[Effect of Carbon Dioxide Emission Reduction Policy on Air Quality Improvement in Jiangsu Province]

Carbon emission peaking and air quality improvement is an urgent issue in the research of the atmospheric environment. Here, the emission factor method was used to compile the city-level greenhouse gas emission inventory of Jiangsu Province from 2010 to 2019, which was then combined with greenhouse gas-air pollutant synergy analysis and WRF-Chem air quality model simulation to analyze the synergistic gain of air quality improvement under different carbon emission reduction scenarios. The results revealed that the annual mean CO₂ emission in Jiangsu Province from 2010 to 2019 was 701.74-897.47 Mt. Suzhou, Xuzhou, and Nanjing had the highest emissions (91.19-182.12 Mt·a^-1); Yangzhou, Suqian, and Lianyungang had the lowest emissions (13.19-32.54 Mt·a^-1); and majority of the cities had a continuous upward trend in the CO₂ emissions. Energy activities were the main source of CO₂ emissions, accounting for nearly 90%, whereas industrial production processes contributed to the remaining 10%. This study designed three types of CO₂ emission reduction conditions according to different emission reduction priorities, namely, sector-wide collaborative, energy priority, and industrial priority. Each type of emission reduction condition included a different intensity of CO₂ emission reduction (10%, 20%, and 40%). The condition-based simulation results demonstrated that, taking 2017 as the base year, the average annual decrease in PM_2.5 concentration in sector-wide collaborative, energy priority, and industrial priority emission reduction was 6.7-21.1, 3.1-12.0, and 3.4-14.3 μg·m^-3, respectively. Sector-wide collaborative emission reduction had the most notable improvement in PM_2.5 pollution. Under the condition of the sector-wide collaborative emission reduction of 40%, the average annual PM_2.5 concentration of all cities, excluding Xuzhou and Suqian, met the national Ⅱ standard (35 μg·m^-3). The change responses of PM₁₀, SO₂, NO₂, and CO were similar to that of PM_2.5, but O₃ pollution increased under the conditions of energy and industrial priorities.