Zhengzhou Living Lab

Impact the city’s future sustainable development.

TIMELINE

Nov. 2018

Zhengzhou City Living Lab launched at MIT China New City Forum

MIT News

 Summer 2019

Zhengzhou City Living Lab Surveys

Winter 2020

RCT 

Air pollution and commuting behavior

  

Summer 2020

RCT

Driving route externalities 

UPCOMING EVENT

Shanghai Workshop

Shanghai

Juan

1

Jan

Ongoing Projects

 
  • Whether people choose to switch commuting modes to protect themselves under air pollution scenario?

  • Whether people are rational in making tradeoffs between the health benefit and cost?

  • What are the determinants of the choices?

1.

Health Perception and Commuting Choice

Yichun Fan, Juan Palacios, Siqi Zheng

(a) Job locations of survey participants; (b) 2019 daily average PM2.5 pollution (micrograms/m3) in Zhengzhou

(a) Commuting modes composition; (b) Home-job walking distance

Survey designs

The survey was conducted in July, 2019. Our major target group is non-vehicle commuters whose job location is around Zhengzhou CBD area, with a total 2285 valid participants.

Questionnaires are designed and collected through Qualtrics. Survey takes 15-20 minutes, including an opening video introducing the iPad interface of electronic questionnaire, informed consent, four rounds of commuting choices questions, two information intervention, and some socio-demographics habits and preferences characterization questions. All respondents are asked to make four rounds of commuting choices. Each round of choices is bundled with three questions: their primary mode of commuting, whether they are willing to switch to active commuting (i.e., biking or walking) given a reasonable amount of subsidy, and what is the minimum amount of subsidy they are willing to accept for the switch.

Survey structure and group decompositions

Results

1. Air pollution and commuting choice

After we present with people their personal pollution exposure information, we see a large reduction in respondents choosing public transit and a large increase in motor vehicle (i.e., car/ taxi). Assuming information is the only thing updated their believes, the result indicates a knowledge gap between people’s perceived pollution exposure and the reality, specifically, people seem to  underestimate the exposure in public transit.

Descriptive analysis of commuting choice for Treatment Group 1 under different pollution scenarios

2. Health trade-offs in active commuting

Though biking within 30 minutes has exercise benefit outweighs pollution cost, people who live close to the job location also intentionally switch from active commuting to other transportation modes to hedge against their subjective perception of exposure risk.

3. Implications for active commuting policies

People with counterfactual biking time 28-60 minutes value the health cost of pollution exposure the most. The differences for extensive and intensive margin can be partially explained by the selection bias, since people who adopts active commuting for biking time longer than 30 minutes usually have greater preferences for physical activities or having limited alternative transportation choice. 

Active commuting reduction after informed pollution exposure by counterfactual biking time.

(a) Changes in willingness to go active given subsidy; (b) Changes in minimum acceptable subsidy

Conclusion

  • People have the intention of switching commuting modes as a channel of air pollution adaptation

  • People seem to overreact to air pollution

  • Both financial subsidy and green nudge policies to encourage active commuting are likely to be in vein under air pollution

2.

Impact of social externality information on fostering sustainable travel

  • Response to social externality

Can informing car users of the high emissions impact of car travel increase their intention to switch to cleaner modes?

  • Self-protection

Is the response to social externality information countered by self-protection, when drivers learn non-car modes expose them to more air pollution?

Rachel Luo, Yichun Fan, Priyanka deSouza, Binzhe Wang, Xin Yang

Experimental designs

Measured at each stage:

•Intention to drive to work next month, no green subsidy offered

•Intention to drive to work next month, green subsidy offered

•Size of desired subsidy (willingness to accept/WTA)

Personalized information:

  • Travel time

  • Travel cost

  • Distance

  • PM 2.5 exposure by mode, as cigarette equivalent

 

Based on respondent’s home, work address

Difference-in-difference analysis:

Without subsidies, will drivers go green?

  • Social externality treatment in stage 1 reduces people’s intention to commute by car, but becomes less effective given pollution information.

  • Share of drivers intending to commute by car falls for both groups over time (due to common information)

  • Total drop for treatment group is steeper, though its rebound in stage 3 also steeper.

With subsidies, will drivers go green?

  • Subsidy offer has dramatic effect on moving individuals of both groups away from intended car use.

  • Little difference in response between the two groups in subsidy case.

  • Treatment group begins with slightly lower share of people unwilling to switch, but gap is insignificant.

How large must subsidies be, among those willing to switch from driving?

  • Treatment and control group began at roughly similar WTA, and both saw required subsidy drop overall.

  • Treatment group experiences sharper WTA drop, but also steeper rebound in stage 3.

Policy Implications

  • Social externality information interventions can be effective in promoting intention to use green modes, even in polluted areas.

  • However, commuters seek to balance green transport use with need to protect against air pollution. This reduces policy cost effectiveness in polluted places.

  • Subsidies are effective, but do not complement informational interventions. Policy portfolio mix is key to cost effectiveness.

  • Both health and cleaner air are key but conflicting policy goals. Policymakers, with citizens, must decide tradeoff between revealing more pollution information, and the reduction in green transportation the city achieves.

Reporting the levels of multiple traffic related pollutants: PM, SO2 , CO, NO2 in different transport microenvironments in downtown Zhengzhou. ​

In-bus, in-car with the ventilation system turned on, bicycle

3.

Air Quality in different transport modes in Zhengzhou

Priyanka deSouza, Ruiqiang Lu, Pat Kinney, Siqi Zheng

Research Design:

 

Route Selection   →

 

Time of day:

  • Rush Hours: 7 am -9 am, 5 pm- 7pm All days of week - Sunday July 5 - July 26

Overview of results:

- NO2 , CO and  SO2 are primary pollutants and are highest in the bus and taxi, reflecting the closest proximity to the exhaust of other vehicles. PM and O3 are secondary pollutants. 

 

-The highest correlations between pollutants on different transport modes were found between PM2.5 and PM10. The bus and bike PM were more highly correlated than that of taxi and bus, or taxi and bike.

 

-O3 and NO2 were inversely correlated, especially for bike drivers.

 

-SO2 on taxi is strongly inversely correlated with CO measured on bikes, while it is weakly correlated with the CO measured in the bus. The bike and bus CO are weakly inversely related.

The most direct comparisons between travel mode were made by taking the average of individual (pairwise) run ratios between modes, because this limited the effect of temporal variations in meteorology

When comparing the average of ratios from the pairwise analysis to the ratio of the overall mode averages, we found that the rank ordering obtained is the same.

 

In general the pairwise analysis provided greater contrasts, with an extreme increase in the bus/bike NO2 ratio, the SO2 ratios of all three: taxi/bike, bus/bike and taxi/bus

Pros and Cons:

Pros: (1) We measured a range of pollutants, (2) We attempted to do a pair-wise analysis

Cons: (1) Our study was only conducted in one season: summer, and therefore the results cannot be generalized to other seasons, (2) We used low-cost sensors, and there could be issues with calibration

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Future steps:

Calibrate Purple Air data + integrate subway analysis

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