Health and economic benefit of China's greenhouse gas mitigation by 2050

Because of the fast socio-economic development in the past three decades, China is consuming a large amount of fossil fuel. At the same time, China is facing huge pressure in terms of climate change mitigation and air pollution (Dong et al 2015, Peng et al 2018). To address such challenges, China submitted Nationally Determined Contributions (NDCs) to the Paris Agreement to reduce carbon emissions (He 2015). Many studies have suggested that climate mitigations bring significant co-benefits on health and the economy (Zhang Y et al , Xie et al 2018, Vandyck et al 2018, Li et al 2019). At the global level, West et al (2013) estimated the representative concentration pathway 4.5 (RCP 4.5)-equivalent greenhouse gases (GHGs) mitigation would result in 2.2 ± 0.8 million fewer air pollution-related premature deaths globally in 2100, compared with its reference scenario without climate policy. The economic co-benefits are more significant in East Asia than in other regions, about 10–70 times the marginal cost in 2030 (West et al 2013). Mccollum et al (2013) found that carbon reduction efforts could reduce energy-related health outcomes by about 2–32 million reduction of disability-adjusted life years in the world in 2030. One study from OECD shows the economic costs of outdoor air pollution by 2060 will increase to 1% of global GDP, with highest GDP losses in China (Dellink et al 2019). Our previous study estimated the co-benefits of climate change mitigation in Asian countries and found that climate change mitigation under the 2 °C goal could reduce 0.79 million premature deaths in 2050 in Asia. The economic benefit is equivalent to 2.8 trillion USD in 2050, about three times of the climate mitigation cost in Asia (Xie et al 2018).

At the national level, Balbus et al (2014) estimated that the health outcomes reductions due to the fine particulate matter (PM_2.5) exposure decline would save the United States $6–30 billion (in 2008 $) by 2020. Another study in the United States found that climate change mitigation would avoid more than 10,000 premature deaths in 2050 and 5,000 premature deaths in 2100 because of air quality improvement, equivalent to a value of statistical life (VSL) of approximately $150 billion (in 2005 $) by 2050 and $1.3 trillion by 2100 (Garcia-Menendez et al 2015). One study in EU showed the positive health effect could offset the resource costs associated with the clean air policy, which resulted in a net benefits on the economy (Vrontisi et al 2016). In China, Yang and Teng (2017) showed that compared to 2010 levels, if China reduces its 2030 carbon emissions intensity by 60–65%, it would reduce 78.85% of sulfur dioxide, 77.56% of nitrogen oxide, and 83.32% PM_2.5 emissions by 2030. Health improvement constitutes a substantial fraction of the potential benefits, along with averted adaptation costs and residual damage. Li et al (2019) recently estimated the co-benefits on air quality and human health in 2030 from China's targeting CO₂ intensity reductions, and found that 149,400 (95% confidence interval (CI): 125,400–173,300) deaths would be avoided in 2030 in China, including both PM_2.5 and ozone. For other regions, total avoided premature deaths are 1,200 (900–1,600) in South Korea, 3,500 (2,800–4,300) in Japan, and 1,900 (1,400–2,500) in the US, respectively (Li et al 2019).

Previous studies have focused on the health benefits of climate mitigation at the aggregate global level or short term until 2030, and they typically used a one-way assessment: from air pollutant emissions to the adverse health outcomes. Few studies attempted to estimate both the health and economic co-benefits (such as health expenditure saving and work supply change), from China's climate mitigation policy to China and transboundary regions in the long term. In this study, we aim to distinguish benefits of China's climate mitigation to China and other countries towards 2050. Moreover, we adopted an integrated methodology that includes the environment-health-economy by combining an economic model, a global chemistry transport model, and a health assessment model to evaluate the interactions among the economic systems, environment and human health in China. This study differs from our previous one (Xie et al 2018) which estimated the co-benefit on air quality and health in Asia from global climate policy, as for this study we quantify the co-benefits on air quality and health in China and downwind regions from different climate policies in China only.

2.1. Integrated assessment methods

2.2. Scenarios setting

3.1. Co-benefit of air quality improvement

Figure 1 shows the air pollutants emission change from C0 scenario to C1 scenario in 2050 s in China. The emission changes in 2030 s are shown in figure S2. Climate policy reduces the fossil fuel consumption, therefore, the emissions of the air pollutants precursor such as NO_x and SO₂ will decline in C1 scenario. From figure 1 we see that NO_x and SO₂ will decline by 20% to 30% in 2050 s compared with 2015, while for OC, BC and NH₃, the reductions are around 6–8%. On average, CO reduces 17.1 Tg yr⁻¹ at 2050 s, which is10.6 Tg yr⁻¹ higher than the year 2030 s (figure S2). At the same time, NO_x decreases 2.9 Tg yr⁻¹, and SO₂ decreases 3.3 Tg yr⁻¹ by 2050, almost double the changes in 2030 s (figure S2). BC and NH₃ also see larger decreases in 2050 s compared with 2030. At the regional level, most of the air pollutants emissions are reduced in the North and East of China, such as Shandong, Henan, Hebei, Anhui and Jiangsu. In the provinces in the West of China, such as Xinjiang, Qinghai, Inner Mongolia, Yunnan and Guizhou, the air pollutants emissions are very low and the emissions reductions are also limited.

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We simulated the annual average PM_2.5 concentration in 2015, and concentration changes in C0 and C1 in 2030 s and 2050 s using CAM-chem model (figure 2). In 2015, PM_2.5 concentration is the same for both C0 and C1, since the climate mitigation policy is the same in our assumption in the base year. From figure 2 we see that, the PM_2.5 concentration will decrease around 20 µg m⁻³ in north China in 2030 s and more provinces will experience significant reduction (more than 20 µg m⁻³) by 2050 s even in C0 scenario, and the provinces near the over-polluted regions also have around 10 µg m⁻³ decrease. Comparing with C0 scenario, more provinces in China experience remarkable PM_2.5 concentration reduction, larger than 10 µg m⁻³ in 2050 s. Meanwhile, the provinces with more than 20 µg m⁻³ PM_2.5 concentration in C1 scenario will extent to Northeast and Southwest of China. The provinces with slight PM_2.5 concentration reduction will extend to Northwest of China. Table S2 in the appendix shows the PM_2.5 concentration in North China in 2050, which is from 26.1 µg m⁻³ to 96.5 µg m⁻³ in the C0 scenario and from 23.3 µg m⁻³ to 75.5 µg m⁻³ in the C1 scenario.

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Under the C1 scenario, PM_2.5 concentration decreases more significantly in China compared with C0 scenario. The co-benefits (C1-C0) for annual PM_2.5 concentration in China are usually larger than 2.0 µg m⁻³ in the north and east of China in 2030 s (figure 3(a)). However, more significant reduction are seen in the 2050 s. Besides China, other East Asian countries also has co-benefit on PM_2.5 concentration reduction from China's climate mitigation. Figure 3 shows the PM_2.5 concentration reduction around 2 to 4 µg m⁻³ in 2030 s and 4 to 6 µg m⁻³ in 2050 s in the East Asia, especially in the Korea and west of Japan. The South Asia experiences tiny PM_2.5 concentration reduction, but reduction is smaller compared with East Asia. For other regions, the PM_2.5 concentration change is not significant.

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3.2. Health impact and economic co-benefits

Climate mitigation can improve the air quality and then reduce air pollution attributable premature deaths. In C0 scenario, the PM_2.5-related premature deaths are around 6.53 million globally in 2030 s and it will decrease to 6.40 million in 2050 s by using non-linear IER function. While, under the stringent climate mitigation policy the premature deaths will be reduced to 6.36 million in 2030 s, and 6.03 million in 2050 s in the C1 scenario. The avoided premature deaths from China's mitigation policy are 0.17 and 0.37 millions in 2030 s and 2050 s globally. Using non-linear GEMM function, in C0 scenario, the PM_2.5-related premature deaths are around 9.69 million globally in 2030 s and it will increase to 10.69 million in 2050 s. While, under the stringent climate mitigation policy the premature deaths will be reduced to 9.61 million in 2030 s, and 10.45 million in 2050 s in the C1 scenario. The avoided premature deaths from China's mitigation policy are 0.09 and 0.24 millions in 2030 s and 2050 s globally. Asian countries share the most of the avoided premature deaths, especially China, India, South East Asia and East Asia (figure S3, table S3). In China, the air quality improvement could reduce the premature deaths due to air pollution by 0.17 and 0.36 million in 2030 s and 2050 s separately, around 97% of globally total avoided deaths. Southeast Asia has the second largest co-benefits, about 2900 avoided premature deaths in 2030 and 7,600 in 2050. For India, the avoided premature deaths from China's climate mitigation are 280 in 2030 s and 740 in 2050 s. For other regions, the health co-benefit is not significant.

Previous studies have shown that different exposure response functions (ERFs) could have large uncertainties on the air pollution mortality analysis (Burnett et al 2018, Ostro et al 2018), and usually much larger than the uncertainty from concentration changes (Crippa et al 2019, Jin et al 2019). Here we estimate the avoided PM_2.5-related mortality in nine Asian countries by adopting three different ERFs: linear (Krewski et al 2009), non-linear GEMM (Burnett et al 2018), and non-linear IER (Apte et al 2015) (figure 4). The co-benefits for the avoided premature mortality due to ambient PM_2.5 exposure typically range in 2%–8% in most countries except India. When using the two non-linear functions (GEMM and IER), we get the similar co-benefits for the avoided premature mortality, though the air pollution related mortality burdens are significantly different by using different ERFs (figure S4). The mortality burden estimated using the non-linear IER function are smaller than those using the linear or the non-linear GEMM functions (figure S4). For countries with higher PM_2.5 exposure levels, the linear ERF yields the highest estimated mortality values, and the IER yields the lowest. In 2015, the estimated mortality under the C0 scenario in China and India based on the IER function was 1.68 (95% CI: 0.85–2.24) and 0.97 (95% CI: 0.54–1.37) million, respectively, consistent with previous studies (Lelieveld et al 2015). By 2050 s, the estimated mortality burden under the C0 scenario would be 3.60 (95% CI: 2.65–4.41) and 2.48 (95% CI: 1.76–3.13) million for China and India, respectively, using the IER function. By applying the GEMM function, we estimated the PM_2.5-related premature mortality is 2.87 million in China and 1.86 million in India in 2015 (table S4), and by 2050 they decrease to 2.05 (95% CI: 1.44–2.51) and 2.48 (95% CI: 1.44–1.73) million, respectively.

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We then use the willingness to pay method (WTP) to estimate the economic benefits of China's climate mitigation policy for China and other regions. Our results show air pollution leads to a heavy burden on the economy in the world. Economic cost due to PM_2.5 exposure are over $9000 billion globally in 2030 s, and it will increase to $13 trillion in 2050 s under the C0 scenario. Stringent Climate policy in China (WEO2016_450) will save $406 billion in 2030 s and $1206 billion in 2050 s globally. At the regional level, China has the most of the co-benefits from China's climate mitigation. In 2030 s, the avoided economic cost due to PM_2.5 reduction is around $400 billion, and it will increase to $1200 billion in 2050 in China. Some part of the avoided economic cost increase is due to the economic development in China. Most of the avoided economic cost is because of the avoided premature deaths due to air quality improvement. Following China, the Southeast Asia has $2.6 billion economic benefit from China's climate mitigation in 2030 s, and $10.3 billion in 2050 s.

Climate change mitigation also has co-benefits on reducing the PM_2.5-related morbidity, including the asthma attack, chronic bronchitis, respiratory hospital admission including respiratory, cardiovascular and cerebrovascular disease (the RRs for all the PM_2.5-attributed morbidity are seen in table S5). Figure S5 shows the total morbidity due to PM_2.5 exposure and the morbidity reduction due to China's climate mitigation policy. Air quality improvement can reduce the hospital admissions significantly both in 2030 s and 2050 s. The stringent climate policy in China can avoid 5.6 million cases morbidity globally in 2030 s, and 11.3 million in 2050 s. China has the largest fraction, about 5.1 million and 10.8 million in 2030 s and 2050 s. Southeast Asia shares significant co-benefits from China's climate mitigation, about 9,500 reduction of morbidity in 2030 s and 0.2 million in 2050 s. Besides, India and Japan also have co-benefit of avoided morbidity, about 1,000–1,500 in 2030 s and 2,000–4,000 in 2050 s, respectively.

Climate mitigation also brings co-benefits on health expenditure saving due to air pollution-related morbidity decrease. We use the Cost of Illness (COI) method to estimate the total health expenditure saving from China's climate mitigation, based on the number of each morbidity. The PM_2.5-related health expenditure was $8.9 billion in 2015 in China in the C0 scenario. While in the C1 scenario, China gets the most expenditure saving from the mitigation policy. In 2030 s, PM_2.5-related health expenditure is about $18.4 billion in China, and climate policy can reduce $1.2 billion expenditure in 2030 s. In 2050 s, the health expenditure saving will be much higher than 2030 s, around $4.1 billion. The Southeast Asia also has economic benefit from China's mitigation, about $7.7 million in 2030 s and $34.6 million in 2050 s. India has lower economic benefits compared with China and Southeast Asia, about $1.4 million in 2030 s and $7.6 million in 2050 s.

3.3. Worktime loss changes

Health impacts on labor force can reduce work time, which has negative impacts on the labor supply and the economy (Wu et al 2017, Xie et al 2018, 2019). Figure 5 shows the worktime loss and changes due to China's climate mitigation policy in 2030 and 2050. Asian countries have most of the co-benefit on avoided worktime loss. In the C0 scenario, China, India and Rest of Asia have the higher per capita worktime loss from PM_2.5 exposure, about 16.5 h, 15.9 h and 17.1 h in 2030 s. In 2050 s, the worktime loss will increase to 40.0 h in China, 44.3 h in India and 39.0 h in rest of Asia under C0 scenario, respectively. Under China's climate mitigation, worktime change is small in 2030 s even in China. However, it will become more significant in 2050 s due to larger air quality improvement. For example, China's climate mitigation can reduce the per capita work time loss about 5.6 h in China and 0.21 h in Japan in 2050 s. In Southeast Asia and Rest of Asia, per capita work time loss reduction is about 0.2 h in 2050 s.

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In this study, we combined GAINS-China, CAM-Chem and IMED|HEL models to quantify the benefit of climate mitigation policy in China and the impacts to the transboundary regions. Our result shows climate mitigation can bring significant benefit on air quality and health in the long term in China, especially in north and east of China. At the provincial level, cost-benefit is quite different among 30 provinces (tables S6–S8). For these provinces with higher population density and more heavy industry, such as Shandong, Henan, Hubei, Anhui and Hebei, mitigation policy could bring more benefit on the health and economy (table S8). While for the provinces with relatively good air quality, less populous and industrial regions, co-benefit is not significant, such as Yunnan, Guizhou, Xinjiang and Gansu in the west of China. It also has positive impact on the air quality improvement and health for East Asia and southeast Asia, such as South Korea and Japan. Both for the air pollution control and climate mitigation, cooperation is essential for neighboring regions.

We evaluated the model's performance in simulating the annual PM_2.5 concentration by comparing the model simulation in 2015 with three years surface observation data in China from 2014 to 2016 (table S9 in the appendix). For all the years, we have a pretty good correlation coefficient for the model performance (with R larger than 0.68) which are comparable with previous studies (Li et al 2018, Shindell et al 2018), though our model tends to overestimate PM_2.5 concentration. The difficulties for atmospheric chemistry models, especially for global models with coarse resolution, in capturing high concentrations of PM_2.5 especially in urban areas have been discussed extensively in the literature (Sun et al 2014), which could be caused by missing emissions, formation pathways such as in the case of secondary organic aerosols (Fu et al 2012) or spatial heterogeneity in emissions, meteorology and chemistry. A previous study which estimated the uncertainty for the health impact by applying seven PM_2.5 products, including groundbased observations, remote sensing and chemical transport models, and found that 28% uncertainty in the state-level PM_2.5 mortality burden were due to the choice of PM_2.5 products, much smaller than the uncertainty (130%) associated with the exposure-response function. Also based on the non-linear response function indicated by the IER function (Burnett et al 2014), there are supralinear responses of mortality to PM_2.5 over the high concentration end of the concentration–response functions. Meanwhile, since in our study, we focus on the PM_2.5 concentration changes from the future relative to present, and the overestimations are systematic, we do not expect this would affect our general conclusions.

For the health impact assessment, we use the historical nature death rate for the future projection and assume it is constant until 2050. However, China will become an aging society. The proportion of old people will increase until 2050. These old people are more sensitive to the air pollution. Air pollution attributable mortality will be much higher than our estimation. On the other hand, due to the economic development and urbanization, health system will become better in China and other developing countries. The accessibility to the health service will have benefit on the health condition, which may reduce the mortality. Because of the complicated impacts on health, health expenditure due to air pollution-related morbidity will also be quite different. In our estimations, we consider the health service price changing in the future. Table S10 in the appendix shows the health expenditure and uncertainty range in eight counties. The WTP method shows air pollution is leading enormous economic burden in many countries. China has highest economic loss due to PM_2.5 pollution about $ 7552 billion(95% CI: $ 5956, $8998) in the C0 scenario in 2050 and $ 6854 billion (95% CI: 5380, 8206) in the C1scenario by using ERF CIs. VSL selection will lead much larger uncertainty range (table S11).

At the national level, when the local policymakers construct mitigation policy, the regional differentiation should be considered. The mitigation policy or carbon reduction strategies not only focus on the carbon emission reduction, but also on the potential impact on the air quality, health and the economy. The provinces with higher GDP or carbon emissions should contribute more to the carbon reduction. At the same time, it will bring more co-benefit on the residents' health in these provinces. For these provinces with lower GDP and less carbon emissions, developing the economy will be the primary strategy. Less aggressive carbon emission target for local government will be more acceptable since Chinese government pays more and more attention and efforts on the air pollution control and climate mitigation via series of policy plan, including the development of renewable energy, end-of-pipe technology and regular environment protection actions. The fossil fuel consumption and air pollutants emissions decrease more than the model projections. For the regions under the intensive control plan, the air quality improvement may exceed the simulation from our model.

Climate mitigation absolutely has benefits on the air quality and human health (Xie et al 2019). Many studies have documented climate mitigation has net benefit to humanity. If we consider the co-benefit both on human and other systems on the earth (such as ecosystems, ocean), the total benefit will be much higher than our estimation. On the other hand, climate mitigation also costs a lot, especially for developing countries. Disadvantaged countries aim for economic growth and industrialization, which need more energy. At the same time, the technology is usually not so advanced in these countries. High-efficiency technology is expensive for them. The air pollution reduction is a heavy burden and may have negative impact on their economy. For example, China is under fast development and the imposing restrictions on the fossil fuel will have negative impacts on the energy-intensive sectors in the short-term, which will be sure to have unacceptable impacts. In the long-term, restriction on the traditional energy-intensive sectors will stimulate the low-carbon and high energy efficiency technology, which will actuate the development of the relative sectors.

There are some limitations in the setting of our study. This study only estimates the co-benefit of China's climate mitigation policy to China and transboundary regions, and ignores the climate mitigation in other countries. The estimated emissions and concentration will be much higher than considering global climate mitigation (West et al 2013, Zhang et al, 2017). In performing future air quality simulations, we also neglect the possible climate changes from the global and China climate policies on the influence of air quality. Study has shown that the climate change could have significant effect for ambient ozone but not for PM_2.5 (Jacob and Winner 2009, Fiore et al 2012, Zhang et al 2017b). This study used air pollution control strategy in the GAINS model, which only reflected the policy implications around 2015. Our estimations for the co-benefits for air quality and health will be higher since GAINS model is not able to capture the latest stringent air pollution policies in China after 2015. In the uncertainty analysis, we do not consider uncertainties from emissions and simulated air pollutant concentration, which could be quite large given our transport models large biases in predicting current PM_2.5 levels in China.

This study uses the integrated assessment methods to distinguish the air quality, human health and economy co-benefits from China's climate mitigation policies in China and other transboundary regions. We find that China's stringent mitigation policy, which aims to limit the average global temperature increase to 2 °C s above pre-industrial levels by 2100, will have significant co-benefits for China's PM_2.5 reduction and other regions, especially in the downwind countries, such as Korea and Japan, compared with its less tringent policy (the commitment of UN NDCs target). The stringent policy will contribute 2.0 to 4.0 μg m⁻³ PM_2.5 reduction in the north and east of China in the 2030 s, and the benefit will be higher by 2050 s. The policy can avoid 0.37 million premature deaths attributable to ambient PM_2.5 exposure by 2050 s globally. More than 95% of avoided premature deaths will happen in China, following by Korea which are 3800 avoided deaths, and Japan which are 2000 avoided deaths by 2050 s. Due to the large population size, India also has 1400 avoided premature deaths by 2050 s.

We then use the WTP method to estimate the economic benefits from the improved air quality, and find that the reduced ambient PM_2.5 concentration could avoid $406 billion and $1206 billion economic costs in 2030 s and 2050 s globally, with China the largest fraction of 98.5% ($400 billion) and 99.5% ($1200 billion), respectively. Beside the avoided mortality, we also estimate other health endpoints, such as morbidity due to ashma attack and hospital admissions, as well as the work time loss. PM_2.5 reduction due to China's stringent climate mitigation can avoid 5.6 million cases morbidity globally in 2030 s, and almost double that by 2050 s. We then use the COI method to estimate the total health expenditure saving, from the avoided morbidity due to stringent climate policy in China. Globally, the health expenditure saving is about $1.2 billion by 2030 s and $4.1 billion by 2050. Besides, PM_2.5 reduction also lowers per capita work time loss due to the morbidity and mortality. Worktime loss reduction will have positive on the economy and public welfare. Considering increasing of work time, total benefit of China's climate mitigation will much higher than current estimation both for China and other transboundary regions.

This study was supported by the Natural Science Foundation of China (71903010, 71704005, 51861135102, 71690241, 71690245, 71810107001), the Key Projects of National Key Research and Development Program of the Ministry of Science and Technology of China (2017YFC0213000), Gansu Provincial Health Industry Research Program Project(GSWSKY2017-28).

The data that support the findings of this study are available upon reasonable request from the authors.