Urban Transportation, Land Use, Air Quality, and Health

An interdisciplinary household survey in the city of Chengdu, led by Chris NIELSEN (Harvard-China Project), James HAMMITT (Harvard T.H. Chan School of Public Health), and SHEN Mingming and YAN Jie (School of Government, Peking University), was completed in July 2016. The data have been joined with an earlier dataset collected in a nearly identical Harvard-China Project-PKU survey in 2005, to yield a time series covering changes in Chengdu's economy, urbanization, travel behavior, land use, air quality, and environmental health over 11 years. In addition to this multi-faceted research on Chengdu, the Project conducts additional individual studies of transportation, land use, emissions, air quality, and/or health at urban scale. 

Click on "More Publications" below for a full list of publications supported by the Harvard-China Project in this research area. 
 
Both the 2005 and 2016 Chengdu surveys collected data across a wide range of research interests, including transportation planning, travel behavior, mobile-source emissions, outdoor and indoor air pollution exposures, health effects of air pollution, and perceptions and valuation of health risk. Harvard researchers devised the questionnaires and are leading a number of parallel analyses. Covering 2000 households, field implementation of both surveys was led by political scientist Shen, using RCCC's rigorous geospatial sampling methods to insure inclusion of non-registered migrants left out of most social surveys in China.  
 
Joining Hammitt and Nielsen in ongoing analyses of the datasets are China Project research affiliates GUAN ChengHe (Graduate School of Design, Harvard), Sumeeta SRINIVASAN (Department of Urban and Environmental Policy and Planning, Tufts University), WANG Haikun (School of Environment, Nanjing University), and GENG Fangli (Harvard T.H. Chan School of Public Health).

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Rong Xie, Clive E. Sabel, Xi Lu, Weimo Zhu, Haidong Kan, Chris P. Nielsen, and Haikun Wang. 2016. “Long-term trend and spatial pattern of PM2.5-induced premature mortality in China.” Environment International, 97, Pp. 180-186. Publisher's VersionAbstract

With rapid economic growth, China has witnessed increasingly frequent and severe haze and smog episodes over the past decade, posing serious health impacts to the Chinese population, especially those in densely populated city clusters. Quantification of the spatial and temporal variation of health impacts attributable to ambient fine particulate matter (PM2.5) has important implications for China's policies on air pollution control. In this study, we evaluated the spatial distribution of premature deaths in China between 2000 and 2010 attributable to ambient PM2.5 in accord with the Global Burden of Disease based on a high resolution population density map of China, satellite retrieved PM2.5 concentrations, and provincial health data. Our results suggest that China's anthropogenic ambient PM2.5 led to 1,255,400 premature deaths in 2010, 42% higher than the level in 2000. Besides increased PM2.5 concentration, rapid urbanization has attracted large population migration into the more developed eastern coastal urban areas, intensifying the overall health impact. In addition, our analysis implies that health burdens were exacerbated in some developing inner provinces with high population density (e.g. Henan, Anhui, Sichuan) because of the relocation of more polluting and resource-intensive industries into these regions. In order to avoid such national level environmental inequities, China's regulations on PM2.5 should not be loosened in inner provinces. Furthermore policies should create incentive mechanisms that can promote transfer of advanced production and emissions control technologies from the coastal regions to the interior regions.

Yu Deng and Sumeeta Srinivasan. 2016. “Urban land use change and regional access: A case study in Beijing, China.” Habitat International, 51, February, Pp. 103-113. Publisher's VersionAbstract

In the recent past Beijing has experienced rapid development. This growth has been accompanied by many problems including traffic congestion and air pollution. Understanding what stimulates urban growth is important for sustainable development in the coming years. In this paper, we first estimate a binary auto-logistic model of land use change, using physical and socioeconomic characteristics of the location and its access to major centers within the city as predictors. We find that variables determining regional access, like time distance to the city center, the Central Business District (CBD), industrial centers, employment centers, and the transportation system, significantly impact urban land conversion. By using measures of access to predict land use change we believe that we can better understand the planning implications of urban growth not only in Beijing but other rapidly developing cities.

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.

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