Low Carbon Urban Transportation Policies in Chinese and Indian Cities

Transcription

1 Low Carbon Urban Transportation Policies in Chinese and Indian Cities Wei-Shiuen Ng International Transport Forum (ITF) Organisation for Economic Co-operation and Development (OECD) rue André Pascal F- Paris Cedex France + (0) wei-shiuen.ng@itf-oecd.org Submission Date: August, 0 Word Count:, Number of Figures and Tables:

2 Abstract Chinese and Indian urban cities have experienced rapid population growth, economic development, urbanization, and motorization since the 0s. Although Chinese and Indian cities have grown at different rates over the past three decades, they have similar transportation challenges that would have to be promptly addressed in order to reduce the raising levels of carbon dioxide (CO ) emissions, together with local air pollution, energy use and congestion. This study selected five Chinese and five Indian cities of various sizes, geographical locations and transit services, to illustrate different possible motorization growth patterns and CO emissions levels in 00 and 00. Using three scenarios, this study also examines the impact of different types of policies that could help Chinese and Indian cities reduce carbon emissions from the transportation sector. Each scenario includes varying levels of land use planning, public transportation services, economic instruments, and government regulations that could lead to different future outcomes. A comprehensive set of policies and measures has to be implemented to significantly reduce 00 carbon emissions by to percent across all cities, when compared to the business as usual scenario.

3 INTRODUCTION Chinese and Indian cities are among the fastest growing urban areas in the world. They have experienced rapid population growth, economic development, urbanization, and motorization at alarming rates over the past few decades. Although transportation services have provided positive benefits to urban cities, they have also contributed to high levels of congestion, energy consumption, local air pollution, and carbon dioxide (CO ) emissions. The level of CO emissions from the transportation sector in China has more than doubled from 000 to 0 () and will continue to increase due to the growing number of passenger vehicles. In 0, China surpassed the United States to be the largest automobile market in the world (). More than 0 million vehicles were sold in China in 0, resulting in a total number of million passenger light duty vehicles (LDVs), which include both passenger cars and light trucks, in the same year (). Despite its low vehicle ownership rate, at vehicles per,000 persons, compared to the United States with 0 vehicles per,000 persons (), it is already the world s largest CO emitter and has recently become the top global crude oil importer. As a result, China s demand for oil and subsequent CO emissions will only increase as its transportation sector grows, unless a comprehensive range of policies and measures is implemented to alter the course of development. In India, raising automobile sales, household income, urban population, and urbanization are all leading to higher transportation demand. Urban population has increased from million in to million in 00 (). The number and size of urban cities have also increased, which will ultimately increase travel demand and total distance traveled. The number of passenger vehicles has grown from million in 0 to almost 0 million in 000, reaching a total number of million in 0 (). Most of these vehicles are motorized two-wheelers (Ws) and only million of them are passenger LDVs. Compared to China, India has a much smaller passenger LDV market but the transportation sector is still a leading contributor to Indian cities CO emissions mainly because of motorized Ws. India s passenger vehicle market is heavily dominated by motorized Ws, which are the fastest growing type of vehicle in India, with an average annual growth rate that is higher than all other types of motor vehicles. Hence, in addition to CO emissions, local pollution is also a major problem in Indian cities. The increase in motorization and its subsequent climate change impact in China and India are clearly just at the beginning, as current projections show a continuous growth over the next few decades under a business as usual scenario (,,,,, ). Reductions in CO emissions will ultimately occur when transportation demand shifts to less carbon intensive options and when the total travel distance declines. Policies and measures that support the adoption of advanced vehicle technology and alternative fuel will also reduce CO emission by improving energy intensity. However, as a result of the increasing number of vehicles China and India, advancing vehicle and fuel technology may not be enough to reduce CO emissions, as the growth of vehicle ownership and use can easily surpass any technological improvements made. A range of policies and measures that include land use planning, public transportation development, economic instruments, and governmental regulations must therefore be considered. Such policies and measures have the capacity to directly influence demand and behavior, while complementing efforts that are focused on the improvement of vehicle efficiency and fuel intensity. The development of public transportation in areas with high urban population density and user demand is a way to reduce congestion and emissions. In general and especially so in developing cities, public transportation provides services at a lower cost to the user than driving. However, travel time could be longer for transit than driving in some cases. There is usually a trade-off between travel cost and time when choosing between public transportation

4 0 0 and driving alone. Increase in public transportation efficiency and ridership can also be achieved by investment that will ultimately lead to economic benefits through high economic rates of return in dense cities (). The benefits of public transportation will not be constrained within improving traffic conditions but can also provide greater advantages to the economic growth of the region. Since major urban cities in China and India have vehicle ownership rates that are significantly higher than the national average, they will continue to drive their countries energy use and CO emissions. Transportation policies in cities can thus have a significant impact on emissions reductions both on a local and national scale. This study highlights motorization trends in cities under the context of three different scenarios for 00 and 00. METHODOLOGY This study applies three different scenarios to illustrate the impact of various policies and measures on transportation and CO emissions growth for each of the selected cities. Although each scenario contains a different set of policy assumptions, the framework for estimating transportation demand, energy use and CO emissions remain the same across all scenarios. This study includes a total of six types of transportation modes in all scenarios, namely ) LDVs; ) Ws; ) three-wheelers, but for Indian cities only; ) bus; ) bus rapid transit (BRT), and ) passenger rail. Selected Urban Cities The selected urban cities in this study include five Chinese and five Indian cities that reflect a wide range of population size, motorization rates and existing transportation policies and services (Table ). Some cities offer more transit services than others. For example, out of the ten cities in this study, only three cities offer both BRT and metro rail transit services. On the other hand, the selected cities have rapid population growth, economic development, urbanization, and motorization rates in common. TABLE Transportation Characteristics of Selected Chinese and Indian Cities in 0 City (Million) Number of LDVs (Million) Number of Ws (Million) China LDV Rate W Rate Vehicle Restriction (Year) Beijing Yes (0) Transit Services Bus, BRT, Metro Shanghai... Yes () Bus, Metro Bus, BRT, Guangzhou Yes (0) Metro Tianjin.. 0. Yes (0) Bus, Metro Xi an N/A Bus, Metro India Mumbai N/A Bus, Metro Delhi... N/A Bus, Metro Bangalore. 0.. N/A Bus, Metro Ahmedabad N/A Bus, BRT

5 0 0 0 Number of LDVs (Million) Number of Ws (Million) Vehicle Restriction (Year) City (Million) LDV Rate W Rate Transit Services Bus, BRT, Jaipur N/A Metro Note.. LDVs include cars, sports utility vehicles (SUVs) and pickup trucks.. Ws refer to two-wheelers, such as scooters, mopeds and motorcycles.. Vehicle restriction refers to vehicle registration restriction policies that have been implemented in several cities in China. This policy limits annual vehicle ownership by restricting the number of vehicles that can be registered at a given time period through auctions or lottery schemes.. Transit services in all cities include bus services, but not all cities offer BRT and/or metro services currently. Source. Chinese Cities Statistical Yearbooks for population, LDVs and Ws data in Chinese cities. TERI data () for population, LDVs and Ws data in Indian cities. Passenger Transportation Demand Transportation demand can be measured by distance traveled, i.e. vehicle kilometer (vkm) or passenger kilometer (pkm). This section describes how projections of passenger transportation demand of six different modes were estimated for 00 and 00. Private Vehicle Motorization Rates Long term passenger transportation demand projections for both LDVs and Ws in Chinese and Indian cities were modeled against trends that were observed in other Asian urban cities that have experienced rapid motorization growth over the past few decades. Since most of the motorization growth occurs in major cities in both China and India, it is assumed that the national motorization rates will reflect urban motorization rates, if not underestimate them. Urban motorization rates are therefore, estimated by using national motorization rates as a benchmark. The two countries that have the most similar motorization and economic development patterns to China and to a certain extent, India, are Japan and South Korea. By comparing the motorization rates of these four countries, it was found that the LDV per GDP ratio for India in was very similar to South Korea s level in. China s LDV per GDP ratio in 0 was in fact identical to South Korea s level in 0 (Table ). This study assumes that China s motorization rate in 00 will match South Korea s 0 level, while in 00, China will have South Korea s 00 motorization rate. Due to the relatively low motorization growth rate compared to South Korea s current rate, India s motorization rate in 00 will be similar to South Korea s in 0, while in 00, it will have South Korea s 0 motorization level. Although few countries have characteristics that match China and India (), Japan and South Korea, which both have significant domestic automobile industries and strong historical economic growth, do project motorization growth trends that China and India could very possibly follow (Figure ). As shown in Figure, motorization rates, defined by the number of passenger LDVs per,000 population, for India and China closely follow those of South Korea s. India s motorization path for LDVs seems to follow the path defined by China but at a much slower rate, while China s recent motorization levels from 00 onwards, with respect to GDP per capita, were very close to what South Korea experienced in the 0s. India s current motorization rate is equivalent to China s level in the mid-000s.

6 00 Motoriza(on Rate (LDVs per,000 Popula(on) Japan ( - 0) South Korea (0-0) India ( - 0) China (0-0) GDP per Capita (000 USD PPP) FIGURE Comparison of motorization rate (LDVs per,000 population) with respect to per capita GDP in Japan, South Korea, India, and China. The range of years included for each country is shown in parentheses in the legend. Source. Author s estimation using International Energy Agency vehicle database for LDV data, UN World population data and OECD for per capita GDP data. Despite the increasing motorization projections in Chinese cities, the estimated LDV motorization rate for China was only applied in Xi an, as it is currently the only city out of the five Chinese cities included in this study that does not have a vehicle registration restriction policy. Therefore, it is the only city that will follow the motorization path identical to South Korea s. A similar approach was applied to derive two-wheeler motorization rates, by comparing the number of Ws per,000 population in Chinese and Indian cities with other Asian cities with high two-wheeler motorization rates with respect to per capita GDP. Figure shows the recent two-wheeler motorization rates in the ten cities selected in this study. Bicycles are excluded from Ws in this case. As shown in Figure, Indian cities have much higher levels of two-wheeler motorization than Chinese cities. The average 0 W motorization rate in Chinese cities was, yet 0 in Indian cities. Chinese cities are not only having lower W motorization rates than in Indian cities, they are also seeing a decrease in W motorization rate in recent years. The decrease in W motorization rate was most significant in Guangzhou and Tianjin, where W motorization rates decreased by more than 0 percent between 00 and 0. This trend was most likely caused by the scrapping and banning of motorcycles, triggered by the need to reduce congestion, traffic accidents and crime, especially in Guangzhou where motorcycles were completely banned in 00. In fact, all five Chinese cities included in this study has banned or restricted motorcycles to a certain extent (). Since motorcycle use is heavily restricted in most major urban cities in China, it is unlikely that its rate would increase in the near future, unless there are new policies introduced. Moreover, since the per capita GDP values of the five Chinese cities are already

7 over or close to US$,000, the decline in the W motorization growth has started to occur, as observed in other Asian cities (). The W motorization rate for the selected Chinese cities will thus follow the rate, which is motorcycles per,000 population, as observed in other global cities. This study assumes that W motorization rates for Chinese cities will remain low through 00, but not for Indian cities (Table ). When compared to Asian cities with high W motorization rates, such as Hanoi, the Indian cities in this study have relatively low motorization rates with respect to per capita GDP. Hanoi s per capita GDP was approximately US$, in 0, when its W motorization rate was approximately 00. The average 0 per capita GDP for the five Indian cities in this study was almost twice as much as Hanoi s per capita GDP in the same year, yet their average motorization rate was only a third of Hanoi s. However, when compared to other non-affluent Asian cities, where there are motorcycles per,000 population (), most Indian cities have a relatively high W motorization rate. It is assumed that the growth in Ws will continue in Indian cities until 0, which is the point when per capita GDP reaches $,000 and above for all five cities. 0 Motoriza(on Rate (Ws per,000 Popula(on) FIGURE Motorization rate for two-wheelers, excluding bicycles, in five Chinese and five Indian cities. Source. Chinese Cities Statistical Year Books (00 0) for Chinese Ws data; TERI () for Indian cities data.

8 0 0 TABLE Motorization Rate Assumptions for 00 and 00 City Beijing Shanghai Guangzhou Tianjin LDV Two-Wheeler Three-Wheeler Vehicle Vehicle Restriction Restriction N/A N/A Vehicle Vehicle Restriction Restriction N/A N/A Vehicle Vehicle Restriction Restriction N/A N/A Vehicle Vehicle Restriction Restriction N/A N/A Xi an 0 N/A N/A Mumbai Delhi Bangalore Ahmedabad Jaipur Note. LDV motorization rates for Beijing, Shanghai, Guangzhou, and Tianjin are not applicable, as these four cities have implemented vehicle registration restrictions, which are assumed to still be relevant in 00 and 00. Motorization rates are defined by the number of vehicle per,000 population. After deriving motorization rates for both LDVs, Ws and three-wheelers, which were used as saturation points in 00 and 00 (Table ), the total number of vehicles was then estimated for every five year interval up to 00. The total number of vehicles remains constant across all three scenarios, whereas vehicle use will fluctuate according to the assumptions made in each scenario. Total transportation demand, in the form of vkm, was then estimated by multiplying vehicle stock by distance traveled per vehicle per year. For LDVs, the average distance traveled per vehicle per year assumed in this study for the five Chinese cities was based on Huo et al. s (0) estimations, which showed a gradual decrease in vehicle traveled distance over time, ranging from,00km in 000 to,00km in 00 per vehicle. Average annual distance traveled per vehicle for Indian cities were derived from TERI s estimations (), which ranged between,km in 000 to,km in 00. For Ws, Chinese and Indian cities have more similar average annual distance traveled, ranging from,0km in 000 to,km in 00 (, ). In order to obtain total passenger travel, i.e. pkm, vkm would have to be multiplied by the average load factor. The average load factor used in this study was derived from the IEA s estimations () for both Chinese and Indian cities, where LDVs load factor ranged from. in 000 to. in 00 and the same W load factor of. was used for all cities for every year to 00. LDVs load factor is assumed to decrease over time. Similarly to the number of vehicles, the estimations for annual distance traveled per vehicle and load factor for all time periods remain the same across all three scenarios. Public Transportation Projections for bus and passenger rail transportation demand follow the population density growth. For the case of passenger rail, the projections were also estimated based on per capita GDP growth rates to 00. BRT projections were based on current and proposed corridor length, which varies by city, ranging from to km (). The number of BRT buses in each city was then estimated using the historical vehicle-corridor ratio of..

9 0 0 Energy Use and CO Emissions Within each scenario, energy use was estimated for each mode based on the IEA s Mobility Model (MoMo) (), which has a detailed characterization of vehicle technology and fuel mix. MoMo includes a range of powertrain technologies, such as internal combustion engines that run on gasoline, diesel, compressed natural gas (CNG), or liquid petroleum gas (LPG), hybrid electric, plug-in hybrid electric, electric, and fuel cell powertrains. Related fuel options in MoMo hence include, gasoline and diesel, biofuel and synthetic fuel alternatives to liquid fuels, gaseous fuels including natural gas and hydrogen, and electricity. Total energy use (E) for vehicle type i can therefore, be estimated using the following equation. E! = (N! I! D! ) () where, E! is the total energy use, N! is the total number of vehicle type i, I! is the energy intensity for vehicle type i, which also reflects fuel type, and D! is the average distance traveled by vehicle type i. Total CO emissions were then estimated for each fuel type using coefficients of carbon per unit of energy consumed in vehicle type i. SCENARIOS A set of three scenarios was developed for each city, projecting transportation demand and CO emissions at an interval of five years to 00. The scenarios reflect a range of policies and measures, such as land use planning, public transportation development, economic instruments, and governmental regulations that have shown to influence transportation demand and emissions. The impact of such policies and measures on transportation demand is evaluated using demand elasticity estimates from existing studies. Price elasticity of demand is often used as a standard measurement to compare demand responses to changes in price, transit service, or other variables (). It is assumed that a cumulative impact of the pricing policies will have a smaller combined effect on transport demand. Long term elasticity estimates are also higher than short term estimates, as individuals adapt to the changes in price and alter their travel behavior accordingly over time. Travel behavior in certain cities may take longer to change due to existing transportation options and constraints. The scenario outcomes are not predictions, but simply different possible futures based on the assumptions applied in each scenario (Tables and ). Each scenario illustrates the impact of various policies and measures that are capable of influencing future motorization and CO emissions trends. TABLE Key Assumptions for Chinese Cities Scenarios Business as Usual Robust Governance Integrated Land Use and Transportation Planning % Increase from UN World % Increase from UN World Density Public Transportation Development Average Travel Time (Min) BRT Availability Beijing and Guangzhou Beijing and Guangzhou Beijing and Guangzhou Beijing and Guangzhou All Cities All Cities

10 Scenarios Business as Usual Robust Governance Integrated Land Use and Transportation Planning Density % Increase from UN World % Increase from UN World Economic Instruments Fuel Tax Increase (%) No Change No Change (Similar to Korea) (Similar to Japan) (Similar to Korea) (Similar to Japan) Parking Pricing (US$/hr) Road Tolls (US$) Bus Subsidy Increase (%) No Change No Change Rail Subsidy Increase (%) No Change No Change Governmental Regulations Annual Vehicle Registration Restriction Beijing,00,00,0,00,0,00 Shanghai 0,000 0,000,0,0,0,0 Guangzhou,000,000,,,, Xi an N/A N/A 0,000,0 0,000,0 Tianjin 0,000 0,000 0,000,0 0,000,0 Fuel Economy Standards IEA DS IEA DS IEA DS IEA DS IEA DS IEA DS Note.. Based on average commute time by public transportation ().. Fuel economy standards reflect the type of fuel mix and are based on IEA s assumptions in the C Scenario (DS) and the C Scenario (DS). The C Scenario (DS) takes into account countries current intentions to limit emissions and improve energy efficiency that will limit the long-term temperature increase to C. The DS reduces CO emissions by almost 0 percent by 00 (compared with 0) and a projection of declining carbon emissions post 00 until carbon neutrality is reached (). TABLE Key Assumptions for Indian Cities Scenarios Business as Usual Robust Governance Integrated Land Use and Transportation Planning % Increase % Increase from UN from UN World World Density Public Transportation Development Average Travel Time (Min) BRT Availability Economic Instruments Fuel Tax Increase (%) Delhi, Ahmedabad, Jaipur, and Indore Delhi, Ahmedabad, Jaipur, and Indore Delhi, Ahmedabad, Jaipur, and Indore Delhi, Ahmedabad, Jaipur, and Indore All Cities All Cities No change No change (similar to Japan) (Similar to Japan) (similar to Japan) (Similar to Japan) Parking Pricing (US$/hr) Road Tolls (US$) Bus Subsidy Increase (%) No change No change Rail Subsidy Increase (%) No change No change Governmental Regulations Fuel Economy Standards IEA DS IEA DS IEA DS IEA DS IEA DS IEA DS Note.. Based on average commute time by public transportation.. Fuel economy standards reflect the type of fuel mix and are based on IEA s assumptions in the C Scenario (DS) and the C Scenario (DS). The C Scenario (DS) takes into account countries current intentions to limit emissions and improve energy efficiency that will limit the long-term temperature increase to C. The DS reduces CO emissions by almost 0 percent by 00 (compared with 0) and a projection of declining carbon emissions post 00 until carbon neutrality is reached ().

11 Business as Usual Scenario In the Business as Usual (BAU) scenario, no major new public transportation development, economic instruments, or governmental regulations will be implemented in the selected cities, apart from those that have already been introduced. For example, existing restrictions on vehicle registration in Chinese cities will continue but not spread to other cities that currently do not have such regulations. Motor vehicle ownership and use will continue to increase for cities without any restrictions on vehicle growth. These cities include all five Indian cities and one Chinese city, i.e. Xi an. There is no substantial effort to improve public transportation services, in terms of reducing bus and rail travel time or developing BRT systems in cities where such systems are currently unavailable. Advanced vehicle technology and alternative vehicle fuel use will continue to penetrate the market but at a relatively low rate, especially for Indian cities. There will also be no significant improvement in fuel efficiency standards, which coincides with the IEA DS (). Robust Governance Scenario In the Robust Governance (ROG) scenario, city governments play a larger role in regulating vehicle use and ownership. Governmental regulations and standards refer to non-market based policies, such as restrictions on annual vehicle ownership growth and fuel economy standards. A cap on vehicle growth rate through tight vehicle quota control regulations will ensure reductions in congestion, local air pollution and global carbon emissions. Auctions or lottery schemes that distribute the limited number of license plates available will accompany such regulations, as currently implemented in cities such as Singapore, Shanghai and Beijing. In this scenario, restrictions on vehicle registration will exist in all Chinese cities. Most of these cities will also gradually decrease their respective quota between 00 and 00. Fuel efficiency standards in this scenario follow the assumptions made in the IEA DS (), which are more stringent than in the BAU scenario. Fuel economy standards can significantly improve vehicle efficiency and reduce emissions by pressurizing manufacturers to produce more efficient vehicles. Such standards are usually implemented on a national level but often complement local measures. In addition to changes in vehicle registration and fuel efficiency standards by 00, other economic instruments will also be implemented. Economic instruments can change the total cost of driving or public transportation, through the form of a direct pricing policy or subsidy. Pricing indicators that individual users are sensitive to include fuel cost per mile, parking costs, road pricing, tolls, and transit fare. In this scenario, fuel taxes, road tolls and parking pricing will be widely implemented in all Chinese and Indian cities, leading to a general increase of percent in the cost of driving in 00 and percent in 00. More details are shown in Tables and. There will also be an increase in bus and rail subsidies, which will lower the cost of public transportation to users by 0 percent in 00 and 0 percent in 00. Integrated Land Use and Transportation Planning Scenario In the third scenario, Integrated Land Use and Transportation Planning (LUT), there is a greater urge for sustainable urban transportation development in Chinese and Indian cities. Therefore, economic instruments assumed in the ROG scenario will be implemented together with appropriate urban planning measures that will reduce travel distance and urban sprawl through an increase in population density. density will be percent and

12 0 0 percent higher in 00 and 00 respectively in this scenario compared to the BAU and ROG scenarios. This could be achieved by the development of more mixed use transit corridors. In this scenario, public transportation will also be greatly improved through the decrease of bus and rail travel time by 0 percent in 00 and 0 percent in 00. The elasticity of public transportation travel time on public transportation demand can be much higher than price elasticity (). BRT services are available in all cities in this scenario, which will further decrease transit travel time, yet increase accessibility. The same governmental regulations and fuel efficiency standards assumed in the ROG scenario are also valid in this scenario. SCENARIO RESULTS This section describes total passenger travel, energy use and CO emissions results from the three policy scenarios for each city. Total Passenger Travel In general, total passenger travel measured in pkm will increase from the base year under all scenarios between percent (Beijing in ROG) and over 00 percent (Bangalore in LUT) in 00. Total pkm in Chinese cities will increase at a lower rate through 00 across all scenarios, yet the opposite is true for the five Indian cities. When comparing the changes in total passenger travel between BAU and ROG or LUT, it was found that total pkm will decrease more in ROG than LUT for Beijing and Shanghai, while pkm in Guangzhou will decrease in ROG but increase in LUT from BAU in 00. Total pkm in Xi an and Tianjin will be the lowest in LUT compared to the BAU and ROG scenarios in 00. Total Energy Use In the 0 base year, Beijing had the highest, while Jaipur had the lowest level of energy use out of all Chinese and Indian cities (Figure ). Without any form of changes in the regulations of motor vehicle growth, energy use in Indian cities will increase by an average of almost 00 percent in 00 and,000 percent in 00 from the base year in the BAU scenario. Combining pricing policies and integrating land use and transportation planning, as well as increasing the energy efficiency of vehicle technology will lead to much lower energy use levels in ROG or LUT than in BAU, especially for Indian cities.

13 Total Energy Use (Mtoe) Base Year (0) BAU (00) ROG (00) LUT (00) 0 FIGURE Total energy use in 00 (Mtoe) for base year and three policy scenarios in 00. Total CO Emissions Although emission levels for each city mirror its respective energy use, emission trends do vary by city (Figure ). The significant decreases in emissions in the Chinese cities are due to the vehicle ownership regulations that will become more stringent over time, as well as reductions in vehicle use due to the assumptions made in the ROG and LUT scenarios. Xi an, which is the only Chinese city in this study that does not have a limitation on vehicle ownership will continue to have increasing emission levels in the BAU and ROG scenarios. CO emissions will only decrease in Xi an when a wider range of policies and measures are implemented as assumed in the LUT scenario.

14 .00 Carbon Dioxide (CO) Emissions (Mt) Base Year BAU (00) ROG (00) LUT (00) FIGURE Total CO emissions under three policy scenarios in 00. Emission trends in Indian cities will greatly differ from those in the five selected Chinese cities and will follow a much more linear trend over the next few decades. Nevertheless, the rate of growth could decrease as shown in the ROG and LUT scenarios if cities implement appropriate transportation policies and measures to change travel demand and behavior. Over the long term, emission gaps among Indian cities would narrow, so will the reduction levels between Chinese and Indian cities. In 00, Beijing will achieve an emission reduction rate at three times that of Delhi s, while their average difference in 00 is only 0 percent. DISCUSSION AND CONCLUSIONS Although certain policies have a higher impact on transportation demand than others, the influence of one policy on another is often uncertain. A policy with a relatively smaller impact than another policy can serve a complementary or supporting role in a complex transportation system. Key policy insights identified based on the outcomes from each of the three scenarios are described in this section. First, government regulations are complementary to economic instruments in reducing passenger vehicle use and CO emissions. Xi an, the only Chinese city selected in this study without an existing regulation on vehicle registration restriction, had in fact the lowest number of LDVs in 0 compared to all the other four cities. However, without any governmental regulation, its total number of vehicles would increase by 0 percent and percent in 00 and 00 respectively, as seen in the BAU scenario. Government regulations are critical in regulating vehicle growth and vehicle use, especially in cities that are already encountering severe traffic congestion and local air pollution. In the five Indian cities selected for this study, the increase in passenger LDVs can be as high as percent as seen in

15 the city of Mumbai. This possible outcome can be avoided by a mix of government regulation and economic instruments, in the form of a fuel tax, road pricing, parking fee, or transit subsidies, which will provide incentives to drive less or use public transportation more. Such policies and measures are reflected in the ROG scenario and can reduce passenger light duty vehicle use by up to. percent in 00. The impact on vehicle use and emission reduction will be the strongest when both types of policies are implemented at the same time, as seen in the ROG scenario. Economic instruments can be used to influence vehicle use and distance traveled, while government regulations have the capacity to regulate vehicle ownership. In order to effectively reduce CO emissions from the transportation sector, both types of policies have to be implemented simultaneously. Second, economic instruments need be coupled with integrated land use and transportation planning to ensure maximum accessibility. Land use and transportation planning strategies that can change the density of urban cities, diversity of activities in neighborhoods and distance traveled will be able to reduce transportation demand and CO emissions subsequently. Accessibility to public transportation infrastructure and services or the quality and coverage of existing public transportation network will affect driving frequency and distance. An increase in population density will reduce total distance traveled in the short and long term, which can be due to the agglomeration of businesses and decreases in travel cost. Travel distance will decrease as accessibility to goods and services or to jobs has improved due to high levels of urban population density. The LUT scenario illustrates a possible future where CO emissions can be influenced by a combination of economic instruments, such as pricing, as well as land use and transit improvement policies and measures. In this scenario, pricing policies help regulate demand, which would not be altered if otherwise. When implementing economic instruments to regulate vehicle use, alternative modes have to be provided to ensure maximum accessibility. Therefore, the implementation of a combination of different types of policies is required to achieve lower light duty vehicle use and CO emission. Generally, the decrease in LDV use from the BAU scenario in the LUT scenario is significantly higher than in the ROG scenario. In the LUT scenario for all cities, LDV demand has decreased by up to percent in 00, while it has only decreased by up to percent in the ROG scenario. Meanwhile, annual bus passenger travel has increased by percent in all cities in 00 in the LUT scenario, but only increased by percent in the ROG scenario. The third key policy insight is that the improvement of public transportation services, in the form of convenience or reduced travel time, is essential to increase ridership. Given the high population densities in Chinese and Indian cities, public transportation can be very effective in reducing CO emission through the connection of the existing transportation modes within the system, especially since public transportation share in these cities remain relatively high at a current average of 0 percent. Public transportation services using high capacity vehicles in areas with high population densities and user demand will reduce congestion and emissions, as an increase in ridership can attract enough passengers to offset the energy consumed by the vehicles. Since the cost of public transportation is relatively low in Chinese and Indian cities, the provision of further transit subsidies may not be effective in encouraging greater public transportation use. Another way to increase its appeal is to improve the quality of the services, for example, by decreasing travel time. This implies the development of faster travel modes with greater convenience through scheduling and adjusting frequency. Speed can be further enhanced by using dedicated roadways or introducing BRT systems, and enabling smoother transfers between vehicles and other transportation modes. As the speed of transit vehicles increases, public transportation modes will be able to serve more passengers, increase ridership demand and maintain transit use over time.

16 0 0 0 The LUT scenario reflects the improvement of bus and passenger rail travel time and the availability of BRT systems in all cities. Compared to the BAU scenario, bus ridership will increase by to percent in Chinese cities and by to percent in Indian cities in 00. Bus ridership in the ROG scenario, which only included transit subsidies as a policy to increase ridership, will increase on a much smaller scale. A similar trend is observed for passenger rail ridership. Hence, the improvement of transit services in the form of decreasing travel time is more effective in increasing demand and reducing emissions than providing transit subsidies. The final key policy insight concerns the timing of policy implementation. Cities should act now to reduce transportation CO emissions as the timing of policy implementation is critical in the long term. In 0, Beijing had the highest passenger light duty vehicle demand (passenger km) in China, while Delhi had the highest demand in India. This fact remains true by the end of 00 in all three scenarios. Even though the actual number of vehicles has been controlled in Beijing, its passenger demand remains high regardless of the policy scenarios. This is likely due to the fact that Beijing only started regulating its passenger vehicle growth in 0, whereas Shanghai implemented its vehicle ownership policy years earlier in. By the time Beijing started regulating its passenger vehicles, the total number of LDVs in Beijing was already more than three times greater than Shanghai s. The rate of vehicle ownership in Beijing in the same year was LDVs per,000 persons, which was four times higher than Shanghai s vehicle ownership rate. Despite having a larger population and higher GDP level, Shanghai has managed to keep its vehicle ownership rate low, not just because it implemented a vehicle restriction policy, but also because it started early while its vehicle ownership rate was still relatively low. Cities have to act now to avoid greater levels of traffic congestion, local air pollution, energy use, and global CO emissions. The sooner policies and measures are designed and implemented, the more effective they will be. While technological advancement in vehicle and fuel will lead to reductions in CO emissions, as shown by the differences in total energy use and CO emissions between BAU and ROG or LUT, further reductions will need to be derived from non-technological measures that target both vehicle ownership and use. A more detailed analysis on behavioral responses to different types of policies and measures in Chinese and Indian cities is necessary. A mode choice model for each city can also be integrated into the emission model to provide a more robust set of motorization projections. REFERENCES. CAIT Climate Data Explorer. Historical Emissions. World Resources Institute, Washington D.C. Assessed July, 0.. CAAM. Automotives Statistics. 0 China Vehicle Sales. China Association of Automobile Manufacturers, Beijing. Assessed March, 0.. CAAM. Automotives Statistics. 0 China Vehicle Sales. China Association of Automobile Manufacturers, Beijing. Assessed March, 0.. Wang, Y., J. Teter, and D. Sperling. 0. China s Soaring Vehicle : Even Greater Than Forecasted? Energy Policy, Vol., No., 0, pp. 0.

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