How Does Straw Burning Affect Urban Air Quality in China? Shiqi - - PowerPoint PPT Presentation

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

How Does Straw Burning Affect Urban Air Quality in China?

Shiqi (Steven) Guo The Graduate Institute of International and Development Studies, Geneva September 2017, UNU-WIDER

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

Effects of Air Pollution

Health mortality rate in US (Chay and Greenstone, 2003), Indonesia (Jayachandran, 2009), China (Tanaka, 2015; He et al., 2016), India (Greenstone and Hanna, 2015), South Korean (Jia and Ku, 2016), Mexico (Arceo et al., 2016), Brazil (Rangel and Vogl, 2017) life expectancy in China (Chen et al., 2013) mental health in China (Zhang et al., 2017) Individual performance agricultural worker productivity in US (Graff Zivin and Neidell, 2013) cognitive performance in Israel (Ebenstein et al., 2016) investment performance in China (Huang et al., 2016) Labor market labor supply in Mexico (Hanna and Oliva, 2015) Consumption air purifiers in China (Ito and Zhang, 2016) particulate-filtering masks in China (Zhang and Mu, 2017)

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Straw Burning in China

fuels, forages, fertilizers changes in rural economy (energy structure, farm mechanization, rural labor) clear the fields in time for the next plantings

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

Straw Burning in China

“The day of burning straw, is the day when you will be in prison.” “7 days detention and 1000 RMB fine for straw burning” “15 days detention and 3000 RMB fine for straw burning” “Banning straw burning is patriotism.”

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

Environmental Literature

Research areas ⇒ causal link, general effect Emission factors (Cao et al.,2008; Huang et al., 2012; Zhang et al.,2016) Co-movement of air pollution and straw burning (Li et al., 2008; Zha et

al., 2013)

Meteorological models (Yamaji et al., 2010; Cheng et al., 2014; Zhong et al., 2017) Microstructure of pollutants (Li et al., 2010) Case studies with severe pollution scenarios ⇒ overestimate Mount Tai, June 2006 (Yamaji et al., 2010); Beijing, 12-30 June 2007 (Li et al., 2010); Shanting, 14-27 June 2010 (Zha et al., 2013); Chengdu, 18-21 May 2012 (Chen and Xie, 2014); Huai River Basin, October 2015 (Zhong et al., 2017)

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

Overview

1

Data

2

Main Effects temporal effect density effect spillover effect

3

Heterogeneous Effects main pollutants pollution levels

4

Robustness Check samples models randomly generated burning

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

Data

Straw Burning Ministry of Environmental Protection (MEP) of China various satellites: 10:30, 13:30, 14:30–16:30 14,528 fire points in 26 October 2014 – 31 December 2016

Satellites Data Availability

Urban Air Quality MEP: 1,496 ground monitoring stations Air Quality Index (AQI), PM2.5, PM10, SO2, NO2, CO, O3 142 cities at first, 284 cities in 2016 Weather tianqi.2345.com maximum temperature, minimum temperature, smog, rain, sun, cloud, overcast, wind Observations: 284 prefectural-level cities × 538 days

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

Straw Burning

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

Air Quality

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Straw Burning And Air Quality over Time

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

Summary Statistics

Variable Mean Median St.d Min Max Description AQI 68.35 59.17 39.69 5 500 Air Quality Index PM2.5 44.38 35.4 37.47 2 1793 Fine particles ≤ 2.5µm in diameter in µg/m3 PM10 79.49 64 73.31 3 8775 in µg/m3 SO2 21.13 15.5 20.77 1 739.2 in µg/m3 CO 1 0.88 0.55 18.94 in mg/m3 NO2 28.71 25.17 16.26 1.8 461 in µg/m3 O3 107.4 101 47.02 2.25 863 in µg/m3 Fire 0.1 1.5 169 Number of straw burning fire points Fired 0.02 0.15 1 Straw burning dummy Htemp 22.44 25 9.63

• 27

43 Maximum temperature in degrees Celsius Ltemp 13.09 15 10.16

• 40

31 Minimum temperature in degrees Celsius Smog 0.06 1 Smoggy day dummy Rain 0.39 0.49 1 Rainy day dummy Sun 0.31 0.46 1 Sunny day dummy Cloud 0.5 1 0.5 1 Cloudy day dummy Overcast 0.16 0.37 1 Overcast day dummy Wind 0.38 0.49 1 Windy day dummy

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

Main Effects

Temporal effect How does straw burning affect urban AQI in the following days? Density effect number of fire points in the city-date grids Spillover effect How does straw burning affect urban AQI of the surrounding cities?

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

Temporal Effect

AQIi,t =

τ=15

• τ=0

bτFiredi,t−τ + Wi,tγ + ui + vt + wi,t Firedi,t: whether there exists straw burning in city i on day t Wi,t: weather covariates ui, vt: city, date fixed effects s.e. clustered at city level

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

Temporal Effect

Obs = 126,106; R-squared = 0.2889 AQI Helsinki: 22

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

Temporal Effect

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

Density Effect

Linear: number of fire points detected in city i on day t

AQIi,t =

τ=10

• τ=0

bτFirei,t−τ + Wi,tγ + ui + vt + wi,t

Categorical: number of fire points in {1}, [2,4], [5,+∞)

AQIi,t =

τ=10

• τ=0

bτFireD1i,t−τ +

τ=10

• τ=0

bτFireD2i,t−τ +

τ=10

• τ=0

bτFireD3i,t−τ +Wi,tγ + ui + vt + wi,t

Quadratic: linear and quadratic terms

AQIi,t =

τ=10

• τ=0

bτFirei,t−τ +

τ=10

• τ=0

aτFire2

i,t−τ + Wi,tγ + ui + vt + wi,t

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

Density Effect

(1) (2) (3) (4) (5) (6) Models Linear Categorical Quadratic Average AQI 68.35 68.35 68.35 1 point 2-4 points ≥ 5 points linear terms quadratic terms Firet 0.28** 0.17

• 2.18*
• 0.83

0.14

• 0.0001

Firet−1 0.92*** 3.33*** 5.09*** 16.59*** 1.40***

• 0.007***

Firet−2 0.68*** 3.56*** 5.10*** 13.83*** 1.08***

• 0.006**

Firet−3 0.17*** 3.64*** 4.43*** 3.25** 0.41***

• 0.004***

Firet−4

• 0.02

2.99*** 2.35* 6.81*** 0.47***

• 0.008***

Firet−5 0.19 3.24*** 2.46* 4.58** 0.52***

• 0.006***

Firet−6 0.16 1.60* 4.75*** 0.80 0.29

• 0.003

Firet−7 0.34*** 2.90*** 4.10*** 10.69*** 0.91***

• 0.009***

Firet−8 0.05 1.87** 4.87*** 5.79*** 0.56***

• 0.008***

Firet−9 0.002 1.80*

• 0.36
• 0.78

0.14

• 0.003**

Firet−10 0.11 2.13** 1.15 4.23* 0.22

• 0.002

s.e. (0.05,0.18) (0.78,1.06) (1.10,1.67) (1.59,2.78) (0.15,0.26) (0.001,0.003) City, date FE Yes Yes Yes Weather Yes Yes Yes Observations 126,106 126,106 126,106 R-squared 0.3449 0.3465 0.3460 Number of cities 284 284 284

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

Spillover Effect

AQIi,t =

τ=10

• τ=0

bτFiredi,t−τ +

τ=10

• τ=0

bτFiredR1i,t−τ +

τ=10

• τ=0

bτFiredR2i,t−τ +

τ=10

• τ=0

bτFiredR3i,t−τ + Wi,tγ + ui + vt + wi,t Firedi,t: whether exists straw burning in city i on day t FiredR1i,t: whether exists straw burning in other cities within 200 km from city i on day t FiredR2i,t: 200 km - 400 km FiredR3i,t: 400 km - 600 km

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

Spillover Effect

(1) (2) (3) (4) Distance 0 km 0-200 km 200-400 km 400-600 km (Helsinki) (Turku) (Stockholm) (Oulu) Number of other cities 7 18 25 Firedt

• 0.22
• 1.14***

0.63**

• 0.10

Firedt−1 4.50*** 1.30*** 1.56*** 1.35*** Firedt−2 4.48*** 1.10*** 1.65*** 0.69** Firedt−3 3.60*** 1.18*** 0.62** 0.05 Firedt−4 2.81*** 1.77*** 0.53*

• 0.56**

Firedt−5 3.47*** 0.42

• 0.54*
• 1.30***

Firedt−6 2.93*** 0.11

• 0.82***
• 0.62**

Firedt−7 3.82*** 1.45*** 0.43

• 0.54**

Firedt−8 3.10*** 0.64 0.40

• 0.32

Firedt−9 1.33**

• 0.26
• 0.03
• 0.51*

Firedt−10 2.35***

• 0.51

0.22

• 0.07

s.e. (0.64, 1.00) (0.37, 0.43) (0.28, 0.37) (0.24, 0.32) City FE, date FE, weather Yes Obs = 126,106; cities = 284; R-squared = 0.3470

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

Heterogeneous Effects

Main pollutants PM2.5, PM10, SO2, CO, NO2, O3 Pollution levels quantile regression Regions Northeast, North, Central and South China Seasons

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

Main Pollutants

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

Main Pollutants

Emission factors (Cao et al., 2008)

Wheat straw Rice straw Corn stover Cotton stalk PM 8.8 6.3 5.3 4.5 NO2 0.4 0.3 0.3 0.2 SO2 0.04 0.2 0.04 CO 58 68 68 106 (in g/kg)

O3 (Yamaji et al., 2010; Zhong et al., 2017) PM10 by 10-15 µg/m3 from rice residue in Eastern Spain (Viana et al, 2008) PM10 and O3 from sugarcane in Brazil (Rangel and Vogl, 2017)

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Pollution Levels

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

Robustness Check

Different samples missing days, no-burn days, year 2016, early cities, no-burn cities Different models dynamic model (Difference GMM) random coefficient model Panel Vector Autoregressive (Panel VAR) model Randomly generated burning same number of straw burning grids in every month, all over China

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

Different Samples

(1) (2) (3) (4) (5) Sample + missing days

• no-burn days

Year 2016 Early cities + no-burn cities Cities 284 284 284 142 367 Days 798 386 335 538 538 Firedt 0.28

• 1.28
• 0.42
• 1.29

2.20 Firedt−1 5.94*** 6.95*** 5.50*** 4.52*** 7.81*** Firedt−2 5.79*** 8.03*** 5.25*** 5.86*** 5.96*** Firedt−3 4.77*** 7.20*** 3.92*** 6.21*** 4.76*** Firedt−4 3.83*** 5.27*** 3.26*** 5.32*** 3.83*** Firedt−5 3.83*** 5.23*** 2.95*** 6.30*** 4.06*** Firedt−6 3.19*** 4.14*** 1.16*** 4.28*** 3.31*** Firedt−7 4.41*** 5.79*** 2.49*** 5.56*** 4.61*** Firedt−8 3.63*** 4.68*** 1.75*** 4.94** 3.76*** Firedt−9 1.27** 0.92***

• 1.74***

2.36*** 1.11*** Firedt−10 2.38*** 3.36*** 1.10 3.83*** 2.95*** s.e. (0.6,1.1) (0.9,1.4) (0.6,1) (0.8,1.5) (0.7,1.1) Weather Y Y Y Y City, Day FE Y Y Y Y Y Observations 200,233 40,118 84,996 64,748 153,397 R-squared 0.35 0.24 0.32 0.35 0.23

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

Panel Vector Autoregressive model

        Raini,t Suni,t Cloudi,t Windi,t Firei,t AQIi,t         =

15

• j=1

        π11j π12j π13j π14j π15j π16j π21j π22j π23j π24j π25j π26j π31j π32j π33j π34j π35j π36j π41j π42j π43j π44j π45j π46j π51j π52j π53j π54j π55j π56j π61j π62j π63j π64j π65j π66j                 Raini,t−j Suni,t−j Cloudi,t−j Windi,t−j Firei,t−j AQIi,t−j         +         u1i u2i u3i u4i u5i u6i         +         v1t v2t v3t v4t v5t v6t         +         w1i,t w2i,t w3i,t w4i,t w5i,t w6i,t        

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

Impulse Responses

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

Impulse Responses

All responses

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

Random Generated Burning

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

Conclusion

Straw burning increases the urban AQI by 6.8 on the first two days after burning. The effect decreases gradually and remains significant for eleven days. Each fire point increase urban AQI by 0.9 on the first day after burning. The effect is larger with denser burning. The marginal effect is decreasing. Cities 400 to 600 km away are also affected. Heterogeneous effects are found with different pollutants, pollution levels, regions and seasons. Effects are robust with different sub-samples and models.

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

Thank you!

Email: shiqi.guo@graduateinstitute.ch Webpage: https://sites.google.com/site/stevenshiqiguo/shiqi-guo

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

Regions

(1) (2) (3) (4) (5) (6) Regions Northeast North Central, South Cities 46 56 129 Average AQI 70.1 103.4 67.3 Average Fire 0.3 0.06 0.01 Straw burning Dummy Number Dummy Number Dummy Number Firet 1.17 0.24**

• 1.63

0.4 2.41** 0.64 Firet−1 7.74*** 0.81***

• 0.22

0.72*** 5.9*** 2.21*** Firet−2 4.95*** 0.47*** 2.59** 0.42*** 4.08*** 1.27** Firet−3 5.54*** 0.07 3.13*** 0.27* 2.43** 0.34 Firet−4 1.91

• 0.08

3.93*** 0.6*** 0.81

• 0.13

Firet−5 1.51 0.11 4.41*** 0.78*** 0.84

• 0.06

Firet−6 2.06

• 0.01

3.42*** 0.04 2.05* 0.67* Firet−7 2.66**

• 0.01

2.92*** 0.48** 1.06 0.61** Firet−8 3.21***

• 0.21**

2.68** 0.42 0.33

• 0.37

Firet−9 1.4 0.01 1.02 0.48***

• 0.22
• 0.64**

Firet−10 2.07** 0.09 2.61** 0.78***

• 1.13
• 0.9***

s.e (1,1.7) (0.06,0.19) (1,1.4) (0.12,0.3) (0.8,1.4) (0.2,0.6) Weather Y Y Y Y Y Y City, day FE Y Y Y Y Y Y Observations 32,267 32,267 40,482 40,482 91,389 91,389 R-squared 0.5042 0.5036 0.5965 0.5963 0.4562 0.4562

Northeast: Heilongjiang, Jilin, Liaoning, Neimenggu; North: Hebei, Henan, Shandong, Shanxi; Central and South: Hubei, Hunan, Sichuan, Chongqing, Yunnan, Jiangsu, Zhejiang, Anhui, Jiangxi, Fujian, Guangdong, Guangxi

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

Seasons

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

Seasons

(1) (2) (3) (4) Months Mar-May Jun-Aug Sep-Nov Dec-Feb Average AQI 81.8 60.3 79.9 109.9 Average Fire 0.09 0.03 0.09 0.003 Firedt

• 0.76

1.88 0.03

• 17.46***

Firedt−1 2.97*** 3.13** 9.54***

• 8.83**

Firedt−2 4.07*** 1.41 9.34***

• 1.16

Firedt−3 1.17 2.4*** 8.45*** 10.01*** Firedt−4 0.61 4.93*** 6.12***

• 2.14

Firedt−5 1.65* 6.62*** 3.95***

• 5.12

Firedt−6

• 0.23

4.26*** 5.89***

• 5.73

Firedt−7

• 0.29

2.66*** 10.31***

• 3.2

Firedt−8 0.86 4.02*** 5.77*** 1.06 Firedt−9

• 2.45***

3.68*** 4.76***

• 8.19*

Firedt−10 0.82 3.8*** 5.01***

• 14.59***

s.e. (0.7,1.1) (0.8,1.3) (1.1,1.6) (3.9,5.2) Weather Y Y Y Y City, Day FE Y Y Y Y Observations 51,497 50,523 52,567 45,788 R-squared 0.2192 0.1883 0.3202 0.2796

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Random Coefficient Model

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

Dynamic Panel Model

(1) (2) Models FE Arellano-Bond L.aqi 0.61*** 0.52*** (0.01) (0.009) L2.aqi

• 0.06***
• 0.12***

(0.006) (0.005) Fire 0.21 1.52* l1fire 6.21*** 6.86*** l2fire 2.57*** 4.18*** l3fire 1.85** 3.91*** l4fire 1.88** 3.47*** l5fire 2.16*** 3.41*** l6fire 0.69 1.6** l7fire 1.73** 2.35*** l8fire 1* 1.29* l9fire

• 0.77
• 0.89

l10fire 2.1*** 1.22 s.e. (0.58,0.96) (0.68,1.08) Weather Y Y City, Month FE Y Y Cubic Trend Y Y Observations 199,345 198,690 R-squared 0.5024

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

All Impulse Responses

Impulse Responses

Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix

Satellites Data Availability

Data