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Pollution and Climate Change ALLISON LARR, COLUMBIA MAT THEW NEIDELL, COLUMBIA AND NBER Climate change air quality child well-being Climate change and ozone ◦ Ozone = f(VOC, NOx, temperature, sunlight) ◦ If unabated: higher temperatures higher ozone Climate change mitigation and pollution (co-benefits) ◦ Fossil fuels ◦ CO2 emissions ◦ Criteria pollutants (PM2.5, SO2, NOx) ◦ If abated: less fossil fuels less PM2.5 and ozone ◦ E.g. Clean power plan: reduce SO2 and NOx Air pollution and well being ◦ Early years: birth weight, gestational length, infant mortality ◦ Later years ◦ Respiratory diseases. E.g., asthma ◦ Performance in school, labor market Outline 1. Review studies on climate and air quality ◦ Overview of AQ projections ◦ Studies that project both: ◦ PM2.5 and ozone ◦ BAU and mitigation 2. Review QE studies on PM2.5/ozone and well-being ◦ Pollution not randomly assigned. E.g., sorting ◦ QE studies limit OVB 3. Combine 1 & 2 above to project future changes in well-being ◦ Fraught with heroic assumptions ◦ Need evidence in light of uncertainty 4. Comment on other (developing) nations AQ modeling: 4 steps 1. Carbon emissions: IPCC scenarios (A1, A2, B1, B2) ◦ Fossil fuel reliance ◦ Economic and population growth ◦ Technological progress 2. Climate change projections ◦ General circulation model (GCM) of earth’s climate: temperature, precipitation, etc. ◦ Results on coarse spatial resolution, ignore local conditions. E.g., 100 km2, topography 3. Downscaling ◦ Modify spatial resolution: country, state, city, etc. ◦ Provide local weather 4. AQ models: Community Multi-scale Air Quality (CMAQ) model ◦ Simulates chemical and physical processes involved in atmospheric chemical transport ◦ Combine emissions & weather local pollution Table 1. Ozone and PM2.5 projection model scenarios Projection Emissions Scenario(s) Climate Models and Downscaling Air Quality Models Period Chen et al. 2004 IPCC A2 PCM / MM5 CMAQ / MOZART-2 2045-2054 Hogrefe et al. 2004 IPCC A2 GISS GCM / SMOKE / MM5 CMAQ 2053-2057 Avise et al. 2009 IPCC A2 PCM/MM5 CMAQ 2045-2054 Tao et al. 2007 IPCC A1Fi and B1 PCM / MM5 SAQM 2050 Nolte et al. 2008 IPCC A1B and current emissions GISS GCM / MM5 CMAQ 2045-2055 Tagaris et al. 2007 IPCC A1B and current emissions GISS GCM / MM5 CMAQ 2049-2051 Trail et al. 2014 RCS 4.5 GISS GCM / WRF CMAQ 2048-2052 Penrod et al. 2014 IPCC A1B WRF CMAQ 2030 Abbreviations: PCM - Parallel Climate Model; MM5 - Fifth-Generation Penn State/NCAR Mesoscale Model; GISS GCM - Goddard Institute of Space Studies General Circulation Model; SMOKE - Sparse Matrix Operator Kernel Emissions Model; CMAQ - Community Multi-Scale Air Quality Model; MOZART-2 - Model for OZone and Related chemical Tracers; SAQM - SJVAQS-AUSPEX RegionalModeling Adaptation Project Air Quality Model How does AQ affect children? Health effects ◦ Ozone: lung irritant shortness of breath, lung inflammation, asthma exacerbation ◦ PM2.5: travels though lungs into bloodstream ◦ Respiratory effects ◦ Cardiovascular effects (heart attacks, blood pressure, etc.) Human capital effects ◦ Indirectly via health effects ◦ Direct effects: PM2.5 travels up olfactory nerves to brain ◦ Latent effects via epigenetic changes (FOH) Quasi-experimental approach Major concern: sorting ◦ AQ higher in more productive areas (e.g., cities) wealthier families live in dirtier areas ◦ AQ capitalized into housing prices wealthier families live in cleaner areas Two approaches ◦ Policy event ◦ 1980-82 recession ◦ CAAA as an instrument ◦ Military relocation ◦ Area fixed effects ◦ People sort on average pollution ◦ SR variation within area exogenous Focus on QE ozone and PM (TSP, PM10, PM2.5) studies Author Outcome Pollutant Contemporaneous exposure & infant health Chay and Greenstone (2003a) infant mortality TSP Chay and Greenstone (2003b) infant mortality TSP Sanders & Stoecker (2011) sex ratio TSP Janke et al. (2009) <15 mortality PM10 Knittel et al. (2011) infant mortality PM10 Arceo-Gomez et al. (2012) infant mortality PM10 Contemporaneous exposure & child health Lleras-Muney (2010) hospitalizations ozone Neidell (2009) hospitalizations ozone Beatty and Shimshack (2014) hospitalizations ozone Contemporaneous exposure & human capital Zweig et al. (2009) test scores PM2.5 Lavy et al. (2012) test scores PM2.5 Early exposure & later human capital, productivity Sanders (2012) test scores TSP Isen et al. (2013) earnings TSP Design Magnitude recession CAAA CAAA fixed effects fixed effects fixed effects 1 unit TSPs --> 4-7 more infant deaths per 100,000 births 1 unit TSPs --> 5-8 more infant deaths per 100,000 births 35 unit TSPs --> male births increase by 3.1 percentage points 10 unit PM10 --> 4 fewer deaths per 100,000 children 1 unit PM10 --> 18 fewer infant deaths per 100,000 live births 1 unit PM10 --> 0.24 more infant deaths per 100,000 births relocation .008 ppm ozone --> respiratory hospitalization decrease by 8-23 percent fixed effects 0.01 ppm ozone --> hospital admissions increase by 1.09%, 2.88% with alerts fixed effects .0024 ppm ozone --> respiratory treatment increase by 2.5–3.3 percent fixed effects 10% PM2.5 --> increased math by 0.34% and reading by 0.21% fixed effects 10 unit PM2.5 --> reduces test scores by .46 points (1.9% of SD) recession CAAA 1 SD TSPs --> high school test scores decrease by 6% of SD 10 unit TSPs --> reduce earnings by 1% Projections Focus on Tagaris et al. (2007) ◦ ◦ ◦ ◦ Baseline (2001), BAU (2050), and mitigation (2050) Same model for PM2.5 and ozone Regional projections Smaller unabated ozone projections than others: 1 vs. 5 ppb Projections for: ◦ PM2.5 and infant mortality ◦ PM2.5 and adult earnings ◦ Ozone and hospitalizations Table 2. Ozone and PM2.5 Projections by Region year 2001 2050 2050 BAU West M8hO3 (ppb) 49.75 46.25 49.75 PM2.5 (μg/m3) 4.05 3.65 4.15 Plains M8hO3 (ppb) 48.25 44.25 49 PM2.5 (μg/m3) 6.925 5.425 6.875 Midwest M8hO3 (ppb) 45.25 40.5 45.25 PM2.5 (μg/m3) 11.725 9.025 12.2 Northeast Southeast US M8hO3 PM2.5 M8hO3 PM2.5 M8hO3 PM2.5 year (ppb) (μg/m3) (ppb) (μg/m3) (ppb) (μg/m3) 2001 46.25 9 54 12.3 48.75 8 2050 41.75 6.425 46.25 8.425 44.25 6.125 2050 BAU 46 9.625 55.5 11.975 49.25 8.1 Notes: M8hO3 is the annual average of the daily 8-hour maximum ozone. PM2.5 is the annual average of the daily PM2.5. Infant mortality and PM2.5 projections 1. Number of births by region: NVSS (2012), assume constant 2. δIM/δPM from: ◦ Chay and Greenstone: 5.5 per 100k ◦ Knittel et al.: 18 per 100k 3. Conversion to PM2.5 ◦ TSP to PM2.5: 0.16 CG 34 per 100k ◦ PM10 to PM2.5: 0.54 KMS 33 per 100k 4. δPM2.5/δCC from Tagaris et al. 5. Impact = 1 X 2 X 3 X 4 Table 3A. Infant mortality and contemporaneous PM2.5 West Plains Midwest Northeast Southeast All Births 832,065 635,916 820,761 835,041 798,891 3,922,674 No mitigation Infant death vs. 2001 5,076 28 3,879 -11 5,007 133 5,094 177 4,873 -88 23,928 133 Mitigation No Mitigation Mitigation vs. no mitigation Mitigation vs. no vs. 2001 mitigation vs. 2001 vs. 2001 mitigation -113 -141 0.56% -2.23% -2.79% -324 -314 -0.28% -8.36% -8.08% -753 -886 2.65% -15.05% -17.70% -731 -909 3.48% -14.35% -17.84% -1,053 -964 -1.81% -21.60% -19.79% -2,501 -2,634 0.56% -10.45% -11.01% Adult earnings and PM2.5 projections 1. Per capita income by region (BEA REIS) 2. δearnings/δPM: 1.1% for 10 unit TSP (Isen et al.) 3. TSP to PM2.5: 0.68% for 1 unit PM2.5 4. δPM2.5/δCC from Tagaris et al. 5. Impact = 1 X 2 X 3 X 4 Table 3B. Adult earnings and early childhood exposure to PM2.5 West Plains Midwest Northeast Southeast All Per capita income $44,589 $43,680 $41,548 $52,417 $38,550 $44,455 No mitigation vs. 2001 -$30 $15 -$134 -$222 $85 -$30 Mitigation Mitigation vs. no vs. 2001 mitigation $121 $151 $443 $429 $759 $893 $913 $1,135 $1,011 $926 $564 $594 No Mitigation mitigation Mitigation vs. no vs. 2001 vs. 2001 mitigation -0.1% 0.3% 0.3% 0.0% 1.0% 1.0% -0.3% 1.8% 2.1% -0.4% 1.7% 2.2% 0.2% 2.6% 2.4% -0.1% 1.3% 1.3% Hospitalizations and ozone projections 1. δhosp/δozone from: ◦ Lleras-Muney ◦ 1 SD increase 8-23% decrease; 15.8% preferred ◦ 1 SD = .008 ppm ◦ 1 ppb ozone 1.97% change in hosp. ◦ Beatty & Shimshack ◦ 10% ozone increase 2.5-3.3% increase in hospitalizations; choose lowest ◦ 10% = 2.43 ppb ◦ 1 ppb ozone 1.03% change in hosp. 2. δozone/δCC from Tagaris et al. 3. Impact = 1 X 2 (No good baseline of hospitalizations by region) Table 3C. Respiratory hospitalizations and contemporaneous ozone West Plains Midwest Northeast Southeast All No mitigation vs. 2001 BS LM 0.0% 0.0% 0.8% 1.5% 0.0% 0.0% -0.3% -0.5% 1.5% 3.0% 0.5% 1.0% Mitigation vs. 2001 BS LM -3.6% -6.9% -4.1% -7.9% -4.9% -9.4% -4.6% -8.9% -8.0% -15.3% -4.6% -8.9% Mitigation vs. no mitigation BS LM -3.6% -6.9% -4.9% -9.4% -4.9% -9.4% -4.4% -8.4% -9.5% -18.2% -5.1% -9.9% Other countries Comparable wealth to US: comparable effects LMIC could be different ◦ More rapid development, higher levels of pollution ◦ Past US pollution like current pollution Figure 1. Trends in Air Pollution for US, China, and Mexico China Pittsburgh Mexico Other countries Comparable wealth to US: comparable effects LMIC could be different ◦ More rapid development, higher levels of pollution ◦ Past US pollution like current pollution ◦ Arceo-Gomez et al: dose-response comparable (Mexico vs. US) ◦ Non-linear relationship (log(y)): larger effects ◦ Often equatorial larger temperature increases ◦ If unabated, ozone increases likely bigger ◦ Less likely to mitigate. E.g., Kyoto ◦ No co-benefits Suggest pollution via CC will have larger effects in LMIC countries Conclusion Unabated climate change minimal effects on child well-being (relative to baseline) Mitigating climate change large “co-benefits” (relative to BAU and baseline) ◦ ◦ ◦ ◦ Reduced infant mortality Reduced respiratory illnesses Improved test scores Increased earnings Many unknowns involved in projections ◦ Suggest immediate benefits from mitigation policies ◦ Not for geoengineering (CCS, solar radiation management) ◦ Useful starting point