Pollution reduced during Odd-Even rule in Delhi : Harvard University and Chicago University

A Q&A released from Harvard and Chicago university highlights the fact that pollution has been reduced during the Odd-Even rule in Delhi. Delhi Govt has run a trial in the city allowing Odd and Even cars on alternative days for the period of 15 days.

Many people and opposition parties have criticised the rule as it creates hassles for some people. Most of the people who faced difficulty because of this rule was outsiders who were using Delhi as an interim city. While Cheif Minister of Delhi, Arvind Kejriwal has claimed that it has reduced the pollution significantly in the city.

Delhi has been categorised among the most polluted cities of the world by many agencies, which has created the alarming situation for the city.

The Govt has initially started car free day scheme in Delhi, according to which one day there would be no cars in a particular area. Later the govt changes the scheme and turns it into a trial phase of 15 days. However, later on, the objection of court and many opposition parties, govt decided to discontinue the scheme.

According to the report of Harvard University and Chicago University ‘The programme reduced concentrations by about 35-47 µg/ m3 , or an estimated 10-13% on average.A statistically significant additional 10% reduction in emissions is estimated on average between 8am-8pm; air quality levels in the evening after 7pm are relatively unaffected. These results are supported by another set of hourly regression models which show stark reductions in PM concentrations around the noon hours, which then taper off in the late afternoon and evening. ‘

Q&A: Methodology

Pollution levels have increased in January 2016, as compared to end December 2015. How can the program have reduced pollution?

Although absolute levels have increased both within and outside Delhi in the NCR, the levels in Delhi have seen a smaller increase. We argue that this can be attributed to the program, and apply a popular impact evaluation method called differences-indifferences to quantify the reduction. Particulate Matter (PM) has many different sources: like thermal power plants, construction activity, road dust, vehicles, burning of solid fuels for cooking and heating, trash burning.

Particulate Matter concentrations are also affected by weather conditions: low temperatures and low wind speeds typically result in higher concentrations of PM. Ambient concentrations in Delhi are highest during November- January, and this year, there has been an increase from late December to January likely due to weather conditions (low wind speeds, temperature and precipitation).

However, it is likely that this is still lower than what it would have been in the absence of a program restricting one source of pollution, i.e. cars, and the challenge is in separating out the component that has reduced. We have estimated this using a statistical technique called difference-in-differences by comparing ambient monitoring data from within Delhi and outside Delhi in the NCR during November 1, 2015 to January 8, 2016.

The NCR has very similar weather conditions throughout and is equally exposed to external factors affecting pollution, like crop burning in the nearby states. As a result, the program impact can be estimated as the differences in the changes in pollution levels, before and after the program.

The estimate seems too high. There are reports that say vehicles account for only 25% of the air pollution in Delhi of which only a fraction are due to cars—how could reducing half the cars make so much difference?

  • The odd-even program reduces pollution in two key ways
  • Fewer cars on the road—thereby, directly removing some of the polluting sources

Reduced congestion would reduce idling and slow moving traffic across the city, thereby reducing pollution for everybody. This second factor is especially not well understood, and may well have been a major factor here, but this question merits further investigation. In addition, there are several estimates

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