THE SOLVABLE CHALLENGE OF AIR POLLUTION IN INDIA Anant Sudarshan, - - PowerPoint PPT Presentation

the solvable challenge of air pollution in india
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THE SOLVABLE CHALLENGE OF AIR POLLUTION IN INDIA Anant Sudarshan, - - PowerPoint PPT Presentation

THE SOLVABLE CHALLENGE OF AIR POLLUTION IN INDIA Anant Sudarshan, Michael Greenstone (University of Chicago), Santosh Harish (EPIC-India), Rohini Pande (Harvard University) Anant Sudarshan India Policy Forum 2017, July 11 2017 OUTLINE Air


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Anant Sudarshan, Michael Greenstone (University of Chicago), Santosh Harish (EPIC-India), Rohini Pande (Harvard University) Anant Sudarshan

THE SOLVABLE CHALLENGE OF AIR POLLUTION IN INDIA

India Policy Forum 2017, July 11 2017

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OUTLINE

Air Quality Regulation: The Health Rationale Reviewing Command and Control

  • Technology mandates
  • Bans and rationing

Implications

  • Collect data reliably and transparently
  • Incentive-compatible and transparent regulation
  • Rigorously evaluate pilots
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Air Pollution and Health

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STRONG EVIDENCE THAT PM REDUCES HUMAN LIFESPAN

Source: Chen et al (2013)

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660 MILLION PEOPLE MAY LOSE 3.2 YEARS OF LIFE

  • We use data from 456

monitors across 190 cities, combined with ground calibrated satellite estimates from Dey et al (2012) to produce the map on left.

  • Drawing upon research

from across the world and China, we estimate life- expectancy drops by 1.1 to 3.2 life years because

  • f non-compliance with

Satellite and Ground Monitoring Data Source: Greenstone et al (2015)

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THE EVIDENCE SUGGESTS POLLUTION CAN BE REDUCED

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Reviewing Command and Control

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REVIEWING COMMAND-AND-CONTROL

Efficient environmental regulation requires i. High Quality Data ii. Low Costs and Incentive Compatible Design iii. Rigorous Evaluation

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AIR POLLUTION LEVELS VARY SIGNIFICANTLY OVER TIME

100 200 300 400 500 PM2.5 conc (mg/ m3)

Jan round April round

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LIMITED SPATIAL MONITORING…

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…BUT LOTS OF SPATIAL VARIATION

100 200 300 400 PM2.5 conc. (mg/m3)

Jan 2016 April 2016

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TECHNOLOGY DOES NOT ENSURE DATA AVAILABILITY

Eric Dodge and Rohini Pande, January 19 2016. http://www.indiaspend.com/cover-story/to-cut-delhis-air-pollution-pinpoint-the-source- 40763

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MONITORING EMITTERS CAN BE EVEN HARDER

Duflo, E., Greenstone, M., Pande, R., and Ryan, N. (2013). Truth-telling by third-party auditors and the response of polluting firms: Experimental evidence from India. The Quarterly Journal of Economics, 128(4):1499–1545.

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COMMON PROBLEM IN TRANSPORT SECTOR ALSO

  • 79 percent emission testing centers in Mexico City accept

bribes and substitute emissions readings of failing cars (Oliva, 2015)

  • Private centers in California fail vehicles at half the rate at

which government run centers (Wenzel, 1998)

  • CPCB 2013 audit of pollution checking centers around

Delhi:

  • Manpower poorly trained and unaware of testing

protocols

  • Equipment not maintained, and rarely properly calibrated
  • Software used to generate dummy measurements
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CONTINUOUS EMISSIONS MONITORING SYSTEMS MIGHT HELP, BUT AGENT INCENTIVES STILL MATTER

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CASE STUDY: DELHI’S ODD-EVEN PROGRAM

  • Delhi government ran two rounds of the car-rationing

program

  • January 1-15, 2016
  • April 15- 30, 2016
  • Ban on driving a car with an odd numbered license plate on
  • dd dates, and an even numbered plate on even dates
  • Ban in effect between 8am and 8pm with important

exceptions (two-wheelers, female drivers, taxis)

  • Fines for non-compliance: Were these enforced?
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DELHI TIME SERIES YIELDED MIXED REVIEWS…

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CHANGES IN TRENDS WITHIN AND OUTSIDE DELHI

Jan January 19, 19, 2016 2016 May 13, 13, 2016 2016

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DID THE ODD-EVEN PROGRAM REDUCE EMISSIONS?

  • We compare changes in time trends for monitors within

and outside Delhi before, during and after the pilot

  • Difference-in-Differences method uses outside Delhi

monitor trends as a control for all exogenous factors – meteorology, crop burning, wind direction – that influence the pollution time-series

  • Especially in January, monitors within and outside Delhi

had similar trends before the pilot, diverged during the pilot, and reverted to being similar after the pilot

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DID THE ODD-EVEN PROGRAM REDUCE EMISSIONS?

50 100 150 200 250 300

PM2.5 conc. mg/ m3

Non-Delhi Jan Delhi Jan Non Delhi- April Delhi- April

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IMPACTS CLEAR IN JANUARY, NOT IN APRIL

  • 140
  • 120
  • 100
  • 80
  • 60
  • 40
  • 20

20 40 60 1 2 3 4 5 6 7 8 9 101112131415161718192021222324 HOUR OF DAY January round April round

Change in concentrations during the dates when odd-even was implemented (microgram/m3)

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ONE EXPLANATION: WEATHER WAS THE DIFFERENCE

S.K. Guttikunda, R. Goel, 2013. Health impacts of particulate pollution in a megacity— Delhi, India. Environmental Development 6 (2013) 8–20

  • Dispersion is faster when

temperatures are higher

  • Reduction in emissions may not

translate into reduction in concentrations that are identifiable

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BUT COMPLIANCE MAY HAVE BEEN LOWER…

  • In January, traffic surveys by the School of Planning and

Architecture, travel time queries from Google Maps and self- reported behavior all suggested high compliance (Kreindler 2016 survey of 960 commuters).

  • In April, travel speeds (an indirect proxy related to traffic)

and self-reported behavior suggested compliance.

  • BUT, surveys found traffic volumes were higher during the

second round of the program than the first round, and that there was a large shift to two-wheelers.

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SIMILAR EXPERIENCES IN OTHER PARTS OF THE WORLD

Mexico City (Davis, 2008)

  • Restrictions led to an increased adoption of used cars.
  • Substitution to relatively older vehicles on restricted days

may have led to a net increase in pollution. Beijing (Wang et a 2014)

  • Non-compliance may have been as high as 48 percent
  • Car owners who traveled during peak hours and/or for

work trips, were more likely to break the driving restriction rules.

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THE REGULATOR IS LEFT CHASING A MOVING TARGET

  • Ban on brick kilns in Delhi,

led to movement just

  • utside Delhi border
  • Enforceable regulation, but

no change in polluting behavior and limited impacts

  • High costs imposed on

industrial activity

  • Challenge: Regulate

emissions not technology and minimize costs of compliance

S.K. Guttikunda, R. Goel (2013) “Health impacts of particulate pollution in a megacity—Delhi, India Environmental Development 6 8–20

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TECHNOLOGY MANDATES- EASY TO ENFORCE, BUT DO NOT TRANSLATE TO REDUCTIONS IN EMISSIONS

  • 1. Two emission sources attached

each with a cyclone and bag filter;

  • 2. Two emission sources attached,

each with a cyclone and scrubber;

  • 3. Two emission sources attached,

each with a cyclone, scrubber and bag filter;

  • 4. A single boiler attached with a

cyclone and bag filter;

  • 5. A single boiler attached to a

cyclone and scrubber. Source: CPCB survey of 1000 industries in Gujarat, Tamil Nadu, Maharashtra

500 1,000 1,500 1 2 3 4 5

Source: Baseline Survey

(Gujarat)

Box plot of PM Concentration by Stack Attachments

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NOTWITHSTANDING DESIGN, REGULATION HAMSTRUNG BY INFLEXIBLE PENALTY OPTIONS AVAILABLE TO SPCBS

  • Do not have the

ability to levy penalties commensurate with offence

  • Very strict

penalties can be difficult to enforce, leading to effectively lax regulation

“The Value of Discretion in the Enforcement of Regulation: Experimental Evidence and Structural Estimates from Environmental Inspections in India” (with Esther Duflo, Michael Greenstone and Rohini Pande). NBER Working Paper #20590.

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Reforming Environmental Regulation

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THREE STEPS SEEM ESSENTIAL

1. Collect reliable and transparent data 2. Move towards incentive-compatible and efficient regulation 3. Pilot and evaluate impact of new policies

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Two Important Examples

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WHY EXPERIMENT WITH MARKET-BASED REGULATION?

  • Lowers costs of

compliance

  • Encourages monitoring

transparency and quality

  • Incentivizes performance

beyond an absolute standard

  • Introduces flexible,

financial penalties

Progra m Impact Estimate (cost savings / emissions reductions) Study US SO2 Trading Progra m U.S. $30m Carlson et al. (2000) U.S. $358m per year Ellerman et al. (2000) U.S. $153 - 183m per year Keohane (2006) EU ETS Progra m - EU power sector 88 Mton CO2 Delarue, Voorspools, D’haeseleer (2008) 34 Mton CO2 Delarue, Ellerman, D’haeseleer (2008)

ADAPTED FROM ANTHOFF AND HAHN (2010)

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EMPIRICAL EVIDENCE MOTIVATING ETS PILOT IN SURAT

  • For an emissions cap at the

100 mg/Nm3 level, we estimate a 55 percent reduction in costs for industries trading relative to fixed standards

  • Gupta (2002) estimated that

emissions trading would be 2.69 times cheaper than using fixed standards to reduce TSP levels by 50% across 15 polluting industries in Maharashtra

Without Trading With Trading

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MAHARASHTRA STAR RATING SCHEME

  • Maharashtra is the most industrialized state of India with
  • ver 1,00,000 industries:
  • 12,500 high pollution potential
  • 15,500 medium pollution potential
  • 47,000 low pollution potential
  • MPCB conducts extensive industry stack and ambient air
  • monitoring. More than 20,000 stack samples collected

between September 2012 to January 2017

  • The Star Rating Program aims to decrease air pollution by

increasing transparency and removing information asymmetries

7/13/2017 33

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Visit: http://www.mpcb.gov.in/star_rating/browseRating

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Thank You

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Evaluating using a Difference-in-Differences Method

  • We used regulatory monitors in the National Capital Region, inside and
  • utside Delhi and ambient PM2.5 data from November 2015 to April 2016
  • Ytm = α + β. 1(m є Delhi) + γ.1(t є oddeven)
  • + δ.1(m є Delhi) X 1(t є oddeven) + λm + ηt + εtm
  • Ytm is the particulate (PM2.5) concentration at time t (on hour h and day d) for

monitor m.

  • Explanatory variables include an indicator variable for the treatment area (Delhi), an

indicator variable for the days that the odd-even program was in place (termed

  • ddeven), and their interaction term.
  • β and γ are the coefficients for the treatment area and period indicator variables.
  • The interaction coefficient δ estimates the program impact on particulate

concentration.

  • λm are ηt capture fixed effects at the monitor level and for each hour.
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REGRESSION RESULTS

13% 13% red reduction in Ja January No

  • st

statisti tically ly si significant red reductio ion in n Apr pril il