2 Findings and Analysis Uneven Pollution Controls across Space The empirical analysis reveals that air pollution in areas within a 3 km radius of monitors is 3.2 percent lower than that in the unmonitored areas after the automation rollout, while pollution levels in monitored and unmonitored areas exhibited similar trends before automation, suggesting that local governments strategically focused their pollution control efforts on areas adjacent to monitors. Meanwhile, the impact of monitor automation on citylevel pollution is negative but small and statistically insignificant. To further explore the spatial pattern of possible pollution relocation inside the city, the study analyzes the impact at different distances from monitors, from 3 km to 150 km. The results show that the effect of automation on pollution reduction decreases monotonically with distance to the monitor, slowly approaching zero at the distance beyond 120 km, suggesting uneven pollution control across space. whether technology-aided monitoring can lead to better enforcement or localized cleanup efforts. Study Methodology In China’s practice, air pollution reduction performance is calculated by averaging the pollution readings across ground monitoring stations. To fill the spatial gaps in the ground monitoring network, the study measures air quality by utilizing satellite-derived PM2.5 concentration in grid cells covering the whole of China from 2008 to 2017. Combined with information about monitoring stations’ locations, and city-level socioeconomic data to control for confounding factors, the study uses a spatial differencein-differences (DiD) design to examine the difference between pollution reduction in monitored areas and unmonitored surrounding areas. Furthermore, to explore variations in responses across cities, the analysis incorporates political-economy factors, including preexisting data manipulation, local officials’ political incentives, and public pressure for cleaner air, by extracting data from multiple sources. The Timeline of Monitoring Station Automation Figure 1 Note: This figure displays the spatial distribution of monitoring stations that were automated in three waves, which took place in 2012, 2013, and 2014, respectively. Effects of Automated Monitoring on Achieving Air Pollution Control Targets: Evidence from China Public Policy BULLETIN
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