Dongquan He et al., Energy use of, and CO2 emissions from China’s urban passenger transportation sector – Carbon mitigation scenarios upon the transportation mode choices, Transportation Research Part A: Policy and Practice Volume 53, July 2013, Pages 53–67, doi:10.1016/j.tra.2013.06.004
Abstract: The article estimates the energy consumption and CO2 emissions from China urban passenger transportation sector up to year 2030. The methodology used is to estimate the emissions based upon passenger travel behaviors in cities. This methodology enables the policy analysts to quantify how different urban development strategies and patterns would affect about CO2 emissions, also directly link behavioral changes with urban development patterns and analyze the sensitivities of the urban passenger transportation sector in responding to both national- and city-level policies for carbon mitigation, thus helping the policy evaluation and development.
2. Where do the authors work, and what are their areas of expertise? Note any other publications by the authors with relevance to the 6Cities project.
Dongquan He, Fei Meng, Yang Jiang and Peter Calthorpe: The Energy Foundation in Beijing.
Huan Liu, Kebin He, Jiaxing Guo and Qidong Wang: State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, School of Environment, Tsinghua University, China.
Michael Wang: Systems Assessment Section of the Energy Systems Division at Argonne National Laboratory, USA.
Jiangping Zhou: College of Design, Iowa State University, USA.
Zhiliang Yao: School of Food Science, Beijing Technology and Business University, Beijing, China.
3. What are the main findings or arguments presented in the article or report? The results showed that mode choice changes are the most sensitive to policies. Promoting public transportation and limiting car usage can contribute 21% of the total energy reduction of China’s transportation sector in 2030. Enhancing the above by optimizing street network and urban form, this contribution can be doubled in size.
4. Describe at least three ways that the argument is supported.
Business As Usual (BAU) Scenario: There would be no significant policy interventions and urban development patterns and the mode share changes will follow the trajectory of the past. Based on data availability, the article selected the mode split in 2007?009 as the baseline for this analysis.
Transportation Policy Scenario (TPS): This scenario takes into account the national policies that were recently adopted to promote the public transit development at the city level. The most comprehensive description of the policies is in the ‘‘12th Five Year Plan (FYP) for Public Transit Development in China’’. The FYP sets clear targets for public transit development in China.
Enhanced Policy & Urban Optimization scenario (EPUOS): This scenario assumes that on top of the interventions in TPS, stronger urban planning and design policies are introduced to guide urban development patterns. The current urban development pattern characterized by “superblock”, single land use function, and broad arterial roads notably encourages car use. To effectively control the work and non-work trips, local development designs (stop superblocks) and city-wide designs (stop urban sprawl) are necessary. Studies showed that a smart growth pattern with smaller blocks, a higher degree mixed land use, and a dense road network to accommodate high quality NMT facilities and continuous rather than discrete public spaces would significantly reduce car use, encourage public transit and NMT.
5. What three (or more) quotes capture the message of the article or report?
“This paper estimates the energy consumption and CO2 emissions from China’s urban passenger transportation sector up to year 2030. A ‘‘bottom-up’’ methodology is developed to estimate the emissions based upon passenger travel behaviors in cities, which is notably different from popular existing approaches that calculate emissions from vehicular population. This methodology enables policy analysts to (1) quantify how different urban development strategies and patterns would affect about CO2 emissions; (2) directly link behavioral changes with urban development patterns and policies; and (3) analyze and understand the sensitivities of the urban passenger transportation sector in responding to both national- and city-level policies for carbon mitigation, thus helping the policy evaluation and development.”
“Detailed information regarding urban passenger travels are collected in grouped Chinese cities of six categories. With the newly developed methodology, total carbon emissions from China’s urban passenger transportation sector under three scenarios are considered. The results showed that mode choice changes are the most sensitive to policies. Promoting public transportation and limiting car usage can contribute 21% of the total energy reduction of China’s transportation sector in 2030. Enhancing the above by optimizing street network and urban form, this contribution can be doubled in size.”
“This paper sets up a new bottom-up methodology to estimate the total energy consumption and CO2 emissions of urban passenger transportation based on personal behavior. The basic activity data of China cities, including population, trips, trip mode, distance, vehicle load, and etc., are presented. Three major scenarios, Business As Usual (BAU), Transportation Policy Scenario (TPS), and Enhanced Policy & Urban Optimization scenario (EPUOS), are analyzed using this new method.”
6. What were the methods, tools and/or data used to produce the claims or arguments made in the article or report?
The article sets up a new bottom-up methodology to estimate the total energy consumption and CO2 emissions of urban passenger transportation based on personal behavior. The basic activity data of China cities, including population, trips, trip mode, distance, vehicle load, and etc., are presented. Three major scenarios, Business As Usual (BAU), Transportation Policy Scenario (TPS), and Enhanced Policy & Urban Optimization scenario (EPUOS), are analyzed using this new method.
This method is more detailed compared with fuel consumption statistics (Pokharel et al., 2002). The method is straightforward and has been widely used to support national-level policies regarding fuel efficiency and fuel taxes. The method, however, is subject to constraints or difficulties such as:(1) Urban development patterns have notable impacts on residents’ transport mode choice, travel time, travel length and travel frequency. Cities could adopt policies that significantly influence residents’ travel behaviors and CO2 emissions from residents’ travel. That is, the causes of vehicle population and vehicle usage intensities are not built into this method. (2) The registered vehicle population in one city often does not equal actual vehicles on road in the city. The situation becomes more complicated in less developed cities where the registered vehicle number has never been evaluated with the actual on road vehicle activity. (3) The city-wide average travel distance is often inaccurate or unavailable, especially in small and less developed cities. This leads to a surprisingly wide range of estimated VKT in different researches, from 20,000 to 50,000 km (Wang et al., 2011). The total travel demand, trip modes and fuel use intensity are estimated. The following equations were used to determine energy use of, and CO2 emissions from the urban passenger transport sector: Where i, j represent trip mode i powered with fuel type j. Fuelj is the consumption of fuel j (in tons per day). Trips and Residents represent number of trips per person per day, and number of residents in city (million) respectively. Pi,j, Distancei,j, and Loadi,j represent mode split (%), travel distance for each mode (km) and load factor for each mode (persons). Fei,j and density j represent fuel consumption rate based on per distance driven for each mode (L/100 km), and the density of fuel j (kg/L). CO2 is the CO2 emissions (tons); Carbon_intensityj is the carbon content of fuel j (by mass).
This methodology enables one to: (1) Estimate of fuel consumption and CO2 emissions at the city or region level for passenger transportation and get the national total CO2 emissions with a ‘‘bottom-up’’ approach. (2) Utilize as much as possible reliable data in Chinese cities such as population, average person travel distance, mode share. (3) Make more realistic assumptions in cities where relevant data are not available by using reliable data from cities of a similar size and development level. (4) Link comprehensive urban policy scenarios with carbon emissions via travel behavior changes. Overall, the approach is designed to use data that are required and collected by local municipal governments. Constrained by the data quality and hoarding issues in China (Zhou, 2012a), there were still cases where some data still had to be approximated. But in the long run the methodology could be supported by more reliable data.
7. How (if at all) are health disparities or other equity issues addressed in the article or report?
Three scenarios were developed in the article: Business As Usual (BAU), Transportation Policy Scenario (TPS), and Enhanced Policy & Urban Optimization scenario (EPUOS). Figure 1 shows the comparison of the CO2 emission per capita among different city categories. There are significant differences among city categories. For the BAU scenario, the trend from 2007 to 2030 shows the CO2 emission per capita in City Categories A and B will increase to as high as 0.95 tons/year. CO2 emission per capita from towns (City Category F) in 2030 is 0.56 tons/year, which is the lowest among all city categories, but would be even higher than the level in 2007 in City Categories A. If TPS could be materialized, the per capita emissions still increase for every city category, but the upper limit would be 0.73 tons/year. The emissions per capita in City Categories B and C would catch up with that in City Category A. If EPUOS were realized, the emission per capita in City Category A would decrease to 0.45 ton/year in 2030. The per capita CO2 for Category B would start to decrease from 2022, while the other cities’ would still be on the rise before 2030. To summarize, the TPS will lead to a reduction of per capita emissions by 17–34% in 2030, while reduction from the EPUOS would be 38–53%, depending on city category.
8. Where has this article or report been referenced or discussed? (In some journals, you can see this in a sidebar.)
Bin Xua and Boqiang Linc, Factors affecting carbon dioxide (CO2) emissions in China's transport sector: a dynamic nonparametric additive regression model, Journal of Cleaner Production Volume 101, 15 August 2015, Pages 311–322, doi:10.1016/j.jclepro.2015.03.088
Xiaoyu Yana and Roy J. Crookesb, Energy demand and emissions from road transportation vehicles in China, Progress in Energy and Combustion Science Volume 36, Issue 6, December 2010, Pages 651–676, doi:10.1016/j.pecs.2010.02.003
Peter Wells and Xiao Lin, Spontaneous emergence versus technology management in sustainable mobility transitions: Electric bicycles in China, Transportation Research Part A: Policy and Practice Volume 78, August 2015, Pages 371–383, doi:10.1016/j.tra.2015.05.022
9. Can you learn anything from the article or report’s bibliography that tells us something about how the article or report was produced?
From the bibliography we can find out that this article is based on the government annual reports on transportation aspect and the research papers of how the transportation policies influence the air quality. It is developed by analyzing the situation of urban development pattern, vehicle population, and people’s travel mode. Then research on what are the fuel saving and CO2 reduction potential in each different policy scenarios.
10. What three points, details or references from the text did you follow up on to advance your understanding of how air pollution science has been produced and used in governance and education in different settings?
The fuel consumption and carbon emissions from China urban transportation sector would continuously increase over the next two decades, with the solid demand for urbanization. Cities should also choose the development pattern based on the carbon reduction target.
2. Where do the authors work, and what are their areas of expertise? Note any other publications by the authors with relevance to the 6Cities project.
3. What are the main findings or arguments presented in the article or report?
The results showed that mode choice changes are the most sensitive to policies. Promoting public transportation and limiting car usage can contribute 21% of the total energy reduction of China’s transportation sector in 2030. Enhancing the above by optimizing street network and urban form, this contribution can be doubled in size.
4. Describe at least three ways that the argument is supported.
5. What three (or more) quotes capture the message of the article or report?
6. What were the methods, tools and/or data used to produce the claims or arguments made in the article or report?
(2) The registered vehicle population in one city often does not equal actual vehicles on road in the city. The situation becomes more complicated in less developed cities where the registered vehicle number has never been evaluated with the actual on road vehicle activity.
(3) The city-wide average travel distance is often inaccurate or unavailable, especially in small and less developed cities. This leads to a surprisingly wide range of estimated VKT in different researches, from 20,000 to 50,000 km (Wang et al., 2011).
The total travel demand, trip modes and fuel use intensity are estimated. The following equations were used to determine energy use of, and CO2 emissions from the urban passenger transport sector:
Where i, j represent trip mode i powered with fuel type j. Fuelj is the consumption of fuel j (in tons per day). Trips and Residents represent number of trips per person per day, and number of residents in city (million) respectively. Pi,j, Distancei,j, and Loadi,j represent mode split (%), travel distance for each mode (km) and load factor for each mode (persons). Fei,j and density j represent fuel consumption rate based on per distance driven for each mode (L/100 km), and the density of fuel j (kg/L). CO2 is the CO2 emissions (tons); Carbon_intensityj is the carbon content of fuel j (by mass).
(1) Estimate of fuel consumption and CO2 emissions at the city or region level for passenger transportation and get the national total CO2 emissions with a ‘‘bottom-up’’ approach.
(2) Utilize as much as possible reliable data in Chinese cities such as population, average person travel distance, mode share.
(3) Make more realistic assumptions in cities where relevant data are not available by using reliable data from cities of a similar size and development level.
(4) Link comprehensive urban policy scenarios with carbon emissions via travel behavior changes. Overall, the approach is designed to use data that are required and collected by local municipal governments. Constrained by the data quality and hoarding issues in China (Zhou, 2012a), there were still cases where some data still had to be approximated. But in the long run the methodology could be supported by more reliable data.
7. How (if at all) are health disparities or other equity issues addressed in the article or report?
Three scenarios were developed in the article: Business As Usual (BAU), Transportation Policy Scenario (TPS), and Enhanced Policy & Urban Optimization scenario (EPUOS).
Figure 1 shows the comparison of the CO2 emission per capita among different city categories. There are significant differences among city categories. For the BAU scenario, the trend from 2007 to 2030 shows the CO2 emission per capita in City Categories A and B will increase to as high as 0.95 tons/year. CO2 emission per capita from towns (City Category F) in 2030 is 0.56 tons/year, which is the lowest among all city categories, but would be even higher than the level in 2007 in City Categories A. If TPS could be materialized, the per capita emissions still increase for every city category, but the upper limit would be 0.73 tons/year. The emissions per capita in City Categories B and C would catch up with that in City Category A. If EPUOS were realized, the emission per capita in City Category A would decrease to 0.45 ton/year in 2030. The per capita CO2 for Category B would start to decrease from 2022, while the other cities’ would still be on the rise before 2030. To summarize, the TPS will lead to a reduction of per capita emissions by 17–34% in 2030, while reduction from the EPUOS would be 38–53%, depending on city category.
8. Where has this article or report been referenced or discussed? (In some journals, you can see this in a sidebar.)
9. Can you learn anything from the article or report’s bibliography that tells us something about how the article or report was produced?
From the bibliography we can find out that this article is based on the government annual reports on transportation aspect and the research papers of how the transportation policies influence the air quality. It is developed by analyzing the situation of urban development pattern, vehicle population, and people’s travel mode. Then research on what are the fuel saving and CO2 reduction potential in each different policy scenarios.
10. What three points, details or references from the text did you follow up on to advance your understanding of how air pollution science has been produced and used in governance and education in different settings?
The fuel consumption and carbon emissions from China urban transportation sector would continuously increase over the next two decades, with the solid demand for urbanization. Cities should also choose the development pattern based on the carbon reduction target.