Transportation & Urban Environment

MEP meeting

High-Level Meetings with Ministers Xie and Li

August 30, 2017

On August 4, China’s lead official on climate change, Minister XIE Zhenhua, hosted a research and policy consultation with Profs. Mike McELROY, Steve WOFSY, executive director Chris NIELSEN, and Project alumni Dr. ZHANG Hongjun (Holland & Knight, LLP) and Prof. LU Xi (Tsinghua University) at his offices in Beijing. Discussion topics included the state of U.S.-China engagement on climate and the growing role of subnational governments, disparate regional capacities for carbon control within China... Read more about High-Level Meetings with Ministers Xie and Li

Nan Zhong, Jing Cao, and Yuzhu Wang. 2017. “Traffic congestion, ambient air pollution and health: Evidence from driving restrictions in Beijing.” Journal of the Association of Environmental and Resource Economists, 4, 3, Pp. 821–856. Publisher's VersionAbstract

Vehicles have recently overtaken coal to become the largest source of air pollution in urban China. Research on mobile sources of pollution has foundered due both to inaccessibility of Chinese data on health outcomes and strong identifying assumptions. To address these, we collect daily ambulance call data from the Beijing Emergency Medical Center and combine them with an idiosyncratic feature of a driving restriction policy in Beijing that references the last digit of vehicles’ license plate numbers. Because the number 4 is considered unlucky by many in China, it tends to be avoided on license plates. As a result, days on which the policy restricts license plates ending in 4 unintentionally allow more vehicles in Beijing. Leveraging this variation, we find that traffic congestion is indeed 22% higher on days banning 4 and that 24-hour average concentration of NO2 is 12% higher. Correspondingly, these short term increases in pollution increase ambulance calls by 12% and 3% for fever and heart related symptoms, while no effects are found for injuries. These findings suggest that traffic congestion has substantial health externalities in China but that they are also responsive to policy. 

Sumeeta Srinivasan. 2008. “A visual exploration of the accessibility of low income women: Chengdu, China and Chennai, India.” In Gendered Mobilities, edited by Tanu Priya Uteng and Tim Cresswell. Hampshire, UK: Ashgate Publishing. Publisher's VersionAbstract
Being socially and geographically mobile is generally seen as one of the central aspects of women's wellbeing. Alongside health, education and political participation, mobility is indispensable in order for women to reach goals such as agency and freedom. Building on new philosophical underpinnings of 'mobility', whereby society is seen to be framed by the convergence of various mobilities, this volume focuses on the intersection of mobility, social justice and gender. The authors reflect on five highly interdependent mobilities that form and reform social life.
Jieping Li, Joan L Walker, Sumeeta Srinivasan, and William P Anderson. 2010. “Modeling private car ownership in China: Investigating the impact of urban form across mega-cities.” Transportation Research Record , 2193, Pp. 76-84. Publisher's VersionAbstract
The rising prevalence of private cars in the developing world is causing serious congestion and pollution. In China, private cars started to emerge as an important travel mode in the past decade. Prospective research on the relationship between urban form and car ownership is relatively uncommon in the developing world, and China offers a unique study opportunity, given the tremendous increases in private cars and fast-paced urbanization over the past decade. This study investigates the influence of urban form on car ownership as well as the impact of other socioeconomic and demographic factors on private car ownership across megacities in China. Analysis was conducted through the use of data from 36 megacities and two household survey data sets collected in Beijing and the city of Chengdu, China. Ordinary least squares regression and discrete choice models were employed to execute the aggregate and disaggregate analysis of the urban form impact on private car ownership across cities. The statistical model results demonstrate that urban affluence, urban scale, and road infrastructure supply factors have significant positive effects on the city level of private car ownership across cities. Population density calculated at the subdistrict level, however, had a significant negative effect on private car ownership across cities. Households with private cars were found to prefer to live close to urban centers where amenities were readily available. The results provide evidence for urban planners and policy makers.

This paper uses data from the Project's household survey in Chengdu, Sichuan.

Sumeeta Srinivasan. 2005. “Linking land use and transportation in a rapidly urbanizing context: A study in Delhi, India.” Transportation, 32, 1, Pp. 87-104. Publisher's VersionAbstract
Cities in developing countries like India are facing some of the same concerns that North American cities are: congestion and urban growth. However, there is a sense of urgency in cities like Delhi, India in that this growth is far more rapid as both urbanization and motorization are ongoing processes that have not yet peaked. In this paper, we examine land use change and its relationship with transportation infrastructure and other planning related variables in a spatial context. We estimate land use change models at two different scales from separate data. Cellular automation and Markov models were used to understand change at the regional scale and discrete choice models to predict change at the local level. The results suggest that land use in the Delhi metropolitan area is rapidly intensifying while losing variety. These changes are affected by industrial, commercial and infrastructure location and planners and policy-makers need to better understand the implications of location decisions. We also examine these results in the context of a policy framework for data-based planning that links land use and transportation models for Delhi.
Joan L Walker, Jieping Li, Sumeeta Srinivasan, and Denis Bolduc. 2010. “Travel demand models in the developing world: Correcting for measurement errors.” Transportation Letters, 2, 4, Pp. 231-243. Publisher's VersionAbstract
While transport modelers in developed countries are accustomed to working with relatively rich datasets including transport networks and land use data, such databases are rarely available in developing countries. However, developing countries such as China with its immense rate of economic growth are, arguably, most in need of demand models. The research addressed in this paper is how to develop mode choice models for planning and policy analysis when high quality level of service data are not available. The research makes use of a 1,001 household travel and activity survey from Chengdu collected by the China Project at Harvard University in 2005. Chengdu has an urban population of over 3 million and a GDP growth rate of over 20% per year. The survey contains a rich array of self-assessed information on available modes and accessibility and also includes a number of attitudinal questions. The approach taken here is to treat level of service as a latent (i.e., unobservable) variable. Measurement equations (from the structural equation model paradigm) are used to infer latent level of service, and these equations are integrated with the mode choice model. Our initial results indicate that models that do not correct for measurement error may significantly underestimate travelers' values of time. The methodological approach employed has potential for improving models estimated with higher quality network data, because it can correct for measurement error that exists, for example, in network-derived level of service variables.

This paper is based on data from the Project's household survey in Chengdu, Sichuan.

Yu Deng, Shenghe Liu, Jianming Cai, Xi Lu, and Chris P Nielsen. 2015. “Spatial pattern and evolution of Chinese provincial population: Methods and empirical study.” Journal of Geographical Sciences, 25, 12, Pp. 1507-1520. Publisher's VersionAbstract

China has been experiencing an unprecedented urbanization process. In 2011, China’s urban population reached 691 million with an urbanization rate of 51.27%. Urbanization level is expected to increase to 70% in China in 2030, reflecting the projection that nearly 300 million people would migrate from rural areas to urban areas over this period. At the same time, the total fertility rate of China’s population is declining due to the combined effect of economic growth, environmental carrying capacity, and modern social consciousness. The Chinese government has loosened its “one-child policy” gradually by allowing childbearing couples to have the second child as long as either of them is from a one-child family. In such rapidly developing country, the natural growth and spatial migration will consistently reshape spatial pattern of population. An accurate prediction of the future spatial pattern of population and its evolution trend are critical to key policy-making processes and spatial planning in China including urbanization, land use development, ecological conservation and environmental protection. In this paper, a top-down method is developed to project the spatial distribution of China’s future population with considerations of both natural population growth at provincial level and the provincial migration from 2010 to 2050. Building on this, the spatial pattern and evolution trend of Chinese provincial population are analyzed. The results suggested that the overall spatial pattern of Chinese population will be unlikely changed in next four decades, with the east area having the highest population density and followed by central area, northeast and west area. Four provinces in the east, Shanghai, Beijing, Tianjin and Jiangsu, will remain the top in terms of population density in China, and Xinjiang, Qinghai and Tibet will continue to have the lowest density of population. We introduced an index system to classify the Chinese provinces into three categories in terms of provincial population densities: Fast Changing Populated Region (FCPR), Low Changing Populated Region (LCPR) and Inactive Populated Region (IPR). In the FCPR, China’s population is projected to continue to concentrate in net immigration leading type (NILT) area where receives nearly 99% of new accumulated floating population. Population densities of Shanghai, Beijing, Zhejiang will peak in 2030, while the population density in Guangdong will keep increasing until 2035. Net emigration leading type (NELT) area will account for 75% of emigration population, including Henan, Anhui, Chongqing and Hubei. Natural growth will play a dominant role in natural growth leading type area, such as Liaoning and Shandong, because there will be few emigration population. Due to the large amount of moving-out labors and gradually declining fertility rates, population density of the LCPR region exhibits a downward trend, except for Fujian and Hainan. The majority of the western provinces will be likely to remain relatively low population density, with an average value of no more than 100 persons per km2.

Rui Wang. 2009. “The structure of Chinese urban land prices: Estimates from benchmark land price data.” Journal of Real Estate Finance and Economics, 39, 1, Pp. 24-38. Publisher's VersionAbstract
Taking the recent benchmark land prices published by the Chinese city governments, the paper estimates commercial and residential land price curves of Chinese cities using cross-sectional data, controlling for urban population size and income level. The urban land leasing price–distance relationship is estimated based on the argument that monocentric urban structure is representative for Chinese cities. Both population size and income level are found to positively affect urban land price and price–distance gradients. Commercial land prices are higher than residential land prices except in suburbs or outer central urban areas, where the land prices of different uses converge. In most situations, commercial use price gradients are larger than those of residential use.

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