How Does Straw Burning Affect Urban Air Quality in China?

Transcription

1 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

2 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)

3 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

4 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.

5 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, June 2007 (Li et al., 2010); Shanting, June 2010 (Zha et al., 2013); Chengdu, May 2012 (Chen and Xie, 2014); Huai River Basin, October 2015 (Zhong et al., 2017)

6 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

7 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 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

8 Straw Burning

9 Air Quality

10 Straw Burning And Air Quality over Time

11 Summary Statistics Variable Mean Median St.d Min Max Description AQI Air Quality Index PM Fine particles 2.5µm in diameter in µg/m3 PM in µg/m3 SO in µg/m3 CO in mg/m3 NO in µg/m3 O in µg/m3 Fire Number of straw burning fire points Fired Straw burning dummy Htemp Maximum temperature in degrees Celsius Ltemp Minimum temperature in degrees Celsius Smog Smoggy day dummy Rain Rainy day dummy Sun Sunny day dummy Cloud Cloudy day dummy Overcast Overcast day dummy Wind Windy day dummy

12 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?

13 Temporal Effect AQI i,t = τ=15 τ=0 b τ Fired i,t τ + W i,t γ + u i + v t + w i,t Fired i,t : whether there exists straw burning in city i on day t W i,t : weather covariates u i, v t : city, date fixed effects s.e. clustered at city level

14 Temporal Effect Obs = 126,106; R-squared = AQI Helsinki: 22

15 Temporal Effect

16 Density Effect Linear: number of fire points detected in city i on day t AQI i,t = τ=10 τ=0 b τ Fire i,t τ + W i,t γ + u i + v t + w i,t Categorical: number of fire points in {1}, [2,4], [5,+ ) AQI i,t = τ=10 τ=0 τ=10 b τ FireD1 i,t τ + τ=0 Quadratic: linear and quadratic terms AQI i,t = τ=10 τ=0 τ=10 b τ Fire i,t τ + τ=0 τ=10 b τ FireD2 i,t τ + τ=0 b τ FireD3 i,t τ +W i,t γ + u i + v t + w i,t a τ Fire 2 i,t τ + W i,t γ + u i + v t + w i,t

17 Density Effect (1) (2) (3) (4) (5) (6) Models Linear Categorical Quadratic Average AQI point 2-4 points 5 points linear terms quadratic terms Fire t 0.28** * Fire t *** 3.33*** 5.09*** 16.59*** 1.40*** *** Fire t *** 3.56*** 5.10*** 13.83*** 1.08*** ** Fire t *** 3.64*** 4.43*** 3.25** 0.41*** *** Fire t *** 2.35* 6.81*** 0.47*** *** Fire t *** 2.46* 4.58** 0.52*** *** Fire t * 4.75*** Fire t *** 2.90*** 4.10*** 10.69*** 0.91*** *** Fire t ** 4.87*** 5.79*** 0.56*** *** Fire t * ** Fire t ** * 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 R-squared Number of cities

18 Spillover Effect AQI i,t = τ=10 τ=0 τ=10 b τ Fired i,t τ + τ=0 τ=10 + τ=0 τ=10 b τ FiredR1 i,t τ + τ=0 b τ FiredR2 i,t τ b τ FiredR3 i,t τ + W i,t γ + u i + v t + w i,t Fired i,t : whether exists straw burning in city i on day t FiredR1 i,t : whether exists straw burning in other cities within 200 km from city i on day t FiredR2 i,t : 200 km km FiredR3 i,t : 400 km km

19 Spillover Effect (1) (2) (3) (4) Distance 0 km km km km (Helsinki) (Turku) (Stockholm) (Oulu) Number of other cities Fired t *** 0.63** Fired t *** 1.30*** 1.56*** 1.35*** Fired t *** 1.10*** 1.65*** 0.69** Fired t *** 1.18*** 0.62** 0.05 Fired t *** 1.77*** 0.53* -0.56** Fired t *** * -1.30*** Fired t *** *** -0.62** Fired t *** 1.45*** ** Fired t *** Fired t ** * Fired t *** s.e. (0.64, 1.00) (0.37, 0.43) (0.28, 0.37) (0.24, 0.32) City FE, date FE, weather Obs = 126,106; cities = 284; R-squared = Yes

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

21 Main Pollutants

22 Main Pollutants Emission factors (Cao et al., 2008) Wheat straw Rice straw Corn stover Cotton stalk PM NO SO CO (in g/kg) O 3 (Yamaji et al., 2010; Zhong et al., 2017) PM 10 by µg/m 3 from rice residue in Eastern Spain (Viana et al, 2008) PM 10 and O 3 from sugarcane in Brazil (Rangel and Vogl, 2017)

23 Pollution Levels

24 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

25 Different Samples (1) (2) (3) (4) (5) Sample + missing days - no-burn days Year 2016 Early cities + no-burn cities Cities Days Fired t Fired t *** 6.95*** 5.50*** 4.52*** 7.81*** Fired t *** 8.03*** 5.25*** 5.86*** 5.96*** Fired t *** 7.20*** 3.92*** 6.21*** 4.76*** Fired t *** 5.27*** 3.26*** 5.32*** 3.83*** Fired t *** 5.23*** 2.95*** 6.30*** 4.06*** Fired t *** 4.14*** 1.16*** 4.28*** 3.31*** Fired t *** 5.79*** 2.49*** 5.56*** 4.61*** Fired t *** 4.68*** 1.75*** 4.94** 3.76*** Fired t ** 0.92*** -1.74*** 2.36*** 1.11*** Fired t *** 3.36*** *** 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, ,397 R-squared

26 Panel Vector Autoregressive model Rain i,t π 11j π 12j π 13j π 14j π 15j π 16j Rain i,t j u 1i v 1t w 1i,t Sun i,t 15 π 21j π 22j π 23j π 24j π 25j π 26j Sun i,t j u 2i v 2t w 2i,t Cloud i,t Wind i,t = π 31j π 32j π 33j π 34j π 35j π 36j Cloud i,t j j=1 π 41j π 42j π 43j π 44j π 45j π 46j Wind i,t j + u 3i u 4i + v 3t v 4t + w 3i,t w 4i,t Fire i,t π 51j π 52j π 53j π 54j π 55j π 56j Fire i,t j u 5i v 5t w 5i,t AQI i,t π 61j π 62j π 63j π 64j π 65j π 66j AQI i,t j u 6i v 6t w 6i,t

27 Impulse Responses

28 Impulse Responses All responses

29 Random Generated Burning

30 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.

31 Thank you! Webpage:

32 Regions (1) (2) (3) (4) (5) (6) Regions Northeast North Central, South Cities Average AQI Average Fire Straw burning Dummy Number Dummy Number Dummy Number Fire t ** ** 0.64 Fire t *** 0.81*** *** 5.9*** 2.21*** Fire t *** 0.47*** 2.59** 0.42*** 4.08*** 1.27** Fire t *** *** 0.27* 2.43** 0.34 Fire t *** 0.6*** Fire t *** 0.78*** Fire t *** * 0.67* Fire t ** *** 0.48** ** Fire t *** -0.21** 2.68** Fire t *** ** Fire t ** ** 0.78*** *** 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 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

33 Seasons

34 Seasons (1) (2) (3) (4) Months Mar-May Jun-Aug Sep-Nov Dec-Feb Average AQI Average Fire Fired t *** Fired t *** 3.13** 9.54*** -8.83** Fired t *** *** Fired t *** 8.45*** 10.01*** Fired t *** 6.12*** Fired t * 6.62*** 3.95*** Fired t *** 5.89*** Fired t *** 10.31*** -3.2 Fired t *** 5.77*** 1.06 Fired t *** 3.68*** 4.76*** -8.19* Fired t *** 5.01*** *** 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

35 Random Coefficient Model

36 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 * 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 ** l7fire 1.73** 2.35*** l8fire 1* 1.29* l9fire 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, ,690 R-squared

37 All Impulse Responses Impulse Responses

38 Satellites Data Availability Data