Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix
How Does Straw Burning Affect Urban Air Qualityin China?
Shiqi (Steven) GuoThe 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
Healthmortality rate in US (Chay and Greenstone, 2003), Indonesia (Jayachandran, 2009),
China (Tanaka, 2015; He et al., 2016), India (Greenstone and Hanna, 2015), SouthKorean (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 performanceagricultural 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 marketlabor supply in Mexico (Hanna and Oliva, 2015)
Consumptionair purifiers in China (Ito and Zhang, 2016)
particulate-filtering masks in China (Zhang and Mu, 2017)
Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix
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 effectEmission 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 etal., 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 ⇒ overestimateMount 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 Effectstemporal effectdensity effectspillover effect
3 Heterogeneous Effectsmain pollutantspollution levels
4 Robustness Checksamplesmodelsrandomly 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 2016Satellites 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
Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix
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 DescriptionAQI 68.35 59.17 39.69 5 500 Air Quality IndexPM2.5 44.38 35.4 37.47 2 1793 Fine particles ≤ 2.5µm in diameter in µg/m3PM10 79.49 64 73.31 3 8775 in µg/m3SO2 21.13 15.5 20.77 1 739.2 in µg/m3CO 1 0.88 0.55 0 18.94 in mg/m3NO2 28.71 25.17 16.26 1.8 461 in µg/m3O3 107.4 101 47.02 2.25 863 in µg/m3Fire 0.1 0 1.5 0 169 Number of straw burning fire pointsFired 0.02 0 0.15 0 1 Straw burning dummyHtemp 22.44 25 9.63 -27 43 Maximum temperature in degrees CelsiusLtemp 13.09 15 10.16 -40 31 Minimum temperature in degrees CelsiusSmog 0 0 0.06 0 1 Smoggy day dummyRain 0.39 0 0.49 0 1 Rainy day dummySun 0.31 0 0.46 0 1 Sunny day dummyCloud 0.5 1 0.5 0 1 Cloudy day dummyOvercast 0.16 0 0.37 0 1 Overcast day dummyWind 0.38 0 0.49 0 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 followingdays?
Density effect
number of fire points in the city-date grids
Spillover effect
How does straw burning affect urban AQI of the surroundingcities?
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 = 0.2889AQI 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
aτFire2i,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 QuadraticAverage 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 -0.0001Firet−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 -0.003Firet−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 -0.002s.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 YesWeather Yes Yes YesObservations 126,106 126,106 126,106R-squared 0.3449 0.3465 0.3460Number 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 within200 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 0 7 18 25
Firedt -0.22 -1.14*** 0.63** -0.10Firedt−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** 0.05Firedt−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 -0.32Firedt−9 1.33** -0.26 -0.03 -0.51*Firedt−10 2.35*** -0.51 0.22 -0.07s.e. (0.64, 1.00) (0.37, 0.43) (0.28, 0.37) (0.24, 0.32)
City FE, date FE, weather YesObs = 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 4.5NO2 0.4 0.3 0.3 0.2SO2 0.04 0.2 0.04 0CO 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)
Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix
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-burncities
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 overChina
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 citiesCities 284 284 284 142 367Days 798 386 335 538 538
Firedt 0.28 -1.28 -0.42 -1.29 2.20Firedt−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 YCity, Day FE Y Y Y Y YObservations 200,233 40,118 84,996 64,748 153,397R-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 ,tSuni ,tCloudi ,tWindi ,tFirei ,tAQIi ,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 twodays after burning. The effect decreases gradually andremains significant for eleven days.
Each fire point increase urban AQI by 0.9 on the first dayafter burning. The effect is larger with denser burning. Themarginal 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 withdifferent sub-samples and models.
Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix
Thank you!
Email: [email protected]
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, SouthCities 46 56 129Average AQI 70.1 103.4 67.3Average Fire 0.3 0.06 0.01Straw burning Dummy Number Dummy Number Dummy Number
Firet 1.17 0.24** -1.63 0.4 2.41** 0.64Firet−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** 0.34Firet−4 1.91 -0.08 3.93*** 0.6*** 0.81 -0.13Firet−5 1.51 0.11 4.41*** 0.78*** 0.84 -0.06Firet−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 -0.37Firet−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 YCity, day FE Y Y Y Y Y YObservations 32,267 32,267 40,482 40,482 91,389 91,389R-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-FebAverage AQI 81.8 60.3 79.9 109.9Average 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*** -1.16Firedt−3 1.17 2.4*** 8.45*** 10.01***Firedt−4 0.61 4.93*** 6.12*** -2.14Firedt−5 1.65* 6.62*** 3.95*** -5.12Firedt−6 -0.23 4.26*** 5.89*** -5.73Firedt−7 -0.29 2.66*** 10.31*** -3.2Firedt−8 0.86 4.02*** 5.77*** 1.06Firedt−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 YCity, Day FE Y Y Y YObservations 51,497 50,523 52,567 45,788R-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 -0.89l10fire 2.1*** 1.22s.e. (0.58,0.96) (0.68,1.08)Weather Y YCity, Month FE Y YCubic Trend Y YObservations 199,345 198,690R-squared 0.5024 -
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All Impulse Responses
Impulse Responses
Introduction Data Main Effects Heterogeneous Effects Robustness Check Conclusion Appendix
Satellites Data Availability
Data
