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

Abstract

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 PM2.5 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 PM2.5 pollution. Under the condition of the sector-wide collaborative emission reduction of 40%, the average annual PM2.5 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 PM2.5, but O3 pollution increased under the conditions of energy and industrial priorities.