ABSTRACT
China and India together have more than one third of the world population and are
two emerging economic giants of the developing world now experiencing rapid economic
growth, urbanization, and motorization. The urban transportation sector is a major source of
carbon dioxide (CO2) emissions in China and India. The goal of this study is to analyze the
characteristics and factors of CO2 emissions produced by commuters in Chinese and Indian
cities and thus to identify strategies for reducing transportation CO2 emissions and mitigating
global climate change. Xi’an in China and Bangalore in India were chosen as two case study
cities for their representativeness of major cities in China and India. The trends of CO2emissions produced by major traffic modes (electric motors, buses, and cars) in major citiesof China and India were predicted and analyzed. The spatial distributions of CO2 emissions
produced by commuters in both cities were assessed using spatial analysis module in ArcGIS (Geographic Information System) software. Tobit models were then developed to investigate
the impact factors of the emissions. The study has several findings. Firstly, in both cities, the
increase of vehicle occupancy could reduce commuting CO2 emissions by 20 to 50 % or
conversely, if vehicle occupancy reduces, an increase by 33.33 to 66.67 %. It is estimated that,
with the current increasing speed of CO2 emissions in Xi’an, the total CO2 emissions from
electric motors, buses, and cars in major cities of China and India will be increased from135 × 106 t in 2012 to 961× 106 t in 2030, accounting for 0.37 to 2.67 % of the total global
CO2 emissions of 2013, which is significant for global climate change. Secondly, householdsand individuals in the outer areas of both cities produce higher emissions than those in theinner areas. Thirdly, the lower emissions in Xi’an are due to the higher density and more
compact urban pattern, shorter commuting distances, higher transit shares, and more clean
energy vehicles. The more dispersed and extensive urban sprawl and the prevalence of twowheeler motorbikes (two-wheeler motorbike is abbreviated as Btwo-wheeler^ in the followingsections) fueled by gasoline cause higher emissions in Bangalore. Fourthly, car availability,
higher household income, living outside the 2nd or Outer Ring Road, distance from the bus
stop, and working in the foreign companies in Bangalore are significant and positive factors of
commuting CO2 emissions. Fifthly, B70-20^ and B50-20^ (this means that generally, 20 % of
commuters and households produce 70 % of total emissions in Xi’an and 20 % of commuters
and households produce 50 % of total emissions in Bangalore) emission patterns exist in Xi’an
and Bangalore, respectively. Several strategies have been proposed to reduce urban CO2
emissions produced by commuters and further to mitigate global climate change. Firstly, in
the early stage of fast urbanization, enough monetary and land investment should be ensured to
develop rail transit or rapid bus routes from outer areas to inner areas in the cities to avoid high
dependency on cars, thus to implement the transit-oriented development (TOD), which is the
key for Chinese and Indian cities to mitigate the impact on global climate change caused by
CO2 emissions. Secondly, in Bangalore, it is necessary to improve public transit service and
increase the bus stop coverage combined with car demand controls along the ring roads, in the
outer areas, and in the industry areas where Indian foreign companies and the governments are
located. Thirdly, Indian should put more efforts to provide alternative cleaner transport modes
while China should put more efforts to reduce CO2 emissions from high emitters.
Keywords: Global climatechange .Urbantransportation.CO2 emissionsbycommuters .Spatialdistribution . Impact factor . China and India
Citation : Yuanqing Wang, Liu Yang, Sunsheng Han, Chao Li and Ramachandra T V, 2016. Urban CO2emissions in Xi’an and Bangalore by commuters: implications for controlling urban transportation carbon dioxide emissions in developing countries, Mitig Adapt Strateg Glob Change, 21(113): , DOI 10.1007/s11027-016-9704-1
|