Synergistic assessment of air pollution and carbon emissions from the economic perspective in China.

The synergistic evaluation integrating air quality, human health, climate impact, and socioeconomic development is significant for green and low-carbon transition. Here, we quantified the contribution of pollutant emissions in 30 provinces (source) to PM concentration and related premature mortality in each 20 km grid (receptor) of China in 2020 by an integrated model for the first time. Further, we established a cross-province contribution matrix of health impact intensity (HII, PM-related deaths per GDP). According to HII and CEI (carbon emission intensity, defined as CO emission per GDP) levels, 30 provinces were divided into 4 regions including LL, HL, LH and HH. In order to assess the synergy in air pollution and carbon emission, we established an index system consisting of ISEC-AC (index for synergistic assessment) and its two sub index: IHI (index for HII assessment), and ICE (index for CEI assessment). Results showed that the ISEC-AC was more easily influenced by IHI as the variance of IHI was much higher than that of ICE. Influenced by various factors, e.g., economic structure, population density, pollution transport, ISEC-AC exhibited substantial spatial heterogeneity. In general, the ISEC-AC of southeast provinces was higher than that of central and western, indicating the environmental and climate impact per GDP was relatively lower in southeast China. For provinces, ISEC-AC of SH and GD were ~ 16 times higher than NX. For regions, due to low carbon emission intensity and health impact intensity, ISEC-AC of LL was the highest with 176; followed by HL (128), LH (126) and HH (77). Further, we figured out the main control problems and then put forward targeted synergetic control suggestions for air pollution and carbon emission from the perspective of energy structure, industry structure and industry layout, which can provide insights into future green and low-carbon policy making in China and other countries.