Yang Xie

Economic Impacts from PM2.5 Pollution-Related Health Effects in China: A Provincial-Level Analysis

This study evaluates the PM2.5 pollution-related health impacts on the national and provincial economy of China using a computable general equilibrium (CGE) model and the latest nonlinear exposure-response functions. Results show that the health and economic impacts may be substantial in provinces with a high PM2.5 concentration. In the WoPol scenario without PM2.5 pollution control policy, we estimate that China experiences a 2.00% GDP loss and 25.2 billion USD in health expenditure from PM2.5 pollution in 2030. In contrast, with control policy in the WPol scenario, a control investment of 101.8 billion USD (0.79% of GDP) and a gain of 1.17% of Chinas GDP from improving PM2.5 pollution are projected. At the provincial level, GDP loss in 2030 in the WoPol scenario is high in Tianjin (3.08%), Shanghai (2.98%), Henan (2.32%), Beijing (2.75%), and Hebei (2.60%) and the top five provinces with the highest additional health expenditure are Henan, Sichuan, Shandong, Hebei, and Jiangsu. Controlling PM2.5 pollution could bring positive benefits in two-thirds of provinces. Tianjin, Shanghai, Beijing, Henan, Jiangsu, and Hebei experience most benefits from PM2.5 pollution control as a result of a higher PM2.5 pollution and dense population distribution. Conversely, the control investment is higher than GDP gain in some underdeveloped provinces, such as Ningxia, Guizhou, Shanxi, Gansu, and Yunnan.

Economic Impacts from PM2.5 Pollution-Related Health Effects: A Case Study in Shanghai

PM2.5 pollution-related diseases cause additional medical expenses and work time loss, leading to macroeconomic impact in high PM2.5 concentration areas. Previous economic impact assessments of air pollution focused on benefits from environmental regulations while ignoring climate policies. In this study, we examine the health and economic impacts from PM2.5 pollution under various air pollution control strategies and climate policies scenarios in the megacity of Shanghai. The estimation adopts an integrated model combining a Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS) model, exposure-response functions (ERFs), and a computable general equilibrium (CGE) model. The results show that without control measures, Shanghais mortality caused by PM2.5 pollution are estimated to be 192 400 cases in 2030 and the work time loss to be 72.1 h/cap annually. The corresponding GDP values and welfare losses would be approximately 2.26% and 3.14%, respectively. With an estimated control cost of 0.76% of local GDP, Shanghai would gain approximately 1.01% of local GDP through local air pollution control measures and climate policies. Furthermore, the application of multiregional integrated control strategies in neighboring provinces would be the most effective in reducing PM2.5 concentration in Shanghai, leading to only 0.34% of GDP loss. At the sectoral level, labor-intensive sectors suffer more output loss from PM2.5 pollution. Sectors with the highest control costs include power generation, iron and steel, and transport. The results indicate that the combination of multiregional integrated air pollution control strategies and climate policies would be cost-beneficial for Shanghai.

Economic impacts from PM2.5 pollution-related health effects in China's road transport sector: A provincial-level analysis

Economic impact assessments of air pollution-related health effects from a sectoral perspective in China is still deficient. This study evaluates the PM2.5 pollution-related health impacts of the road transport sector on Chinas economy at both national and provincial levels in 2030 under various air mitigation technologies scenarios. Health impacts are estimated using an integrated approach that combines the Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS) model, a computable general equilibrium (CGE) model and a health model. Results show that at a national level, the road transport sector leads to 163.64 thousand deaths per year, increases the per capita risk of morbidity by 0.37% and accounts for 1.43 billion Yuan in health care expenditures. We estimate 442.90 billion Yuan of the value of statistical life loss and 2.09 h/capita of work time loss in 2015. Without additional control measures, air pollution related to the transport sector will cause 177.50 thousand deaths in 2030, a 0.40% per capita increase in the risk of morbidity, accounting for 4.12 billion Yuan in health care expenditures, 737.15 billion Yuan of statistical life loss and 2.23 h/capita of work time loss. Based on our model, implementing the most strict control strategy scenario would decrease mortality by 42.14%, morbidity risk by 42.14%, health care expenditures by 41.94%, statistical life loss by 26.22% and hours of work time loss by 42.65%, comparing with the no control measure scenario. In addition, PM2.5 pollution from the road transport sector will cause 0.68% GDP loss in 2030. At a provincial level, GDP losses in 14 out of 30 provinces far exceed the national rate. Henan (1.20%), Sichuan (1.07%), Chongqing (0.99%), Hubei (0.94%), and Shandong (0.90%) would experience the highest GDP loss in 2030. Implementing control strategies to reduce PM2.5 pollution in the road transport sector could bring positive benefits in half of the Chinese provinces especially in provinces that suffer greater health impacts from the road transport sector (such as Henan and Sichuan).