G. Chen, J. Kauppila 1

Global Urban Passenger Travel Demand and CO2 Emission to 2050:

A New Model

Guineng Chen, PhD

(Corresponding author)

International Transport Forum at the OECD

2 Rue André Pascal, 75775 Paris

Email: Guineng.CHEN@itf-oecd.org

Jari Kauppila, PhD

International Transport Forum at the OECD

2 Rue André Pascal, 75775 Paris

Email: Jari.KAUPPILA@itf-oecd.org

The total number of words is 7,428 (6178 words + 2 tables + 3 figures)

Revised version

Submitted to the 96th

TRB Annual Meeting, January 8-12, 2017

G. Chen, J. Kauppila 2

ABSTRACT 1

This paper presents long-term scenarios on the development of urban passenger mobility and 2

related CO2 emissions up to 2050 in global cities, which have population above 300 thousand 3

based on the International Transport Forum’s (ITF) new global urban passenger transport 4

model. Results from the policy scenarios analysis show that, in the baseline scenario, total 5

motorised mobility and related CO2 emissions in cities will grow by 94% and 27% in 2050 6

compared with 2015, respectively. The share of private cars will continue to increase in 7

developing regions while slightly decreasing in developed economies. Policy measures exist 8

to fulfil mobility demand while reducing carbon-intensity of the travel. Technology 9

contributes the most to the CO2 mitigation in the most transit-oriented scenario. Behavioural 10

policies, such as fuel tax, lower transit fares or controlled urban sprawl can bring the 11

additional mitigation effort required to make cities sustainable and are essential to combat 12

congestion and health issues. 13

14

G. Chen, J. Kauppila 3

INTRODUCTION 1

By 2050, there will be 2.4 billion additional urban dwellers compared to 2015 (1). This strong 2

urbanisation process creates substantial new demand for mobility in cities. The combined 3

effects of rapid urbanisation, income growth and private vehicle ownership of the developing 4

countries will result in significant growth in emissions from transport in the cities. For most 5

transport modes, CO2 emission is the major component of the total emission, non-CO2 6

emissions are usually less than 5% of total vehicle emissions (2). Understanding the drivers 7

of future transport demand and mitigating related CO2 emissions in cities is essential to tackle 8

the challenges posed by climate change. Reliable projections of urban mobility under 9

different urban configurations are crucial to support the development of effective urban 10

policies to reduce CO2 emissions. 11

Most existing urban passenger transport models attempt to explain the travel behaviour 12

for the population of a specific region or urban area, relying on detailed and interconnected 13

modules requiring both large household travel surveys and ad hoc consumer preference 14

surveys, which are not available at global scale (e.g. (3–7)). These detailed models have the 15

advantage of better capturing behavioural aspects but their findings are case specific with low 16

transferability to other areas or regions. 17

For global scale analysis, one commonly adopted approach is to estimate urban 18

passenger travel demand, energy consumption and emissions using vehicle ownership 19

numbers multiplied by average kilometres travelled per vehicle and fuel economy/emission 20

rates. This approach has been widely implemented in modelling different transport modes 21

separately, especially in the private car sector (8–10). Comprehensive multi-modal analysis 22

on a global scale is scarce. The most well-known example is the IEA’s Mobility Model, 23

which estimates and projects the travel indicators, energy consumption, pollutant emissions 24

and greenhouse gas emissions for all modes and regions of the world up to 2050 (11). 25

Similar modelling approach has been implemented by other researchers for national and 26

global regionalised projections (10, 12, 13). However, there are a number of caveats related 27

to the validity of such long-term projections for transport demand. They do not take travel 28

behaviour into account explicitly, as the projections are entirely based on vehicle stocks. 29

These studies extrapolate vehicle fleets based on growth rates independently for each 30

transport mode, and are thus unable to account for the competition between transportation 31

modes and potential impact of mode shift (14, 15). 32

One widely adopted approach to incorporate the modal split in the global and national 33

travel demand analysis is the “travel time budget” and “time-money budget” (9, 15–19). This 34

refers to the idea that individual’s average daily travel time and the budget share of travel 35

expenditure tends to be constant. As the passenger kilometres travelled increases due to the 36

income growth the travellers have to shift towards more flexible and faster transport to 37

maintain the travel time budget constant. Although at the aggregate level, travel expenditures 38

appear to have some stability, they are widely different at different times and locations (20). 39

Furthermore, besides population, income and travel costs, a global transport model should 40

also take into account the urbanisation shifts, infrastructure provision and other variables. 41

Lack of appropriate methods partially explains the scarcity of long-term projections for 42

urban mobility and CO2 emission at a global scale. Another reason is the lack of historical 43

data needed for model calibration. Global data sets are available for the energy sector and 44

have been used to calibrate regional and global energy models (21, 22), but not yet available 45

for the projections of global passenger transport. 46

The new International Transport Forum’s (ITF) global urban passenger transport model 47

is a strategic tool to test the impacts of transport, environment and technology substitution 48

G. Chen, J. Kauppila 4

policies on travel demand and CO2 emissions in world cities up to 2050. The model can 1

assess the joint impacts of several urban transport policies. 2

The model differs from existing models in two main areas. First, it has a global 3

perspective, covering all cities above 300,000 inhabitants (1). In 2015, the total population of 4

these cities amounts to 2.2 billion, making up 57% of the world urban population, covers 5

more than 50% of the world GDP. And their CO2 emission from passenger transportation 6

accounts for 37% of the global passenger transport CO2 emissions and 20% of the entire 7

transport sector. We combine data from various sources to form one of the most extensive 8

databases on mobility in world cities, including socio-economic attributes, aggregated modal 9

split, road and public transport infrastructures, fuel price, parking and transit ticket prices, 10

among others. The model separates five transport modes: private car, public transport, 11

motorcycle, walking and cycling. Secondly, the model represents aggregated travel behaviour 12

explicitly for a segment of travellers as a function of the characteristics of the alternative 13

modes and socio-demographic attributes of the group (23). The mode share module is 14

therefore able to describe the interaction between and evolution of different modes, currently 15

not taken into account in the existing models. 16

DATA AND METHODOLOGY 17

The new ITF model covers all the urban agglomerations (1692 cities) with population above 18

300 thousand, following the definition of UN World Urbanization Prospects (1). The urban 19

boundary for each selected city is provided by the Global Built-up Reference Layer 20

(BUREF2010) (24) and complimented by the space-based land remote sensing data 21

LANDSAT of year 2010. The Gross Domestic Product (GDP) at city level, in the base year, 22

is estimated by redistributing the national GDP volume provided by the OECD, into the 23

urban areas according to the GDP distribution map obtained from LANDSAT 2010, which 24

provides the GDP density for each cell grid with 1 square km resolution. 25

The general model structure comprises six sub-models (see Figure 1). The transport 26

system is composed by three highly interrelated sub-models: Travel Demand, Transport 27

Supply and Vehicle Fleet. The interaction between land-use and transport system is 28

represented as Land-use sub-model influencing the mode choice and car ownership and, in 29

turn, affected by the exogenous population and GDP growth. The exogenous drivers to the 30

transport system include population, economy and vehicle technology. The outputs of the 31

Vehicle Fleet sub-model feed into the Environment sub-model to compute the CO2 emissions. 32

The sub-models are structured as the following and the detailed formulation and model 33

calibration results can be found in the ITF Transport OUTLOOK 2017 (25): 34

Urban population from 2030 to 2050, replicating the projection approach of the UN 35

World Urbanization Prospects (1); 36

GDP growth rates for the cities forecasted using a sigmoid curve (logistic function). 37

The relation between urban concentration of population and GDP is modelled with an 38

S-shaped logistic curve; 39

Road supply and public transport supply, estimated by regression models and 40

explained by the population, GDP per capita and area size; 41

Modal split of each city estimated by a discrete choice model. The model estimates 42

the impacts of socio-economic development, car ownership, urban structure, road and 43

public transport provision and pricing indicators on the aggregated modal splits. 44

Average trip rate and average trip distances estimated by regression models, explained 45

by GDP per capita and area size. 46

Passenger car ownership forecast estimated by a sigmoid curve (logistic function) 47

based on the GDP per capita and urbanisation rate. 48

G. Chen, J. Kauppila 5

CO2 intensities, technological pathways by mode and fuel type are taken from IEA’s 1

Mobility Model for converting vehicle activities into CO2 emission. 2

3

4 5

Figure 1 Schematic description of the model 6

7

While the model is built at the city level, the final analysis is carried out for the 8

following regions. The regional aggregates are the following: 9

Africa: Sub-Saharan Africa and North Africa 10

Asia: South and East non-OECD Asia excluding China and India 11

China + India: China and India 12

EEA + Turkey: EU28 + Switzerland, Norway and Turkey, non-EU Nordic (Iceland) 13

Latin America: South America and Mexico 14

Middle East: Middle East including Israel 15

North America: United States and Canada 16

OECD Pacific: Australia, Japan, New Zealand and South Korea 17

Transition: Former Soviet Union countries and Non-EU South-Eastern Europe 18

POLICY SCENARIOS FOR THE WORLD CITIES 19

Demand-management policies, which encourage modal shift away from private vehicles and 20

aim at reducing kilometres travelled, can be an effective means to reduce emissions (26, 27). 21

We developed three scenarios (28, 25) to assess the impacts of various combinations of urban 22

transport policies aimed at reducing CO2 emissions: Baseline Scenario, Robust Governance 23

Scenario (ROG), and Integrated Land Use and Transport Planning Scenario (LUTP). The 24

urban policies are directly linked with modal split, trip rates and trip distances. 25

26

Baseline 27

In the Baseline scenario, no additional measure to influence travel demand or CO2 emissions 28

are implemented during the 2015-2050 period. This scenario constitutes a business-as-usual 29

reference for travel demand and CO2 emissions in the cities, against which we measure the 30

efficiency of additional policies and compare alternative scenarios. While accounting for the 31

G. Chen, J. Kauppila 6

evolution of exogenous drivers (GDP, population), the Baseline scenario assumes that the 1

future trends of car ownership, road and public transport supply, pricing structures and the 2

urban area growth will follow the past trajectories, as calibrated in each of the sub-models. 3

Advanced vehicle technology and alternative fuels penetrate the market at a relatively 4

low rate, as in the latest 4°C Scenario (4DS) of the Mobility Model developed by the 5

International Energy Agency (IEA). This corresponds to a global average for on-road fuel 6

efficiency of passenger cars of 6.4 litres gasoline equivalent per 100 kilometres in 2050 7

compared to 10.3 litres gasoline equivalent per 100 kilometres in 2015. 8

9

ROG scenario 10

The Robust Governance (ROG) scenario assumes that local governments play an active role 11

in transport policy and adopt pricing and regulatory policies to slow down the ownership and 12

use of personal vehicles from 2020 onwards. Existing literature provides evidence on the 13

effectiveness of rigorous pricing strategies to shift from personal cars towards modes with 14

lower carbon intensities (29–32). In this scenario, pricing policies will target fuel prices, cost 15

of vehicle ownership and use, and lower transit fares will be implemented in all the cities. 16

The scenario includes the following assumptions: 17

In 2030, fuel prices in each country correspond to a fictive oil price of 120 USD 18

(real USD/bbl, 2005). Such prices can result from higher taxation, high oil prices, 19

or a combination of both. The growth rates of oil price between 2030 and 2050 20

are assumed to be the same as in the Baseline. 21

Parking prices are 50% higher than in the Baseline. 22

Transit ticket sub-model estimates the elasticity of single transit ticket price with 23

respect to GDP per capita for each country group. The lowest regional price 24

elasticity is used to determine the future transit ticket price of all the world cities. 25

Governments regulate the car registration cost, purchase cost, operational cost, 26

etc. at national level, affecting overall car ownership levels, but there are no car 27

restriction policies such as those implemented in some Chinese cities. The 28

elasticity of car ownership with respect to GDP per capita is lower than the 29

Baseline. 30

Road supply projection follows a need-based expansion strategy, meaning that 31

more new roads will be built to satisfy only the urban area expansion and 32

population growth, but not the growth in GDP per capita and car ownership, 33

which is the case in the Baseline. 34

Vehicle load factors, fuel efficiency standards and the market penetration of 35

advanced vehicles and alternative fuels reflect the assumptions made in the latest 36

2°C Scenario (2DS) of the Mobility Model developed by the International Energy 37

Agency (IEA). 38

39

LUTP scenario 40

On top of the policies introduced in ROG scenario, the Integrated Land Use and Transport 41

Planning (LUTP) scenario assumes stronger prioritisation for sustainable urban transport 42

development and a joint land-use policy. It is well acknowledged that integrating decisions 43

across land use, transport and environment sectors is crucial for sustainable development 44

(33). The key difference between the LUTP and ROG scenarios is a higher public transport 45

infrastructure supply, more prevalent implementations of mass transit systems in the cities 46

and restricted urban sprawl in the former. Higher population density and transit network 47

density can effectively stimulate an increase transit share and a reduction in trip distances 48

(34), which then contribute to overall larger reductions in CO2 emissions. 49

The followings are the additional policies for the LUTP scenario: 50

G. Chen, J. Kauppila 7

In all regions, the expansion rate of public transport supply with population 1

follows the path found in Europe, which is the highest of the world; 2

The threshold in population density and GDP per capita needed for the 3

development of a mass transit system is 20% lower than in the Baseline scenario 4

for every region. 5

Urban area size remains constant from 2020 onwards. Translating into 6

quantitative terms, the average urban density in 2050 ranges from 20% 7

(Transition) to 83% (Africa) higher than its level in 2015 depending on the 8

regions, compared with relatively constant or decreasing urban density in the 9

Baseline. 10

RESULTS 11

Mode shares 12

Modal split by country group under different scenarios are presented in Figure 2. In the 13

Baseline, the car share continues to increase in all developing regions, but slightly decreases 14

in developed economies. The share of public transport, motorcycle and non-motorized modes 15

in developing countries is decreasing until 2050, meaning that more residents of cities in 16

developing regions shift to private car. At the end of the period, private car is the dominant 17

form of urban transport in all the regions, except Africa, where walking remains the highest. 18

The highest growth in car share occurs in Asia, especially in China and India, where the 19

average share of car reaches 40%, 1.5 times the level in 2015. Following closely are the other 20

developing Asian countries, where the average car share grows from 30% in 2015 to 41% by 21

2050. In the developed regions, we observe a reduction in car share. For instance, from 2015 22

to 2050, the share of private cars decreases by 12 percentage points in Europe and 5 23

percentage points in North America. In these regions, where behavioural aspects win over 24

purely exogenous growth factors, former car users shift to public transport or non-motorised 25

modes while the growth for transport demand remains quite low. This is in line with recent 26

research showing that the per capita daily travel demand has uncoupled from income in some 27

high income countries, for instance Great Britain (35, 36). In many large European cities with 28

extensive public transport network, the pressure put on car usage has brought down the car 29

share (37) 30

In the ROG scenario, car use is lower in all regions because of the lower expansion rate 31

of the road network and more stringent pricing policies increasing the fixed or variables costs 32

of car ownership and car use. Reductions in car shares already happen in 2030, except in 33

China and India, due to the rapid income growth. By 2050, public transport becomes the 34

dominant urban transport mode in every region except North America, where the car share is 35

still around 61%. 36

Motorcycle share has a downward trend in the ROG scenario in all developing regions, 37

shifting to public transport. This is mainly the result of the growth in GDP per capita, as high 38

income travellers prefer safer and more convenient modes (38). Public transport becomes 39

attractive and affordable under the ROG scenario and replaces motorcycle trips, while car 40

and motorcycle use is made more expensive because of higher fuel and parking prices. 41

Another element explaining the growth in transit use is the shift of non-motorised trips to 42

public transport, especially in developing regions, with two main underlying reasons. First, 43

there is strong demand for faster mobility due to income growth. Second, the expansion of 44

cities increases the length of trips for the urban residents, making walking and cycling less 45

viable and encouraging a shift to motorised modes. Walking and cycling becomes an 46

effective complement to public transport, meeting the requirements of short distance travel. 47

G. Chen, J. Kauppila 8

In the LUTP scenario, the transit oriented policies reinforce the use of public transport 1

and further reduce car use, due to controlled urban sprawl, higher population density, higher 2

transit network coverage and mass transit availability. Developing regions are more sensitive 3

to the LUTP policies than developed regions because cities in these regions are less mature. 4

By 2050, the car share of developing Asian countries is 3.1 percentage points lower than in 5

the ROG scenario, but only 1.2 percentage points in North America and 1.3 percentage points 6

in EEA + Turkey region. Although additional LUTP policies seems to have relatively smaller 7

magnitudes of impact on containing car use, they have higher impacts on encouraging the 8

transit use for developing regions, leading to more than 5 percentage points increase in transit 9

use by 2050. 10

11 Figure 2 Modal split by country group under different scenarios 12

13

Mobility levels 14

Projections of the total mobility level (measured in passenger-kilometres) under different 15

scenarios are presented in Table 1. Under the Baseline scenario, total mobility in cities will 16

G. Chen, J. Kauppila 9

rise by 42% in 2030 and 96% in 2050 compared with 2015, reaching 25,680 billion and 1

35,280 billion passenger kilometres, respectively. 2

Discouraging car use in ROG scenario do not impact the total mobility levels negatively, 3

with overall passenger-kilometres numbers increasing in all regions except in North America 4

(less than 2%). In the ROG scenarios, targeted pricing policies impact differently the 5

mobility levels in developed and developing regions. For example, in 2050, Africa’s mobility 6

level will be 38% higher in the ROG scenario than in the baseline, followed by China + India 7

26%. However, the mobility improvement is only 10% in OECD Pacific, and even negative 8

for North America. 9

For developing countries, where the motorised mobility demand is growing rapidly due 10

to the income growth, favouring transit use by reducing the transit ticket price improves 11

significantly the motorised mobility as transit is more affordable for everyone than private car. 12

This is why total mobility levels in these regions are higher in the ROG scenario than in the 13

baseline, where the reliance on cars limits the uptake of motorised mobility. 14

At contrary, in already highly motorised developed regions, public transport needs to 15

compensate for car use restrictions. If restrictions are put in place without significant 16

improvements in public transport, overall mobility levels will decrease. For instance, 17

applying the same restrictions to car use of North America, which has a low base level in 18

public transport supply, reducing the transit price cannot totally offset the reduced mobility 19

caused by increasing car use costs, thus leading to 2% decrease in the total mobility level. 20

The main objective of the LUTP scenario is to meet the transport demand through less 21

carbon-intensive mobility options and shorter trip distances. Many studies have found that the 22

application of transit-oriented development have positive impacts on the sustainability of the 23

cities, such as lowering trip rates by private cars (39), raising the transit mode share and 24

reducing car mode share, and decreasing the average trip distance (40, 41). It is reasonable 25

that the overall mobility improvement under LUTP scenario is smaller than in the ROG 26

scenario. The main reason is the effective control on the urban area size, which leads to lower 27

urban sprawl, higher population density, and more transit-oriented development patterns, 28

contributing to an overall trip distance reduction. In the LUTP scenario, average trip distance 29

is 12% lower in 2030 and 23% lower in 2050 compared with other two alternative scenarios. 30

Although the mobility levels for some regions appear to be slightly lower than in the baseline, 31

the mobility demand is fulfilled with higher transit share and shorter trip distances. 32

33

Table 1 Projection of mobility level by region and scenario [billion PKM] 34

2015 2030 2050

Region Base year Baseline ROG vs

Baseline

LUTP vs

Baseline Baseline

ROG vs

Baseline

LUTP vs

Baseline

Africa 989.8 2016.5 12.1% 0.0% 3788.1 37.7% 6.5%

Asia 1382.3 2342.0 8.5% 1.4% 3560.9 25.0% 8.7%

China + India 5094.3 8443.2 9.0% 4.5% 11720.6 25.7% 15.5%

EEA + Turkey 1699.5 2047.4 5.4% 3.7% 2484.1 13.2% 8.3%

Latin America 1875.0 2513.8 4.3% -0.1% 3380.2 17.4% 7.8%

Middle East 431.3 712.4 2.4% -4.8% 1106.5 9.6% -7.3%

North America 3083.9 3861.9 -1.9% -3.4% 5050.7 -1.4% -5.4%

OECD Pacific 2982.0 3175.5 0.3% 0.4% 3424.0 10.3% 10.8%

Transition 504.6 567.3 5.2% 4.8% 764.7 16.5% 7.4%

World Total 18042.6 25679.8 5.5% 1.4% 35279.8 19.2% 8.3%

G. Chen, J. Kauppila 10

CO2 emissions 1

CO2 emission in the cities by mode and scenarios are presented in Figure 3. The total CO2 2

emission in large cities in the 2015 is 1649 million tonnes, accounting for 37% of the CO2 3

emissions from global passenger transport sector (11). In the Baseline, the total CO2 emission 4

in large cities is 27% (448 million tonnes) higher in 2050 than 2015. CO2 emission levels in 5

the cities are almost the same between 2015 and 2030, because of the large fuel efficiency 6

gains expected in the decade to come, and the low economic growth for years up to 2020. 7

However, emissions grow again from 2030 onwards because of the assumptions related to 8

vehicle technology and fuel efficiency in the 4DS scenario of the IEA’s Mobility Model. The 9

world average fuel efficiency for on-road passenger light duty vehicles improves by 29% 10

from 2015 to 2030 but only 14% from 2030 to 2050. This pace of technology improvement is 11

not enough to offset the growing mobility demand between 2030 and 2050. 12

Because of the growing concerns around congestion and local pollution in large cities, 13

the urban transport sector has received a lot of attention. However, its role in terms of CO2 14

emissions is not as high as the public focus would suggest. The other negative externalities 15

(congestion, local pollutants, inequity) of urban transport justify the widespread attention. 16

Policy interventions, especially rigorous car pricing policies, lower transit fare and 17

higher vehicle technology improvement introduced in the ROG scenario can intensely 18

mitigate CO2 emissions from the urban passenger transportation. With solely the policy 19

measures from ROG, the CO2 reduction potential is about 403 million tonnes in 2030 and 905 20

million tonnes in 2050. The additional policies introduced in LUTP scenario would further 21

reduce the CO2 emissions by 55 million tonnes in 2030 and 117 million tonnes in 2050. 22

Under the most effective policy scenario LUTP, the CO2 emission level from the passenger 23

transport in cities would be 26% lower in 2030 and 35% lower in 2050 than the level in 2015. 24

Private cars are the main contributor of CO2 emissions in cities, representing around 82% 25

of all emissions in the base year, and around 75% in 2030 and 2050. With the policy 26

measures of ROG and LUTP scenarios, the contribution of cars decreases to 42% and 40% 27

respectively in 2050. Buses and motorcycles emissions represent 11% and 7%, respectively, 28

in the base year. These two contribution rates will reach 15% and 10% in 2030 and remain 29

stable till 2050 in the Baseline scenario. Under the ROG and LUTP scenarios, the emissions 30

share of motorcycle shows a small decrease (2 percentage points), but the contribution of 31

buses increases significantly, reaching almost the same level as cars in the ROG scenario in 32

2050. In LUTP scenario, bus even becomes the main contributor of CO2 emissions in 2050. 33

CO2 emissions from urban rail are null in this model, as we only look at tank-to-wheel CO2 34

emission and urban rail is assumed to be fully electrified. A life-cycle analysis would 35

increase the CO2 emissions from urban rail, especially in India and Africa where the IEA 36

projects that electricity production will remain carbon intensive throughout to 2050. 37

The technology side of the ROG and LUTP scenarios contribute most significantly to the 38

CO2 mitigation potential of these two scenarios. Breaking down by type of policy measure in 39

2030 and 2050, technology improvements alone reduce global CO2 emissions in cities by 14% 40

in 2030 and 22% in 2050, compared to 2015. 41

Behavioural changes in these two scenarios have an impact on emissions (e.g., 13% less 42

CO2 emissions in 2050 under the LUTP scenario compared to the baseline) and they are 43

essential to combat congestion or health issues. However, emphasising behavioural changes 44

to combat climate change forgets the surge in mobility resulting from the economic 45

development of lower income countries. An efficient decarbonisation of the transport sector 46

in cities requires extreme changes in mobility patterns, on a scale out of proportions with the 47

efforts currently deployed around the world. Such changes could take the form of much 48

higher taxation of car mobility in cities or higher penetration of alternative fuels. 49

G. Chen, J. Kauppila 11

1 Figure 3 CO2 emission in the cities by mode and scenarios 2

3

Table 2 presents the regional breakdown of total passenger transport CO2 emissions. In 4

the Baseline scenario, the most dramatic increase in CO2 emission occurs in Africa, whose 5

emission level in 2050 is almost three times higher than the level of 2015. However, the 6

highest growth in absolute value happens in China and India. The combined emissions of 7

these two countries grow by 297 million tonnes. CO2 emissions of all regions significantly 8

decrease in ROG scenario. Regions with the most CO2 mitigation potential are North 9

America, because of the widespread use of private cars, and China and India, because of their 10

high motorisation potentials. 11

The additional urban policies in LUTP scenario have much higher impacts on the 12

developing regions (e.g. 23% more in CO2 reduction for Africa in 2050), while the mitigation 13

potentials on the developed economies are negligible (e.g. 2% further reduction on CO2 for 14

OECD Pacific). For EEA + Turkey and OECD Pacific, the benefits from additional transit 15

supply and further urban area controlling are rather marginal due to the existing high levels of 16

these in most of the European, Japanese and South Korean cities. Other OECD countries in 17

the Pacific, such as Australia and New Zealand, have mobility and urban development 18

patterns very similar to the car-dominant North American cities. It makes them less sensitive 19

to the pricing policies. It is important not to undervalue their potential. Larger CO2 reductions 20

could be achieved from the increased use of public transportation combined with more 21

rigorous measures than in other developed countries in the world. 22

23

24

25

26

27

28

29

30

31

G. Chen, J. Kauppila 12

Table 2 Projection of total CO2 emission by region and scenario [million tonnes] 1

2015 2030 2050

Regions Base

year Baseline ROG LUTP

LUTP

vs ROG Baseline ROG LUTP

LUTP

vs ROG

Africa 41.9 75.3 60.3 52.8 -12.4% 155.7 76.5 59.9 -21.7%

Asia 77.5 127.6 99.7 92.0 -7.7% 217.7 103.8 88.5 -14.7%

China + India 245.9 382.5 302.0 284.8 -5.7% 542.6 333.5 297.3 -10.8%

EEA + Turkey 163.8 134.5 109.3 107.0 -2.1% 132.8 96.1 90.9 -5.3%

Latin America 133.5 161.3 133.7 126.9 -5.1% 215.4 140.9 126.2 -10.5%

Middle East 45.2 70.5 59.2 54.5 -7.9% 118.7 72.2 60.2 -16.7%

North America 601.7 490.4 359.4 352.6 -1.9% 491.8 256.5 244.2 -4.8%

OECD Pacific 304.8 204.4 130.7 129.9 -0.6% 172.6 90.5 88.8 -1.9%

Transition 34.9 35.3 24.1 23.5 -2.7% 49.5 22.5 19.7 -12.2%

World Total 1649.2 1681.8 1278.5 1224.0 -4.3% 2096.8 1192.3 1075.6 -9.8%

2

CONCLUSION AND POLICY IMPLICATIONS 3

The scenario analysis in this study offers aggregate predictions on modal split, travel demand 4

and CO2 emissions for world cities on the magnitudes of the impacts that could be derived 5

from changes in socio-economic, transportation and urban characteristics. It provides a useful 6

tool for policy makers to explore the relative impacts of broad policies, guiding the design of 7

specific policies for world cities. 8

Without additional policy intervention, cities in developing economies will witness the 9

trend of shifting from slow and less convenient modes to private car as per capita income and 10

demand for mobility continue to rise. On the contrary, the developed economies have a 11

decreasing trend on the car-dependent mobility. The variations among regions reflect the 12

existing policies, urban and socio-economic development situations, largely determining the 13

selection of transport modes. 14

We expect that CO2 emissions from urban passenger transport in developed countries 15

will stabilise by 2030 even without additional policy intervention. This phenomenon is 16

mainly due to the higher fuel efficiency of private cars and market penetration rate of 17

alternative fuelled vehicles, proving their significance in mitigating the CO2 emissions. In 18

general, the CO2 mitigation potential of vehicle technology is higher than the behavioural 19

impacts, according to our preliminary findings. This gives an important lesson for developing 20

countries about the importance of focusing on the regulation of fuel efficiency standards and 21

adoption of new vehicle technologies. 22

In addition to vehicle technology, implementing strategic development of public 23

transport and road infrastructure, effective pricing policies targeting at transit use, car use and 24

ownership and land-use planning policies can directly influence the travel behaviour and 25

demand. The ROG scenario was found to be very effective in reducing car shares to achieve 26

the goal of CO2 reduction. The potential in changing the travel behaviour varies from region 27

to region due to the existing mobility options available in the cities. For instance, EEA + 28

Turkey shows much higher potential in shifting from private car to public transport than 29

North America. 30

Aligning policies towards transit-oriented development reduces the carbon intensity of 31

urban mobility. For comparison with ROG scenario, we have explored the impacts of higher 32

expansion in the public transport supply and well controlled urban sprawl. Such policies in 33

LUTP scenario will reinforce the transit use and further reduce the car use, but with relatively 34

G. Chen, J. Kauppila 13

small magnitudes. Despite their less significant impacts on behaviour shifting, the decrease in 1

the vehicle travel is significantly higher than other two scenarios, thanks to shorter travel 2

distance brought by limited urban area growth, higher urban density and transit stop density. 3

The total vehicle kilometres travelled in 2050 is 13% lower in LUTP scenario than ROG 4

scenario. 5

The set of policies modelled in the ROG and LUTP scenarios, in general, help increase 6

the mobility levels relative to baseline mobility. Breaking down by country groups, 7

developed countries benefit less in the mobility growth than developing countries. Regions 8

which have relatively low cost of car use (e.g. North America and Middle East) are much less 9

sensitive to the pricing policies placed on driving. In LUTP scenario, the overall mobility 10

demand is reduced due to shorter trip distances brought by the well-controlled urban sprawl 11

and the transit-oriented development pattern. These two scenarios satisfy the urban mobility 12

demand with much less carbon intensive options. 13

We have not explored explicitly the impact of improving the non-motorised transport 14

facilities on the demand for walking and cycling, due to the lack of data on supply and quality 15

of the facilities. However, policies promoting the use of non-motorised modes and ensuring 16

safe and convenient walking and cycling facilities are very important to promote and preserve 17

the non-motorised shares in the urban mobility. 18

Out of all the world regions, the most drastic growth in terms of mobility demand and 19

CO2 emissions from urban passenger transport sector will occur in China + India and other 20

developing Asian countries. Mitigating the CO2 emissions is important for the future well-21

being of the citizens living in these two regions, but also crucial to the future of the rest of the 22

world. If the problem of the exponential growth in motorised mobility in these regions is not 23

effectively dealt with, the world will face sharp increases in CO2 emission. A more in-depth 24

examination at the travel behaviour and travel demand is crucial to propose recommendations 25

on the urban transport and development policies tailored for these regions. 26

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