Delhi’s odd-even policy is over. (Illustration by: C R Sasikumar) Delhi’s odd-even policy is over. (Illustration by: C R Sasikumar)

Written by Michael Greenstone, Santosh Harish, Anant Sudarshan and Rohini Pande

Delhi’s ambitious odd-even pilot experiment to reduce the number of cars on the road, and pollution in the air, has come to an end — at least for now. But the question remains: Was it successful?

Answering this question is challenging. Air pollution data is limited and it comes from many different sources. Pollution also varies with time and weather conditions for reasons that have nothing to do with the odd-even pilot. Thus, simply looking at trends in pollution monitors cannot tell us what we need to know. Reflecting these challenges, different assessments so far have been contradictory, ranging from “complete failure” to “massive success”. In a rigorous new study, however, we conclude that the odd-even pilot did have some impact — reducing hourly particulate air pollution concentrations by 10-13 per cent.

To judge the scheme’s true impact, we compared Delhi’s pollution with the rest of the NCR, which has similar weather but didn’t fall under the ambit of the scheme. We did this between January 1-15, when the scheme was in effect, and in November and December, when it was not in effect.

Watch Video Debating The Success Of Kejriwal’s Odd-Even Formula



The first step was to analyse the effect of the odd-even policy on traffic. Anecdotally, there is a general consensus that there were fewer vehicles on the road during the scheme. We attempted to back this up with some hard data. Analysing real-time vehicle speed data from Uber Delhi revealed that during the odd-even programme, average speeds went up by a statistically significant 5.4 per cent (2.8 standard deviation from normal). This is an especially significant change given that radio taxi drivers are meant to stay within speed limits. If shorter trip times reflect fewer cars on Delhi’s roads, and if vehicle emissions significantly impact Delhi’s air pollution, then we may expect the odd-even pilot to translate into lower pollution. We should keep in mind that lower congestion itself reduces pollution as all vehicles (not just cars) spend less time on the road idling and in slow-moving traffic.

Having said this, the key question is to statistically quantify the level of impact. To do this, we first put together a dataset of hourly air pollution numbers from 23 monitors in Delhi, and three monitors from Faridabad, Gurgaon and Noida, where the odd-even policy was not implemented. This included both government and India Spend monitoring stations. Our results remain similar using only government data.

In December 2015, before the odd-even programme began, daily pollution trends in Delhi and the neighbouring regions were very similar.

This is not surprising considering that they have similar temperatures and weather patterns, and are affected in an equivalent way by crop burning and holiday periods. Starting January 1, while absolute pollution levels increased both inside and outside Delhi (for atmospheric reasons, as noted by other commentators), the increase in fine particle levels in Delhi was significantly less than in the surrounding region. Overall, there was a 10-13 per cent relative decline in Delhi.

It is possible to go one step further in our analysis by tracking pollution changes hour by hour, since the odd-even policy was only in effect from 8 am to 8 pm. The results are striking (see figure). Around 8 am, the gap between Delhi’s pollution and that in neighbouring regions begins to form and steadily increases until mid afternoon. As temperatures begin to fall, and pollution is less likely to disperse, this gap starts to close. We see another small gap emerge between 9-11 pm, which probably reflects the new limits on truck traffic in Delhi, which also came into force on January 1. Soon after midnight, the gap closes, and Delhi and neighbouring areas show similar pollution patterns until 8 am comes around again. When focusing just on the hours that the odd-even policy was in effect, our estimates suggest that particulates pollution declined by 18 per cent due to the pilot. For more details see http://j.mp/odd-even-QA.

While the odd-even policy reduced pollution during its first two weeks in effect, there are reasons to wonder about its ability to reduce pollution over the longer run. A natural concern is that the odd-even policy could easily be gamed or otherwise undermined. Further, Mexico City’s experience with the implementation of a similar policy suggests, it could even make pollution worse by encouraging households to purchase second cars that are old and very polluting.

A more durable effect on pollution might come from a congestion-pricing programme, in which drivers are charged for using the roads at certain places and times. This approach, which has been successful in places like London and Singapore, allows cities to effectively reduce car use at periods of peak congestion and pollution. The Delhi government should pilot the use of congestion charging, and invest any income from the charge in high-quality, high-capacity public transport with zero local emissions — which would again help to reduce demand for driving, congestion, and pollution.

Air pollution is shortening lives in Delhi and too many other places in India and elsewhere. The odd-even scheme has delivered over these two weeks, but may not over the long term. Furthermore, vehicles are only one source of pollution.

There is no shortage of creative ideas and potential pilots, but what is all too often lacking is evidence on which ones work as intended. In one effort to improve matters, the University of Chicago has launched a competition with the Delhi Dialogue Commission to crowdsource ideas for reducing air and water pollution (the Delhi Urban Labs Innovation Challenge). More generally speaking, governments need to accept that we don’t have all the answers to policy problems and adopt a culture of trying out new ideas, testing them carefully, and then deciding which ones to adopt at scale.

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