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)

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 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 Data 1 Main Effects 2 temporal effect density effect spillover effect Heterogeneous Effects 3 main pollutants pollution levels Robustness Check 4 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

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 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 . 5 µ m in diameter in µ g / m 3 PM10 79.49 64 73.31 3 8775 in µ g / m 3 SO2 21.13 15.5 20.77 1 739.2 in µ g / m 3 CO 1 0.88 0.55 0 18.94 in mg/m3 NO2 28.71 25.17 16.26 1.8 461 in µ g / m 3 O3 107.4 101 47.02 2.25 863 in µ g / m 3 Fire 0.1 0 1.5 0 169 Number of straw burning fire points Fired 0.02 0 0.15 0 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 0 0.06 0 1 Smoggy day dummy Rain 0.39 0 0.49 0 1 Rainy day dummy Sun 0.31 0 0.46 0 1 Sunny day dummy Cloud 0.5 1 0.5 0 1 Cloudy day dummy Overcast 0.16 0 0.37 0 1 Overcast day dummy Wind 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 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 τ =15 � AQI i , t = b τ Fired i , t − τ + W i , t γ + u i + v t + w i , t τ =0 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

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 τ =10 � AQI i , t = b τ Fire i , t − τ + W i , t γ + u i + v t + w i , t τ =0 Categorical: number of fire points in { 1 } , [2,4], [5,+ ∞ ) τ =10 τ =10 τ =10 � � � AQI i , t = b τ FireD 1 i , t − τ + b τ FireD 2 i , t − τ + b τ FireD 3 i , t − τ τ =0 τ =0 τ =0 + W i , t γ + u i + v t + w i , t Quadratic: linear and quadratic terms τ =10 τ =10 � � a τ Fire 2 AQI i , t = b τ Fire i , t − τ + i , t − τ + W i , t γ + u i + v t + w i , t τ =0 τ =0

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 Fire t 0.28** 0.17 -2.18* -0.83 0.14 -0.0001 Fire t − 1 0.92*** 3.33*** 5.09*** 16.59*** 1.40*** -0.007*** Fire t − 2 0.68*** 3.56*** 5.10*** 13.83*** 1.08*** -0.006** Fire t − 3 0.17*** 3.64*** 4.43*** 3.25** 0.41*** -0.004*** Fire t − 4 -0.02 2.99*** 2.35* 6.81*** 0.47*** -0.008*** Fire t − 5 0.19 3.24*** 2.46* 4.58** 0.52*** -0.006*** Fire t − 6 0.16 1.60* 4.75*** 0.80 0.29 -0.003 Fire t − 7 0.34*** 2.90*** 4.10*** 10.69*** 0.91*** -0.009*** Fire t − 8 0.05 1.87** 4.87*** 5.79*** 0.56*** -0.008*** Fire t − 9 0.002 1.80* -0.36 -0.78 0.14 -0.003** Fire t − 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 τ =10 τ =10 τ =10 � � � AQI i , t = b τ Fired i , t − τ + b τ FiredR 1 i , t − τ + b τ FiredR 2 i , t − τ τ =0 τ =0 τ =0 τ =10 � + b τ FiredR 3 i , t − τ + W i , t γ + u i + v t + w i , t τ =0 Fired i , t : whether exists straw burning in city i on day t FiredR 1 i , t : whether exists straw burning in other cities within 200 km from city i on day t FiredR 2 i , t : 200 km - 400 km FiredR 3 i , 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 Fired t -0.22 -1.14*** 0.63** -0.10 Fired t − 1 4.50*** 1.30*** 1.56*** 1.35*** Fired t − 2 4.48*** 1.10*** 1.65*** 0.69** Fired t − 3 3.60*** 1.18*** 0.62** 0.05 Fired t − 4 2.81*** 1.77*** 0.53* -0.56** Fired t − 5 3.47*** 0.42 -0.54* -1.30*** Fired t − 6 2.93*** 0.11 -0.82*** -0.62** Fired t − 7 3.82*** 1.45*** 0.43 -0.54** Fired t − 8 3.10*** 0.64 0.40 -0.32 Fired t − 9 1.33** -0.26 -0.03 -0.51* Fired t − 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