Transportation Research Part D 23 (2013) 20–24

Contents lists available at SciVerse ScienceDirect

Transportation Research Part D

journal homepage: www.elsevier .com/ locate / t rd

Urban transportation in Chinese cities: An efficiency assessment

1361-9209/$ - see front matter � 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.trd.2013.03.011

⇑ Corresponding author. Tel.: +86 551 63606562.E-mail addresses: weijc@ustc.edu.cn (J. Wei), xiawang1989@163.com (W. Xia).

Jiuchang Wei a, Wang Xia a,⇑, Xiumei Guo b, Dora Marinova b

a School of Management, University of Science and Technology of China, Hefei, Anhui Province, Chinab Curtin University Sustainability Policy Institute, Curtin University, Fremantle, Australia

a r t i c l e i n f o

Keywords:Urban transportationSustainable citiesChinese citiesEfficient transport

a b s t r a c t

We use 2008 data for 34 Chinese cities to compare urban transportation systems. Theresults show stronger eastern and central cities focusing more on high capacity and lesson sustainable modes of transportation, while western cities do the opposite. Chinese citieswith more sustainable transportation are also more likely to have lower gross domesticproduct per capita, be smaller, are less urbanized and have higher bus usage. This modelneeds to change to align with China’s new policy priorities.

� 2013 Elsevier Ltd. All rights reserved.

1. Introduction

China is becoming a car-dependent society. In 2011, car ownership reached nearly 106 million vehicles, including morethan 78 million private cars. In the past, to cope with this, the approach of transportation planning was on increasing infra-structure capacity. The 12th Five-Year Plan Guideline for Transportation, however, puts more emphasis on resource-savingand environmental friendliness with an integration of economic, environmental and social priorities; the building of a dual-goal society (Jia et al., 2013).

This paper analyzes transportation in Chinese cities from the point of view of the dual-goal society. We explore these is-sues by evaluating the sustainability of urban transport in 34 large and medium cities in China’s eastern, central and westernprovinces.

2. Methodology and data

We evaluate the Chinese urban transportation using a super-efficiency data envelopment analysis (DEA) (Andersen andPetersen, 1993) to rank the relative efficiency of its decision-making units (DMUs). A set of 34 cities (the full list of cities isshown in Tables 1 and 2), which are assumed to be DMUs, is examined and consists of 25 provincial capitals, four centrallyadministered municipalities (Beijing, Chongqing, Shanghai and Tianjin) and five additional major cities (Dalian, Ningbo,Qingdao, Shenzhen and Xiamen). Cities in Taiwan, Hong Kong, Macau, Tibet and Ningxia are excluded because data arenot available. The latest data, for 2008, is from the Urban Construction Statistical Yearbook (National Bureau of Statistics ofChina, 2009a), China Statistical Yearbook (National Bureau of Statistics of China, 2009b) and Urban Statistical Yearbook(Ministry of Housing and Urban–Rural Development of China, 2009).

The input and output indicators for the DMUs are set up to be used in data envelopment analysis while taking into con-sideration the dual-goal society. We calculate two types of efficiency values: efficiency value that indicates the sustainabilitylevel of urban transportation, and efficiency value that represents the capability of urban transportation. Sustainability and

Table 1Urban transport sustainability efficiency (SE1) and ranking (R1) for selected Chinese cities, 2008.

R1 City SE1 R1 City SE1

1 Xining 1.8994 18 Taiyuan 0.38352 Hohhot 1.1847 19 Dalian 0.37903 Guiyang 1.1152 20 Shenzhen 0.37634 Lanzhou 1.0927 21 Harbin 0.31055 Haikou 1.0098 22 Jinan 0.30876 Zhengzhou 0.8297 23 Qingdao 0.29567 Changsha 0.6568 24 Hangzhou 0.27348 Fuzhou 0.6303 25 Chengdu 0.26299 Changchun 0.6203 26 Nanjing 0.2346

10 Ningbo 0.5808 27 Xi’an 0.217611 Hefei 0.5799 28 Shenyang 0.178812 Xiamen 0.5469 29 Wuhan 0.173113 Kunming 0.5310 30 Chongqing 0.161214 Nanchang 0.4666 31 Tianjin 0.157015 Shijiazhuang 0.4567 32 Guangzhou 0.100616 Nanning 0.4404 33 Beijing 0.089917 Urumqi 0.4073 34 Shanghai 0.0881

Table 2Urban transport capacity efficiency (SE2) and ranking (R2) for selected Chinese cities, 2008.

R2 City SE2 R2 City SE2

1 Hohhot 2.2638 18 Nanjing 0.73962 Haikou 1.8298 19 Guangzhou 0.70843 Ningbo 1.5975 20 Tianjin 0.69014 Zhengzhou 1.4470 21 Jinan 0.65965 Guiyang 1.3617 22 Shijiazhuang 0.64426 Shanghai 1.1788 23 Nanning 0.62507 Dalian 1.1462 24 Hangzhou 0.61338 Urumqi 1.1130 25 Lanzhou 0.61239 Qingdao 1.0558 26 Changchun 0.5631

10 Shenzhen 0.9918 27 Kunming 0.562911 Changsha 0.9175 28 Xining 0.543412 Chongqing 0.9051 29 Shenyang 0.521313 Fuzhou 0.9039 30 Harbin 0.516714 Taiyuan 0.8871 31 Nanchang 0.452715 Chengdu 0.8090 32 Xiamen 0.420416 Wuhan 0.7833 33 Xi’an 0.385417 Hefei 0.7822 34 Beijing 0.3802

J. Wei et al. / Transportation Research Part D 23 (2013) 20–24 21

capability represent different aspects of the urban transport system, therefore two categories of index frameworks are setup; they have the same input but different output indicators.

According to the resource-saving aspect of China’s dual-goal society, a sustainable transportation development modeshould emphasize efficient use of natural resources. Resource use is similarly a major aspect of capacity building in trans-portation. Hence required resources for urban transport are inputs for both index frameworks and DEA models. Input indi-cators are selected from the areas of land use, transport facilities, fixed investment and human resources.

� Land use. China’s population growth and rapid urbanization are increasing the demands for land in cities. Urban transportin particular requires roads and parking spaces. As a more convenient transportation tends to encourage people to travelmore, a growing portion of urban land is being dedicated to transport facilities. Land area occupied by road- and railways(in ten thousand square meters) is used to represent the urban land use of transportation.� Transport facilities. Existing transport facilities are the main resources of the urban transportation system. The two indi-

cators chosen from the 16 listed by Jeon and Amekudzi (2005) are length of roads in kilometers and the number of motorvehicles.� Fixed investment. In addition to fixed infrastructure and existing facilities, investment from local government on an annual

basis is essential to continuously support and promote the development of urban transport. Investment in public trans-port (in ten thousand Chinese Yuan) is adopted to reflect this input indicator.� Human resources. Every industry needs human resources and transportation is not an exception. Employment in transport

is used to represent investment in human capital.

22 J. Wei et al. / Transportation Research Part D 23 (2013) 20–24

There are two sets of output indicators, one applicable for the sustainability efficiency value and the other for the capacityefficiency value.

� Emissions of air pollutants. At present, the main source of urban air pollutants in China is automobile exhaust (Ministry ofEnvironmental Protection of People’s Republic of China, 2010). Based on the evidence of its harmful effects on humanhealth and agriculture, we adopt annual daily average NO2 (in milligram per cubic meter) as the traffic air pollution index.� Noise level. It is widely accepted that a high noise emission level in daytime may be detrimental to people (European Eco-

nomic Area, 2001). We include the noise level using the equivalent sound level of road traffic noise (in decibels).

From a mainstream development perspective, urban transportation is a major contributor to the economy. The traffic out-puts used here cover transportation capacity and economic development.

� Transportation capacity. It is understandable that the main task of urban transport is transiting passengers and freights.The indicators ‘‘passenger traffic’’ (in million people) and ‘‘cargo traffic’’ (in ten thousand tonnes) are used to assess trans-port’s ability.� Economic development. It is difficult to separate economic development in the cities from transportation. It fuels economic

growth by allowing accessibility to resources and markets. The indicator used to represent the importance of urban trafficfor the economy is GDP generated by transport sector. Due to the lack of suitable disaggregate data, we use ‘‘transport,storage and postal industry GDP’’ (in one hundred million CNY) instead of ‘‘transport sector GDP’’.

3. Results

Using the two programming models, two efficiency values – SE1 (sustainability efficiency value) and SE2 (capacity effi-ciency value) – are computed. The DEA results regarding efficiency values and ranking, are presented in Tables 1 and 2. Asthe estimation method requires all data to be non-negative, negative environmental output indicators – the annual dailyaverage NO2 and equivalent sound level of road traffic noise – are adjusted before computation.

The results show significant differences in transport efficiency among the cities. This is not unexpected as China’s coast(including the cities of Beijing, Tianjin, Shijiazhuang, Shenyang, Dalian, Shanghai, Nanjing, Hangzhou, Ningbo, Fuzhou, Xia-men, Jinan, Qingdao, Guangzhou, Shenzhen and Haikou) and central areas (including Hohhot, Changchun, Harbin, Taiyuan,Hefei, Nanchang, Zhengzhou, Wuhan, Changsha and Nanning) are more economically developed than western parts of thecountry (Xining, Guiyang, Lanzhou, Urumqi, Xi’an and Chongqing). In terms of SE1, Xining has the highest value and Shang-hai the lowest, while the highest SE2 value is for Hohhot and the lowest for Beijing. The country’s capital Beijing appears tonot only have the worst transport efficiencies ranking at the bottom for sustainability but is also second last for capacity effi-ciency. At the other end of the spectrum, Hohhot is the best performing city in terms of transport sustainability and secondbest in capacity efficiency.

As regional governments manage transportation in Chinese cities, the policies and measures affecting urban transportvary substantially. This leads to inconsistencies and dissimilarities in the input of resources as well as in the choice of travelmodes. It also partly explains the considerable differences in transport efficiencies across cities.

Notwithstanding the individual discrepancies, the SE1 average values for the eastern, central and western cities indicate anegative link between economic development and sustainability transport efficiency – the more economically developedareas are likely to have less environmental efficient urban transport. If China’s central and eastern regions were to achievesustainability transport efficiency similar to that of the west, they would need to reduce their current environmental impactsby 15% and 35%.

On the contrary, the SE2 average values for the eastern, central and western cities indicate that the west has less transportcapacity efficiency than the central and eastern regions that may stem from the diverse speeds and degrees of urbanization.Because city size and urban population are expanding quickly in eastern and central China, the demand for urban transportin these cities is also increasing. Western cities on the other hand are not as prosperous and are still at the early stage of theurbanization process. Hence the economic function of transport in them is more prominent than in cities in the west.

The SE2 efficiency values are higher than the SE1s for the majority of Chinese cities; the only exceptions are Lanzhou,Changchun, Xining and Xiamen. In other words, as far as transport is concerned Chinese cities are better at capacity buildingthan at attaining sustainability efficiency. This is in line with the environmental Kuznets curve (EKC) that suggests that a lot ofresources are required in the take-off stage of economic development and, if the rate of resource use exceeds the speed of theirregeneration, this can trigger environmental degradation (Grossman and Krueger, 1991). Only when a particular level ofwealth is achieved, does the environment become a consideration in optimizing economic structures. China is still on the ini-tial development path that causes environmental deterioration and improved transportation in its cities comes at the cost ofthe urban environment. The eastern region shows greater disparity between SE2 and SE1, with job opportunities in these citiesattracting a lot of immigrants from central and western parts of the country that is putting strains on urban transportation.1

1 There are an estimated 173 million floating workers (National Bureau of Statistics of China, 2009a) who do not live permanently in the cities but workthere.

Fig. 1. Urban transport efficiency in selected Chinese cities, 2008.

J. Wei et al. / Transportation Research Part D 23 (2013) 20–24 23

SE-DEA is used to evaluate the relative efficiency of the cities’ DMUs, their regional governments. We use the reverserankings of SE1 (R10) and SE2 (R20) (Fig. 1) Cities with R20 exceeding R10 have more developed economies and are relativelylarger. On the other hand, cities with R20 lower than R10 are mostly small or medium-sized. Finally, there are cities whoseboth ranks are very similar with their DMUs paying equal attention to transport sustainability and capacity efficiency. Hoh-hot and Beijing are two prominent examples of this but while Hohhot, with a population of around two million, is placed atthe one end of the scale, Beijing with a population of around 22 million is at the other.

4. Factor analysis

There are many factors affecting the sustainable transport efficiency, including urban transport policy, travel mode,urbanization level, economic development level, and urban scale, and we use the Tobit regression to examine the relation-ship between these factors and SE1 values.

An advanced public transport is often mooted at a way of improving the environmental efficiency of urban transportation.In China, because buses are the dominant mode of urban public transport we use the number of buses per million people(vehicles) to represent government investment in public transport. It is however unclear whether high public transportationcan reduce environmental damages, hence the correlation between it and the SE1 value is indeterminate.

We use bus trips per million people to reflect the acceptance of public transport as a mode of transportation. We expect apositive correlation between this indicator and SE1. The scale effect of EKC theory implies that a fast developing economywould have a negative effect on the environment in its early stages; hence we use per capita GDP to indicate China’s eco-nomic development.

According to Zhang and Ma (2010), modern cities are characterized by large disparities in the level of urbanization; largecities grow rapidly while the number of small ones declines. The increasing level of urbanization and the expanding city sizestimulates potential requirements for developing urban transport. To meet these demands, China’s urban transport is devel-oping radically with cities building highways and viaducts. More and more cities are also implementing rail transport. Chi-na’s urban transport, however, is focused on convenience; although environmental issues may be mentioned, they are notseen as targets. High levels of urbanization and large urban scale may lead to negative effects on SE1. We use the percentageof urban metropolitan residents in city population2 to reflect the urbanization level, and population to represent urban size.

Because the range of the efficiency value of the super-efficiency DEA is (0, 1), the dependent variables in a regressionequation are limited. Thus the Tobit regression is used for estimation (Table 3). Model II is constructed to conduct a robust-ness test, in which we set the variable a10 to represent the public transport investment of each city, and set a regional dum-my variable a30 to measure the economic level of development. If a city has rail transport, the value of its a10 is unity,otherwise it is 0. If a city is in the east of China, a30 is one, otherwise it is zero. Tests of model II show that the roles of vari-ables do not change and are as significant as in model I, suggesting model I’s results are robust.

Table 3 shows that most explanatory variables are significant except a1 or a10, and that the correlation is consistent withthe expectation. The significant negative relationship between the level of economic development and efficiency value SE1indicates that China’s urban transport is in its infancy according to the EKC theory.

The results of the analysis also demonstrate that the level of urbanization and city size are significantly and negativelycorrelated with SE1, which may be caused by a lack of attention towards environmental emissions during urbanization. Thisis consistent with PM2.5 and ozone pollution increasing and causing frequent decreasing visibility and haze in Beijing, Tian-

2 Chinese cities still include large rural areas.

Table 3Tobit model results.

Explanatory variables Indicators Model I coefficient Model II coefficienta1 or a10 Bus vehicles per million people 0.0069 0.0067a2 Bus trips per million people 0.0003** 0.0004*

a3 or a30 GDP per capita �8.51e�06*** �0.2057**

a4 Proportion of urban metropolitan residents in city population �0.0072 �0.0010*

a5 City population �0.0004* �0.0004**

* 1% sig.** 5% sig.*** 10% sig.

24 J. Wei et al. / Transportation Research Part D 23 (2013) 20–24

jin, Hebei and other economically developed regions. The Ministry of Environmental Protection of China has issued the newversion of ambient air quality standards to include PM2.5 and ozone concentrations into routine air quality assessment(Ministry of Environmental Protection of People’s Republic of China, 2012). The seriousness of the environmental emissionproblem associated with urban traffic has attracted government’s attention but much more needs to be achieved.

Furthermore, the number of bus trips is positively correlated with SE1, which confirms that an environment-friendlychoice for people, such as traveling by public transport, can indeed reduce environmental emissions associated with traffic,thereby enhancing the urban transport sustainability efficiency value. The fact that the indicators a1 or a10 are not significantfurther illustrates that if the government merely increases transport infrastructure investments, it would still be unable toreduce environmental emissions or improve the urban transport efficiency without encouraging people to change the waythey travel in the city.

5. Conclusions

In our analysis of 34 cities in China, we find big differences between the sustainability efficiency and the capacity effi-ciency values for the Chinese cities indicating strong regional variations. The eastern and central regions show a transportdevelopment mode with low SE1 and high SE2, whereas the opposite is true for the western region of the contrary. Oneexplanation is that investments in urban traffic resources in the western cities, which tend to be smaller, are lower thanin the eastern and western metropolises as their economies lag and there is insufficient demand. Hence the negative effectsof the economy on the environment have not yet manifested in the west. However the eastern and central cities are expe-riencing great changes in urbanization due to major social issues, such as expanding population, demand for public infra-structure construction and vigorous economies which make transport capacity take priority over sustainability. Thisdevelopment model will need to improve in order for China to achieve its current dual-goal society policy priorities.

Acknowledgements

The first and second authors acknowledge the financial support from the National Natural Science Foundation of China(71171183 and 71121061), the Program for New Century Excellent Talents in University (NCET-10-0920), and the largebid project from China’s Social Sciences Fund (Project No. 08&ZD043). The third and fourth authors acknowledge the finan-cial support of the Australian Research Council. All authors are thankful to the Journal’s Editor and its anonymous refereeswho provided useful feedback and helped improve the quality of the manuscript.

References

Andersen, P., Petersen, N.C., 1993. A procedure for ranking efficient units in data envelopment analysis. Management Science 39, 1261–1264.European Economic Area, 2001. Traffic Noise: Exposure and Annoyance. European Environmental Agency, Copenhagen.Grossman, G.M., Krueger, A.B., 1991. Environmental Impacts of a North American Free Trade Agreement. NBER Working Paper, No. 3914, Cambridge, MA.Jeon, C.M., Amekudzi, A.A., 2005. Addressing sustainability in transportation systems: definitions, indicators, and metrics. Journal of Infrastructure Systems

11, 1076-0342.Jia, R., Guo, X., Marinova, D., 2013. The role of the clean development mechanism in achieving China’s goal of a resource efficient and environmentally

friendly society. Environment, Development and Sustainability 15, 133–148.Ministry of Environmental Protection of People’s Republic of China, 2010. China Motor Vehicle Pollution Prevention Annual Report. Chinese Ministry of

Environmental Protection, Beijing.Ministry of Environmental Protection of People’s Republic of China, 2012. Ambient Air Quality Standards. Chinese Ministry of Environmental Protection,

Beijing.Ministry of Housing and Urban–Rural Development of China, 2009. Urban Construction Statistics Yearbook. China Planning Press, Beijing.National Bureau of Statistics of China, 2009a. Urban Statistical Yearbook. China Statistics Press, Beijing.National Bureau of Statistics of China, 2009b. China Statistical Yearbook. <http://www.stats.gov.cn/english/statisticaldata/yearlydata/> (accessed 10.04.12).Zhang, W.C., Ma, Q.Y., 2010. Urban Transport and Urban Development. The Commercial Press, Beijing.