Using big data to evaluate the impacts of transportation infrastructure investment: the case of subway systems in Beijing, China

Publication Details

3ie Funded Evaluation, DPW1.1106. A link to the completed study will appear here when available.

Shanjun Li, Yanyan Liu, Jun Yang
China, People's Republic of
East Asia and Pacific (includes South East Asia)
Environment and Disaster Management
Urban Transport
Gender analysis
Equity Focus
None specified
Evaluation design
Difference-in Difference (DID), Propensity Score Matching (PSM), Regression Discontinuity Design (RDD)
Ongoing 3ie Funded Studies
3ie Funding Window
Development Priorities Window 1


The study will measure the impacts of rapid subway expansion in Beijing in addressing traffic congestion and air pollution.


Rapid urbanization brings improvement in the standard of living and opportunities for economic growth along with huge environmental and societal challenges. Once a city of bicycle riders at the turn of the century, Beijing is now ranked among, the most congested and polluted cities in the world. During 2001-2015, its population grew from 13.8 million to nearly 22 million while the number of vehicles increased from 1.1 million to nearly 6 million. The growing urban population and unprecedented increase in vehicle ownership has led to severe traffic congestion and air pollution in virtually all major urban areas in China, a common challenge faced by other emerging economies.

To address these challenges, central and local governments in China are undertaking huge investment in transportation infrastructure such as subway systems. China’s twelfth national five‐year plan (2011‐2015) outlined 69 new subway lines to be constructed with a total length of 2,100 kilometres and spending of RMB 800 billion (US$ 130 billion). Despite the huge investment in subway infrastructure in Beijing and other major cities in China, rigorous evaluation of the social and economic impacts of subway expansion is lacking. This study will provide a rigorous evaluation as well as a cost-benefit analysis of the socio-economic impacts of rapid subway expansion in Beijing on traffic congestion and air pollution.  

Research questions

  1. What are the effects of subway expansion on:
    1. Traffic congestion
    2. Local air quality
    3. Housing prices
  2. Do the benefits from the subway expansion investments justify their costs?


Intervention design

The intervention being evaluated is the rapid subway expansion in Beijing. Before 2000, Beijing had only two subway lines. Two new lines were opened from 2002 to 2006 and new lines were opened every year since 2007. From a global perspective, Beijing’s rapid development of mass transit since 2007 is unprecedented. From 2007 to 2014, the total investment in transportation facilities amounted to over 350 billion yuan (about US$ 56 billion). During this period, fourteen new subway lines and one airport expressway were constructed with a total length of 440 kilometres. The rapid subway expansion programme is still ongoing in Beijing: another twelve subway lines are under construction and scheduled to open before the end of 2020 with a total length of nearly 378 kilometres. Similar large scale and rapid expansion of subway systems are taking place in other major cities throughout China.

Theory of change

It is hypothesised that subway expansion reduces traffic congestion and improves air quality. However, the long run effects of subway expansion on traffic congestion relief and air quality improvement are lower than the effects in the short run. The same effects are larger in the areas closer to the new subway lines than the effects in the areas farther away from the new subway lines. It is also expected that subway expansion will affect property values especially for the neighbourhoods close to the new subway stations, because the nearby residents will benefit from easier subway access, relieved traffic congestion, and improved air quality.

Evaluation design

The quantitative analysis in this study will take advantage of both spatially and temporally rich big data on traffic congestion, air pollution, and housing transactions. A sharp time‐series regression discontinuity (RD) and distance‐based difference‐in‐difference (DID) with matching methods will be used to examine how the rapid expansion of the subway system affects traffic congestion, air quality, and housing prices.

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