Air Quality Improvement Co-benefits of Low ... - ACS Publications

Policy Analysis Cite This: Environ. Sci. Technol. XXXX, XXX, XXX−XXX

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Air Quality Improvement Co-benefits of Low-Carbon Pathways toward Well Below the 2 °C Climate Target in China Nan Li,† Wenying Chen,*,† Peter Rafaj,‡ Gregor Kiesewetter,‡ Wolfgang Schöpp,‡ Huan Wang,† Hongjun Zhang,† Volker Krey,§ and Keywan Riahi§ †

Institute of Energy, Environment and Economy, Energy Science Building, Tsinghua University, Beijing 100084, China Air Quality and Greenhouse Gases Program and §Energy Program, International Institute for Applied Systems Analysis, Schlossplatz 1, Laxenburg, Austria, 2361

Environ. Sci. Technol. Downloaded from pubs.acs.org by UNIV OF LOUISIANA AT LAFAYETTE on 05/09/19. For personal use only.

‡

S Supporting Information *

ABSTRACT: This research links the Integrated MARKAL-EFOM system model of China (China TIMES) and the Greenhouse Gas and Air Pollution Interactions and Synergies model (GAINS) to assess the cobenefits of air quality improvement under the Nationally Determined Contribution (NDC) and the well below 2 °C (WBD2) target. Results show that the industry sector and power sector are the key sources necessary to reduce air pollutant emissions, mainly due to the phasing out of fossil fuels. The electrification in the building sector will be another main source by which to decrease PM2.5 emissions. The adoption of various low-carbon constraints and further air pollutant control strategies will significantly alleviate the current air pollution problems in China by reducing the concentration and scope of the air pollutants and reducing the corresponding number of premature deaths. A stricter air pollutant control strategy will lead to increases in air pollutant control costs; however, the low-carbon targets will help reduce these costs in the long run. Compared to the current national policy, within the same air pollutant control strategy, the reduction of air pollutant control cost can cover the incremental CO2 mitigation cost under the NDC target, while this cannot be realized under the WBD2 target.

1. INTRODUCTION China is seeking development pathways to achieve its Nationally Determined Contribution (NDC) target1 and air quality improvement target.2,3 Climate change mitigation should not be considered alone, as it is closely connected with air quality and other sustainability issues.4 A quantitative assessment of the potential positive effects of carbon emission reduction on air quality improvement may contribute to the promotion and implementation of climate policies.5−8 Rafaj et al. linked the Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS) and the Prospective Outlook for the Long-Term Energy System (POLES) models to indicate that approximately one-third of the estimated global co-benefits, quantified in terms of the reduction of the expenditures on air pollution control, would occur by 2050 in China.9 He et al. combined an energy projection model, an emission evaluation model, an air quality simulation model, and a health impact assessment model to show that aggressive energy policy can bring about 12−32% decline in air pollutant concentration in 2030.10 Some recent studies adopted the connection between Computable General Equilibrium (CGE) models and other air assessment models to explore the co-benefits. Dong et al. linked the Asian-Pacific Integrated Model (AIM)/CGE with the GAINS-China model to indicate that the SO2, NOx, and PM2.5 emissions would change by a factor 0.8,1.26, and 1.0 of © XXXX American Chemical Society

the 2005 level in 2030, respectively, by implementing CO2 and air pollutant mitigations.11 Li et al. linked the China Regional Energy Model (CREM), which is a CGE model, with the Atmospheric Chemistry Transport Model to indicate that national health co-benefits could partially or fully offset the policy costs depending on chosen health valuation.12 Some studies have focused on a specific sector. Cai et al. pointed out that 18%−62% of the increase of the power generation cost could be covered by the health benefits, which is calculated by the mortality change and value of a statistical life in China.13 Zhang et al. adopted the cost curves and GAINS to show that energy efficiency investment would result in up to notable decrease in air pollutant control cost in the Chinese iron and steel industry and cement industry.14,15 Mao et al. applied a simulation model to identify that a 4%−17% reduction of PM2.5 emissions can be achieved under 10%−100% fuel tax rate in the transportation sector in 2050.16 The aforementioned researches did not discuss the sectoral energy consumption and air pollutant emissions in details. Few of them took into account of different kind of the co-benefits, Received: December 14, 2018 Revised: April 11, 2019 Accepted: April 23, 2019

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DOI: 10.1021/acs.est.8b06948 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

Policy Analysis

Environmental Science & Technology

Figure 1. Linkage framework between the China TIMES model and the GAINS-China model.

quality improvement.9,18,32,33 GAINS adopts reduced-form source-receptor relationships, which define the spatial responses of air quality indicators to changes in precursor emissions within one region in a computationally efficient form. These source−receptor relationships have been derived through a sample of sensitivity simulations using the EMEP Atmospheric Chemistry Transport Model34 to analyze the effects of systematic perturbations of emissions for each source region. The responses of air quality indicators (for example, ambient concentrations of PM2.5) to a change in one unit of emissions are derived by relating the resulting changes of these indicators over the model domain to perturbations in emissions. This response is then scaled up by the amounts of emission changes that result from the emission scenarios.31 GAINS-China is a specific implementation of the GAINS model for China’s 32 subregions and provinces.35 Based on dose−response functions available from the published literature, the GAINS model estimates the long-term impacts from exposure to fine particulate matter. In the GAINS-China implementation used in this study, the disease- and age-specific integrated exposure-response functions from the Global Burden of Disease 201336 are used to calculate the increased risk of mortality from PM2.5 exposure and, consequently, the number of annual premature deaths attributable to ambient PM2.5. 2.3. Linking the China TIMES and GAINS Models. Figure 1 shows the linkage framework between the China TIMES and the GAINS-China models. The energy pathways analyzed herein are developed within the Linking Climate and Development Policies − Leveraging International Networks and Knowledge Sharing (CD-LINKS) project37 by introducing the NDC target, along with the WBD2 target, into the China TIMES model. The resulting energy consumption data are put into the GAINS-China model as the activity pathway. From these data, the GAINS model quantifies the air pollutant emissions and PM2.5 concentrations as well as the health impacts to evaluate the potential co-benefits under situations with and without air pollutant control technologies. After the mitigation targets are applied to the China TIMES model, the model computes scenario-dependent and cost-optimal profiles of technologies and fuels as well as the corresponding CO2 emission abatement costs. Through the adoption of different air pollution control strategies, which include parameters such as unabated emission factors, the application rates of control measures, and removal efficiencies, GAINS simulates the emission abatement legislation and the adoption of environmental standards. However, China TIMES and GAINS differ

including the improvement of air quality, the reduction of premature deaths, and the change of the mitigation cost of air pollutant and CO2 under the climate policy at the same time. Recognizing these gaps, we linked the China TIMES model17 and the GAINS model18 to analyze the air pollution cobenefits of low-carbon pathways toward the well below 2 °C (WBD2) target in China.

2. METHODOLOGY 2.1. China TIMES. As a combination of the Market Allocation Model (MARKAL)19 and the Energy Flow Optimization Model (EFOM),20 TIMES has been developed within the Energy Technology System Analysis Program (ETSAP) of the International Energy Agency (IEA).21 Based on the China MARKAL,22−24 the China TIMES model was developed in 5-year intervals from 2010 to 2050 and has been utilized as a powerful and reliable tool for evaluating carbon mitigation strategies and future energy system in China.17,25−30 China TIMES determines the least expensive combination of technologies and fuels that will meet the projected energy service demand. This system provides a detailed representation of energy flows at the technology level throughout the energy system, including the energy supply, energy conversion and transmission, and the end-use sector. The model is driven by energy service demands for different subsectors, including agriculture, transportation, industry, and building sectors. Energy-intensive industries such as the iron and steel, building materials, petrochemicals, nonferrous metals, and paper industries are explicitly modeled. Transportation is divided into passenger and freight purposes and further sectioned by the transport mode. The building sector considers space heating and cooling, water heating and cooking, lighting, and electric appliances. Space heating and cooling demands are considered relative to different climatic zones. 2.2. GAINS. Developed by the Air Quality and Greenhouse Gases (AIR) program at the International Institute for Applied Systems Analysis (IIASA), the GAINS model is an integrated assessment model that encompasses the interactions between various policies concerning air quality improvement and greenhouse gas emission reduction.18,31 Changes to the energy system caused by energy and climate policies are reflected implicitly through exogenous scenarios. By producing emission scenarios for different air pollutants (PM, SO2, NOx, VOC, and NH3) from exogenously supplied activity data (energy consumption, industrial production, transport, and agriculture projections), the GAINS model has been widely used to evaluate the synergies between carbon mitigation and air B


Policy Analysis

Environmental Science & Technology Table 1. Scenario Definitions air pollutant control strategy, climate target NPi NDC2030 NDCCUM WBD2

current national policy 11Gt in 2030, remain unchanged 400Gt Cumulative emissions in 2010−2050 290Gt Cumulative emissions in 2010−2050

NFC, no further control than 2015

CLE, current legislation

MFR, maximum feasible reduction

NPi-NFC(Reference) NDC2030-NFC NDCCUM-NFC WBD2-NFC

NPi-CLE NDC2030-CLE NDCCUM-CLE WBD2-CLE

NPi-MFR NDC2030-MFR NDCCUM-MFR WBD2-MFR

Figure 2. (a) Total final energy consumption; (b) final energy by industry; (c) final energy by transportation; and (d) final energy by building.

below 400 Gt from 2010 to 2050. Furthermore, the cumulative CO2 emissions below 290 Gt from 2010 to 2050 is applied as an emission constraint to model the WBD2 pathway. The other dimension is the air pollutant control strategy (end-of-pipe controls such as exhaust-cleaning technologies) for improving air quality, simulated in the GAINS-China model. Each low-carbon target is assessed along with three corresponding air pollution control strategies. The No-FurtherControl (NFC) strategy assumes that the control measures in 2015 are carried forward to 2050. In addition to the existing policies and measures in 2015, the Current Legislation (CLE) strategy reflects the impacts of announced policies, which are documented in the official plans and targets. Given that the “coming policies” have not yet been fully implemented in the current legislation and regulations, the scope and timing of their full realization are based on expert judgment within the IIASA AIR program of the relevant political, regulatory, market, infrastructural, and financial constraints. The Maximum Feasible Reduction (MFR) shows the maximum technically feasible realization of all air pollutant control

regarding the sector definition and fuel-type classification. The output data concerning energy consumption from China TIMES are, therefore, converted into the structure of the input data in GAINS-China.

3. SCENARIOS Scenario analysis aims to support policy decisions based on comparisons among different policy scenarios. This study establishes scenarios in two dimensions, as shown in Table 1. One dimension is the climate target for mitigating carbon emissions, simulated in the China TIMES model. We designed four carbon emission reduction pathways in line with those in the CD-LINKS project for the Chinese models in this study.37 The Current National Policy (NPi) scenario assumes the implementation of policies affecting the energy system and climate change and is extrapolated beyond 2020. NDC2030 (NDC pathway, with an emission peak in 2030), extrapolates the current goal of the NDC, which limits the national emissions to below 11 Gt from 2030 to 2050. NDCCUM (NDC pathway, with a cumulative emission target), models the NDC target by restricting the cumulative CO2 emissions to C


Policy Analysis


Figure 3. Sectoral contribution to emission reduction of SO2, NOx, and PM2.5 in 2050 under different scenarios (Mt).

NFC, and WBD2-NFC will reach 32%, 48%, and 62%, respectively, which are much higher than the value of 24% in NPi-NFC. The final energy consumption will decrease significantly because of the decline in the energy service demand and the improvement in the energy efficiency. In 2050, the total amount of final energy consumption in NDC2030-NFC, NDCCUM-NFC, and WBD2-NFC will be 7%, 13%, and 23% less than that in NPi-NFC, respectively (Figure 2a). In terms of sector composition, energy consumption in the industry sector will reduce earlier than in the building and transportation sectors, and the amount of reduction will be larger than that in these two sectors due to industrial structure adjustments and the deployment of energy-saving technologies. The final energy consumption in the industry sector will be 1.6 billion tons of coal equivalent (tce) in 2050 in WBD2NFC, 25% lower than that in NPi-NFC (Figure 2b). The

options known to the GAINS-China model, taking into account the practical constraints of deployment.

4. RESULTS AND DISCUSSION 4.1. Carbon Emission Pathways and Energy Transformations. The impacts of climate targets on the transition of the energy system are assessed in China TIMES. In NPiNFC, CO2 emissions will reach the peak value at 14 Gt in 2040, which is 55% higher than that in 2015. Compared to the case for NPi-NFC, CO2 emissions under the low-carbon development scenarios will significantly decrease. In NDC2030-NFC, CO2 emissions will reach the peak value of 11 Gt in 2030 and remain at this level through 2050, with the cumulative emissions being 425Gt from 2010 to 2050. With stricter emission constraints, CO2 emissions will decrease to 8 Gt in NDCCUM-NFC and 3 Gt in WBD2-NFC by 2050. In 2050, the share of nuclear and renewable energy within the primary energy consumption in NDC203-NFC, NDCCUMD


Policy Analysis


Figure 4. Provincial annual population-weighted concentration of PM2.5 in 2050 in different scenarios.40

consumption of coal and coke will be 1.2 billion tce, which accounts for 55% of the total industrial final energy consumption in 2015. This proportion will decrease to 38%, 37%, 36%, and 27% in 2050 in NPi-NFC, NDC2030-NFC, NDCCUM-NFC, and WBD2-NFC, respectively. The final energy consumption in the building sector will be 1.1 billion tce in 2050 in WBD2-NFC, much smaller than that in NPi-NFC (Figure 2c). In the building sector, biomass combustion, as a non-commercial energy source, will reduce significantly. The share of natural gas within the energy

consumption in the building sector will increase from 10% in 2010 to 20%, 20%, 20% and 23% in 2050 in NPi-NFC, NDC2030-NFC, NDCCUM-NFC, and WBD2-NFC, respectively. In 2010, electricity consumption only accounts for 20% of the energy consumption in the building sector. In 2050, this value will increase to approximately 50% in NPi-NFC and 60% in WBD2-NFC. The energy consumption in the transportation sector will continue to increase by 2050 and will reach 0.94, 0.93, 0.91, and 0.83 billion tce in 2050 in NPi-NFC, NDC2030-NFC, E


Policy Analysis


Figure 5. Premature deaths and population-weighted mean PM2.5 concentrations in different scenarios of air pollutant control technologies.

sector to NOx abatement is smaller because this sector is more dependent on the consumption of refined oil, and transformation of the fuel structure lags behind the developments in the other sectors. Compared to the NPi scenario, the reduction in the PM2.5 emissions from the power sector in 2050 in NDC2030-CLE, NDCCUM-CLE, and WBD2-CLE will be 133, 192, and 121 thousand tons, accounting for 36%, 44%, and 12% of the total emission reductions, respectively (Figure 3f). The industry sector is also an important source of PM2.5 emission reductions. Compared to the NPi-CLE scenario, the reduction of PM2.5 emissions from the industry sector in 2050 in NDC2030-CLE, NDCCUM-CLE, and WBD2-CLE will be 132, 120, and 244 thousand tons, respectively. The PM2.5 emission reduction from the building sector will increase gradually due to the decline in the energy consumption and the increase in electrification with the tightening of emission reduction constraints. Compared to the NPi-CLE scenario, the reduction in the PM2.5 emissions from the building sector in 2050 in NDC2030-CLE, NDCCUM-CLE, and WBD2-CLE will be 77, 95, and 585 thousand tons, accounting for 20%, 21%, and 59% of the total reductions, respectively. The electrification progress transfers the PM2.5 emissions from the building sector to the power sector. However, considering that the power structure is cleaner under low-carbon development, as the proportion of thermal power plants is substantially replaced by non-fossil-fuel power plants, the increased electricity demand will not lead to a great negative effect on emissions in the power sector. For the transportation sector, exhaust emissions from vehicles are an important factor causing haze in large cities, but the primary pollutants from vehicles are carbon monoxide, hydrocarbons, and nitrogen oxides. 4.3. Air Quality Improvement. To cope with the serious situation of environmental pollution, China has placed a high priority on environmental protection and established a stricter standard of air quality, which are 15 μg/m3 (grade 1) and 35 μg/m3 (grade 2) and still far from complying with the WHO standard of 10 μg/m3.38,39 With climate policies and further air pollutant control strategies, China’s air pollution situation is expected to improve. On the one hand, the level of air pollution will be further reduced. Taking Beijing as an example, the population-weighted PM2.5 concentration in 2050 in NPi-

NDCCUM-NFC, and WBD2-NFC, respectively (Figure 2d). The low-carbon technologies in the transportation sector, like the electric vehicles and fuel cell vehicles, are not yet mature, and the corresponding infrastructure is incomplete. It is expensive to transform the traditional internal combustion engine technology, which is currently the dominant technology in this field, into the low-carbon technologies. Therefore, when emission reduction constraints become gradually stricter, the decrease in the energy consumption in the transportation sector will lag behind those in the industry sector and building sector. 4.2. Air Pollutant Emission Reductions. In NPi-NFC, the annual SO2 emissions will reach the peak value at 23.5 million tons (Mt) in 2030, and NOx emissions will reach the peak value at 32.8 Mt in 2025, while the PM2.5 will continue to decrease slowly from 8.5 Mt in 2015 to 5.8 Mt in 2050. In NPiCLE, the emissions of SO2, NOx and PM2.5 will all decrease directly from 2015 to 2050, achieving 75%, 55%, and 68% of the values in 2015, respectively. In NPi-MFR, the emissions of SO2, NOx, and PM2.5 in 2050 will be further reduced to 42%, 36%, and 29% of the values in 2015, respectively. In this section, we mainly discuss the comparison between scenarios under the CLE strategy, which are similar to those under the NFC or MFR strategy. The SO2 emissions in 2050 in NDC2030-CLE, NDCCUMCLE, and WBD2-CLE will be 2, 4, and 8 million tons less than that in NPi-CLE, respectively (Figure 3d). With low-carbon technology development in the power sector and the growing share of desulphurization technology, the power and industry sectors have achieved 77% of the SO2 emission reductions in 2050 in WBD2-CLE compared to that in NPi-CLE. In WBD2CLE, the building sector will also contribute to an emission reduction of 1.9 million tons due to the reduction of coal consumption. Because the sulfur content in China’s refined oil is small, the transportation sector contributes less to the SO2 emission reduction. The power and industry sectors will also be the main sources of reductions of NOx emissions. However, the reduction in the power sector will be first implemented in NDC2030-CLE and NDCCUM-CLE, and that in the industry sector will become the main reduction source in WBD2-CLE (Figure 3e). The NOx emissions from the industry and transportation sectors are comparable, but the contribution of the transportation F


Policy Analysis


Figure 6. Total incremental system cost in different scenarios compared to that under NPi scenarios in 2050.

NFC (Figure 4a) will be 91 μg/m3 and will decrease to 86, 81, and 72 μg/m3 in NDC2030-NFC (Figure 4d), NDCCUMNFC (Figure 4g), and WBD2-NFC (Figure 4j), respectively, with the adoption of low-carbon constraints. Switching to a tighter climate target can yield comparable air quality improvement as changing the air pollutant control strategy from NFC to CLE. For instance, the PM2.5 concentration in WBD2-NFC (Figure 4j) is close to that in NDCCUM-CLE (Figure 4h) in Beijing in 2050. The PM2.5 concentration is further reduced to 34 μg/m3 in 2050 in WBD2-MFR (Figure 4l) with the extension of the MFR control strategy. On the other hand, the number of areas with serious air pollution problems will decrease. The number of provinces in which the population-weighted PM2.5 concentration is less than 35 μg/ m3, which is China’s grade 2 PM2.5 concentration, will increase from 10 in 2050 in NPi-NFC (Figure 4a) to 26, 29, 29, and 30 in 2050 in NPi-MFR (Figure 4c), NDC2030-MFR (Figure 4f), NDCCUM-MFR (Figure 4i), and WBD2-MFR (Figure 4l), respectively. All 30 provinces can reach China’s grade 2 standard in 2050 in WBD2-MFR, but only 5 can reach the WHO standard. The PM2.5 concentrations in Beijing, Tianjin, and Hebei will be higher than those in other provinces in 2050 in the different scenarios, meaning that JJJ should remain a focus area for policies related to air quality. 4.4. Health Impacts. Many epidemiological studies have shown that long-term exposure to air pollution is associated with increased mortality from cardiovascular and respiratory diseases.41−43 Here, we calculate premature deaths from PM2.5 based on the number of people in different exposure classes and GBD-2013 integrated exposure-response functions.44 As shown in Figure 5, the corresponding premature deaths will be 1.61 million in 2050 in NPi-CLE and will be 2%, 5%, and 11% lower in 2050 in NDC2030-CLE, NDCCUM-CLE, and WBD2-CLE, respectively, quantified at 1.57, 1.53, and 1.43 million people. The number will be further reduced to 1.18, 1.14, 1.08, and 0.98 million in NPi-MFR, NDC2030-MFR, NDCCUM-MFR, and WBD2-MFR, respectively. Despite the decreasing emissions and ambient PM2.5 concentrations, premature deaths are expected to increase in several scenarios, mainly due to the aging population and higher baseline mortality. The results indicate that to significantly reduce the premature deaths caused by PM2.5, a stricter control strategy close to the MFR would need to be adopted along with the low-carbon targets.

4.5. System Incremental Cost. The total system incremental cost is composed of the cost of air pollutant control and carbon emission reduction, which are evaluated by GAINS-China and China TIMES, respectively. The growth of energy activities will lead to increase of air pollutant control cost. Even in the NFC case, which does not assume implementation of any further controls beyond those currently implemented, the air pollutant control cost will increase from 92.8 billion dollars (using the dollar value in 2005) in 2015 to 135.7 billion dollars in 2030 and to 156.7 billion dollars in 2050. A stricter air pollutant control strategy such as the application of advanced desulphurization, denitrification, and dust-removal measures will lead to increases in air pollutant control costs. In moving from the NPi-NFC pathway to NPiCLE and NPi-MFR in 2050, the increase of the air pollutant control cost will be 476 and 840 trillion dollars, respectively, as shown in Figure 6. Note that the MFR control strategy is hypothetical extremes to demonstrate the maximum air pollution reduction potential through end-of-pipe measures and is not a realistic policy option. A large fraction of the reduction potentials can be realized at much lower costs by optimizing the control strategy for the most cost-efficient solution, as demonstrated in the EU air quality legislation.45 Therefore, the costs of MFR scenarios should be viewed as the upper boundaries of air pollution control costs that can be incurred. A stricter carbon emission constraint will force the transformation of energy systems, especially the phasing out of fossil fuels, leading to the higher CO2 mitigation cost and lower air pollutant control cost. In moving from NPi-CLE to NDC2030-CLE and NDCCUM-CLE, the decrease of air pollutant control cost will be 176 and 228 billion dollars, while the increase of CO2 mitigation cost will be 87 and 165 billion dollars. Under the combined effect of the CO2 mitigation cost and the air pollutant control cost, the total incremental system cost in NDC2030-CLE and NDCCUM-CLE will be 37% and 13% lower than that in NPi-CLE. The reduction of air pollutant control costs will be larger than the incremental system costs caused by the low-carbon targets in NDC2030 and NDCCUM; however, this cost reduction will not offset the incremental CO2 mitigation costs associated with very deep decarbonization, as simulated in the WBD2 scenario. The total incremental system cost in WBD2-MFR is 132% higher than that in NPi-MFR. G


Policy Analysis

Environmental Science & Technology 4.6. Uncertainty Analysis. There are some research limitations. First, the uncertainties of the low-carbon targets have not been discussed, and it will influence the energy consumption and CO2 emissions. The low-carbon targets in this study are consistent with those for the Chinese models in the CD-LINKS project, which is favorable to comparison between national models and global models in the next phase of research. Second, the uncertainties of the application rates of different air pollutant control strategies have not been explored, and it will impact the co-benefit assessment. In this research, the control strategies are currently from the GAINSChina database and should be updated by the latest policies and database in the next step. Third, the health impact calculation will introduce some uncertainties. Note that different methodologies and risk functions have been developed in recent years, and each of these has an associated uncertainty. Hence, it is more instructive to analyze trends rather than absolute numbers. Given the uncertainty of the premature mortality, the health impacts are not quantified based on the value of statistical life.

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AUTHOR INFORMATION

Corresponding Author

*Phone: +010 6277 2756; e-mail: [email protected]. ORCID

Wenying Chen: 0000-0003-3685-449X Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This research is supported by the National Natural Science Foundation of China (grant no. 51861135102 and 71690243), Ministry of Science and Technology (grant no. 2018YFC1509006), and by the CD-LINKS (Linking Climate and Development Policies − Leveraging International Networks and Knowledge Sharing) project (European Union’s Horizon 2020 research and innovation program, grant no. 642147).

5. POLICY IMPLICATIONS This study links the China TIMES model and the GAINSChina model to assess the co-benefits of air quality improvement under the NDC and WBD2 targets. Results show that energy consumption in the industry sector will reduce earlier and in a larger amount than the building sector and transportation sector under the climate policy. Electrification will be the major decarbonization way in the building sector. To speed up the transformation in the transportation sector, the investment to lower the cost and complete the infrastructure of the electric vehicles and fuel cell vehicles is essential. The industry sector and power sector are the key source to reduce the air pollutant emissions, mainly due to the phasing out of fossil fuels. The consumption of electricity instead of coal and biomass in the building sector can be another major source with which to decrease the PM2.5 emissions. The concentration of air pollution and the area with serious pollution will reduce under the climate policies and air pollutant control strategies, contributing to the decrease of premature deaths caused by exposure in PM2.5. A stricter control strategy like MFR can significantly reduce the premature deaths, while it should be carried out in a more cost-efficient way. Compared to the current national policy, within the same air pollutant control strategy, the reduction of air pollutant control cost can cover the incremental CO2 mitigation cost under the NDC target, while this cannot be realized under the WBD2 target. Therefore, a cost-effective method needs to be identified to approach the WBD2 target without compromising the air quality targets.

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rate of emission reduction controls, carbon emission pathways and nonfossil energy’s share in the primary energy consumption, emission reduction of air pollutants, provincial annual concentrations of PM2.5 in 2015 and the standards of annual concentration of PM2.5, population exposed to different concentrations in different scenarios, and air pollutant control costs (PDF)

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REFERENCES

(1) United Nations Framework Convention on Climate Change. INDCs as communicated by Parties. https://unfccc.int/process/theparis-agreement/nationally-determined-contributions-ndcs (accessed May 9, 2019). (2) National Development and Reform Commission. Action plan for the prevention and control of air pollution. 2013. http://www.gov.cn/ zwgk/2013-09/12/content_2486773.htm (accessed May 9, 2019). (3) World Health Organization. World Health Statistics. https:// www.who.int/gho/publications/world_health_statistics/2017/en/ (accessed May 9, 2019). (4) International Energy Agency. Energy and Air Pollution, world energy outlook special report. https://webstore.iea.org/weo-2016special-report-energy-and-air-pollution (accessed May 9, 2019). (5) Amann, M. Co-benefits of Greenhouse Gas Mitigation Strategies on Human Health through Reduced Emissions of Air Pollutants. Am. J. Roentgenology 2009, 199, 208−212. (6) Muller, N. Z. The design of optimal climate policy with air pollution co-benefits. Resource & Energy Economics. 2012, 34, 696− 722. (7) West, J. J.; Smith, S. J.; Silva, R. A.; Naik, V.; Zhang, Y.; Adelman, Z.; Fry, M.; Anenberg, S.; Horowitz, L.; Lamarque, J. F. Cobenefits of Global Greenhouse Gas Mitigation for Future Air Quality and Human Health. Nat. Clim. Change 2013, 3, 885−889. (8) Rao, S.; Klimont, Z.; Leitao, J.; Riahi, K.; van Dingenen, R.; Reis, L. A.; Calvin, K.; Dentener, F.; Drouet, L.; Fujimori, S.; Harmsen, M.; Luderer, G.; Heyes, C.; Strefler, J.; Tavoni, M.; van Vuuren, D. P. A multi-model assessment of the co-benefits of climate mitigation for global air quality. Environ. Res. Lett. 2016, 11, 124013. (9) Rafaj, P.; Schöpp, W.; Russ, P.; Heyes, C.; Amann, M. Cobenefits of post-2012 global climate mitigation policies. Mitigation & Adaptation Strategies for Global Change. 2013, 18, 801−824. (10) He, K.; Lei, Y.; Pan, X.; Zhang, Y.; Zhang, Q.; Chen, D. Cobenefits from energy policies in China. Energy 2010, 35, 4265−4272. (11) Dong, H.; Dai, H.; Dong, L.; Fujita, T.; Geng, Y.; Klimont, Z.; Inoue, T.; Bunya, S.; Fujii, M.; Masui, T. Pursuing air pollutant cobenefits of CO2 mitigation in China: A provincial leveled analysis. Appl. Energy 2015, 144, 165−174.

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.8b06948. Tables showing the basic assumption of China TIMES, the mapping of fuels and sectors between the China TIMES and GAINS models, air pollutant control options adopted in this research, and the number of provinces in different population-weighted concentration levels; figures showing examples of the application H


Policy Analysis

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