The Effect of Delhi Metro on Air Pollution in Delhi Deepti Goel Delhi School of Economics and Sonam Gupta University of Florida February 22, 2013, ISI Delhi

Research Question Has the Delhi Metro (DM) resulted in an improvement in air quality in the city?

Motivation  Adverse health effects of air pollution  Block et al. (2012); Damage to central nervous system, cardiovascular disease, asthma  Currie & Walker (2011); Moretti and Neidell (2011);  High levels of pollution in Delhi  Several criteria pollutants exceed national standards  Net effect of public transport ambiguous  Traffic Diversion Effect  Traffic Creation Effect  Power Plants (in case of Delhi Metro (DM))

Main Contributions  First study to econometrically analyze the effect of the Delhi Metro (DM) on air quality in Delhi  We identify the effect of several extensions of the DM rail network  We determine the cumulative effect of DM over a fifteen month period

Summary of Main Results  DM led to statistically significant decline in two main vehicular pollutants, nitrogen dioxide and carbon monoxide  Suggestive of a traffic diversion effect

Rest of the Presentation  Identification Strategy  Graphical Presentation of Identification  Estimation Results  Robustness Checks  Conclusions

Identification Strategy  OLS suffers from upward bias  Regression Discontinuity Design  Outcome variable: Hourly air pollutant measure  Treatment: Operation of Delhi Metro  Assignment Variable: Time  Main Identifying Assumption  In the absence of the metro rail extension, conditional on weather, we would observe a smooth time trend for the pollutant measure (Chen and Whalley, 2012)

Econometric Framework Suppose is pollution in the absence of metro, is pollution in the presence of metro. This leads to the regression, where is when metro was extended

(Rough) Graphical Presentation of Identification Strategy

NO2 at ITO, Blue Line Second Extension

CO at ITO, Blue Line Second Extension

Estimation Data and Regression Results

Data and Study Period  CPCB: Hourly Pollution Data from three different Monitoring Stations in Delhi (ITO, Sirifort, DCE)  Four criterion pollutants: NO2, SO2, CO and O3  IMD: Hourly weather data for Delhi  Temperature, Relative humidity, Rainfall and Wind speed  DMRC: Monthly Ridership Data  Overlapping data on pollution and weather only available for  Pollution data also suffers from missing observations

Map of Delhi and Delhi Metro

Phase Wise Extension of the Delhi Metro  Extensions of the metro network Red Line Extension 2 March 31, 2004 Yellow Line Introduction December 20, 2004 Yellow Line Extension 1 July 3, 2005 Blue Line Introduction December 31, 2005 Blue Line Extension 1 April 1, 2006 Blue Line Extension 2 November 11, 2006

Estimation Equation Observed Pollutant measure in logs 1 for time periods after extension and 0 otherwise Quartic in current and 1 hour lags of humidity, rainfall, temperature, and wind speed ; hour of the day; weekday; and their interactions Third order polynomial in time  Estimate for each extension of DM:

 We also estimate an equation with contiguous extensions included to measure cumulative effect (for CO at ITO between Nov 2004 to Jan 2006) Estimation Equation

Missing Pollution Data  Focus on only those extensions that have at least four weeks of data on each side of the extension, with no more than 20 percent of observations missing

Discontinuities Studied at ITO, Permissible window length in weeks NO2COSO2O3 Yellow Line Introduction 99 Yellow Line Extension Blue Line Introduction 13 Blue Line Extension

Nine week window results: ITO Robust std. errors reported

Nine week window results: Siri Fort Robust std. errors reported

Cumulative Effect for the period Nov 2004 to Jan 2006: CO at ITO CO Yellow Introd; Dec ** std. error6.6 Yellow Ext; Jul *** std. error3.3 Blue Introd; Dec *** std. error6 observations9914 Robust std. errors reported

RD Design Validity  Other Discontinuities  Shorter Window  Artificial Discontinuities Test  Appropriate Polynomial Order  Non parametric or Local Linear Regression

RD Design Validity: Robustness Checks  Other Discontinuities  Construction activity undertaken to build DM  Regulation that might cause discontinuous change  Manipulation of choice of extension date

Five week window results: ITO Robust std. errors reported

Five week window results: Siri Fort Robust std. errors reported

Main Results  Nitrogen Dioxide  Decrease for both stations across all extensions  For ITO, 24 and 29 percent across extensions  For Siri Fort, 37 and 40 percent across extensions  Carbon Monoxide  Decrease for both stations across all extensions  For ITO, 26 and 69 percent across extensions  For ITO cumulative effect of 15, then 33 and then 32 percent for three consecutive extensions between Nov 2004 and Jan 2006  For Siri Fort, 22 percent

Main Results  Sulphur Dioxide  For ITO, increase (90 percent)  For Siri Fort, decrease (35 to 89 percent across extensions)  Ozone  For ITO, sign flips across extensions for 9 week window (increase across extensions for 5 week window)  For Siri Fort, increase for 9 week window (insignificant for 5 week window)

Work Ahead  Artificial discontinuities Test  Smoothness tests for residuals after controlling for weather  Different orders of time polynomial (AIC criteria)  Non parametric Estimation with optimal bandwidth  Better Understand SO2 and O3 results  Obtain additional data  Vehicular registrations to support traffic diversion effect  Power generation by coal based power plants within Delhi