Health damage from excess PM2.5 pollution in China and its cross-provincial distribution

Abstract

In this study, we estimate the cost of excess PM2.5 pollution in China and explore how it differs by province. For the analysis of PM2.5-associated health effects, we develop an integrated assessment tool by extending the China Regional Energy Model (CREM-HE). In essence, CREM-HE is a computable general equilibrium model of the Chinese economy, where each province represents one region. The greatest merit of the model is that it explicitly represents the pollution-health linkage within a larger economic system, and can capture pollution-health effects that appear only years later and are cumulative over time. The model’s capacity for provincial level analysis is another great strength, in that the magnitude of pollution-health effects is a function of varied local conditions, and thus a large fraction of the effects remains local. We incorporate epidemiological evidence from the Chinese literature and surveyed data, given that recent empirical evidence does not support the region-neutrality assumption in epidemiological relationships. Our preliminary results show that PM2.5 pollution has caused substantial costs to the Chinese economy. For each year between 2015 and 2030, China’s gross domestic product (GDP) loss from excess PM2.5 levels is estimated to exceed 5% of the baseline level, compared with the counterfactual case when China meets the World Health Organization guideline level. Cross-regional heterogeneity is also substantial. A large fraction of the PM2.5-caused economic loss occurred in Eastern and Central China, where the nation’s population is heavily concentrated. When China is assumed to achieve its national carbon reduction targets, air-quality cobenefits from the climate policy, when measured as avoided premature deaths associated with chronic exposure to PM2.5, are particularly large in China’s central regions, such as Hubei and Hunan, exceeding populous Eastern provinces, such as Guangdong. Among the key drivers of this result are the central region’s relatively high dependence on coal and high energy intensity.