce transport sector activities and consequently reduce CO
2 emissions, with the impact of these changes estimated to be about half of the reduction achieved via technological progress. To achieve the 2050 target, further emissions reduction measures are required.
We demonstrated that the expected population reduction will reduce the population density, which in turn will induce higher car dependency and longer car trip lengths. As a result, the urban structure factor increases the CO2 emissions in the baseline scenario. Therefore, we need a measure to increase population density under the depopulating situation. As shown in our analysis, even an extremely drastic urban compaction scenario would reduce emissions by only 6%. This result clarifies that control of the urban space solely has a quite limited effect on CO2 emissions reductions.
Reducing traffic volumes alleviates road congestion in the urban areas, which contributes to reducing CO2 emissions, but the estimated impact is only 2%. In this study, urban compaction is estimated to reduce vehicle-kms and CO2 emissions. However, if compaction and the resulting concentration of traffic aggravate congestion, then part of the emission reduction expected from urban compaction would not be achieved. Thus, urban compaction measures need to be combined with other countermeasures, including traffic control, land use management, and public transport provision, to alleviate road traffic concentration.
Our spatial analysis indicated that the per capita CO2 emission reduction rate is higher for the local cities where the modal share of passenger cars is high. In these cities, improvements in automobile technologies will have a higher impact. The emission reduction effect of urban compaction is high in local cities and in the suburban municipalities of large cities but smaller at the center of large cities and very minor in municipalities in mountainous regions. These results indicate that the effects of emission reduction measures differ by regions. Therefore, the countermeasures of transport demand management should reflect regional features rather than applying uniform national measures. For instance, the combination of road speed improvements and road pricing to curb car traffic demand can be effective in reducing CO2 emission in large cities. However, they may not be effective in small towns and low-density cities, where people are more dependent on cars and have fewer alternatives and therefore cannot change their travel behavior in response to these measures. In this case, road pricing simply increases user costs but may not reduce CO2 emissions. Urban compaction may have a small effect in the center of large cities, which are already high density, and in mountainous areas, urban compaction has only a marginal effect owing to the sparse development patterns and low population density. Conversely, urban compaction in local cities or suburbs of large cities that have urban sprawl patterns will be effective in increasing population density and in reducing automobile dependency and car trip length. Demand management measures, including urban compaction policies, impose burdens on people to induce them to alter their behavior. Therefore, it is important to determine an effective framework for emission reduction measures that consider differences in impacts by regions.
In this study, we did not analyze the effect of a carbon intensity reduction by energy source and other demand side management measures, such as teleworking. The emissions factor for electricity is assumed to be fixed after 2030. If the share of renewable energy is increased substantially, then the emissions from using electricity will be reduced. As assumed in this study, if electric vehicles are widely disseminated, then a reduction in the emissions factor for electricity will have a considerable effect on CO2 emissions from automobiles. Furthermore, if synthetic fuels derived from bioenergy or by a Fischer–Tropsch process can be delivered at a reasonable cost, then the internal combustion engine cars achieve zero well-to-wheel emissions. However, although these technologies may have a substantial impact, their progress is highly uncertain. Transport demand management measures such as teleworking and modal shifts, which may have a large impact on CO2 emissions reductions, are not fully considered in this study because the feasibility of these measures is also very uncertain. Teleworking is becoming popular among private companies owing to the current infectious disease epidemic, but it is unclear whether this working style will continue. We need to carefully understand the impact of people’s behaviors, including their commuting patterns and location choices, that may affect travel behavior.
It should also be noted that our projections of car usage are based on the individual level, not the household level. Some studies use household-level projection because the purchase and the usage of cars are usually the decision by the household [
38,
39]. Household attributes, such as size, number of children, and income, affect the decision in purchasing cars and usage of cars, especially for accompanied travel. Households are composed of individuals, thus households have a wider variety of types than individuals. The projection of the future composition of household types for all municipalities is another big challenge. In Japan, depopulation and aging are expected to reduce the average household size, but it is unclear how the future shift of household composition affects car usage besides the aging of individual people. It should be addressed in future studies. Additionally, our demography scenario is based on IPSS and MLIT projections, which assumes a relatively conservative number of foreign immigrants (about 70 thousand net immigration annually in 2035). It is also possible to increase the foreign immigrants by the change of the Japanese immigration policy in the future, however, it is unpredictable. Our approach can be applied to the cases of the other demographic scenarios, and the increase of immigration will of course multiply the CO2 emission from passenger cars.
In summary, even with the assumption of substantial diffusion of CO2 emissions reduction technologies, we find that the emissions reductions from passenger cars will not reach 80% in 2050. We need an additional 10–14% reduction to achieve the 80% target. In addition to the countermeasures considered in this study, we need to apply other measures to achieve the reduction target. There is no panacea of mitigation measures for climate change and all possible measures must be considered simultaneously. This maxim is also applicable in the CO2 emissions reduction in the passenger car sector.