Dynamic projection of anthropogenic emissions in China: methodology and 2015–2050 emission pathways under a range of socio-economic, climate policy, and pollution control scenarios

Abstract

Abstract. Future trends in air pollution and greenhouse gas (GHG) emissions for China are of great concern to the community. A set of global scenarios regarding future socio-economic and climate developments, combining shared socio-economic pathways (SSPs) with climate forcing outcomes as described by the Representative Concentration Pathways (RCPs), was created by the Intergovernmental Panel on Climate Change (IPCC). Chinese researchers have also developed various emission scenarios by considering detailed local environmental and climate policies. However, a comprehensive scenario set connecting SSP–RCP scenarios with local policies and representing dynamic emission changes under local policies is still missing. In this work, to fill this gap, we developed a dynamic projection model, the Dynamic Projection model for Emissions in China (DPEC), to explore China's future anthropogenic emission pathways. The DPEC is designed to integrate the energy system model, emission inventory model, dynamic projection model, and parameterized scheme of Chinese policies. The model contains two main modules, an energy-model-driven activity rate projection module and a sector-based emission projection module. The activity rate projection module provides the standardized and unified future energy scenarios after reorganizing and refining the outputs from the energy system model. Here we use a new China-focused version of the Global Change Assessment Model (GCAM-China) to project future energy demand and supply in China under different SSP–RCP scenarios at the provincial level. The emission projection module links a bottom-up emission inventory model, the Multi-resolution Emission Inventory for China (MEIC), to GCAM-China and accurately tracks the evolution of future combustion and production technologies and control measures under different environmental policies. We developed technology-based turnover models for several key emitting sectors (e.g. coal-fired power plants, key industries, and on-road transportation sectors), which can simulate the dynamic changes in the unit/vehicle fleet turnover process by tracking the lifespan of each unit/vehicle on an annual basis. With the integrated modelling framework, we connected five SSP scenarios (SSP1–5), five RCP scenarios (RCP8.5, 7.0, 6.0, 4.5, and 2.6), and three pollution control scenarios (business as usual, BAU; enhanced control policy, ECP; and best health effect, BHE) to produce six combined emission scenarios. With those scenarios, we presented a wide range of China's future emissions to 2050 under different development and policy pathways. We found that, with a combination of strong low-carbon policy and air pollution control policy (i.e. SSP1-26-BHE scenario), emissions of major air pollutants (i.e. SO2, NOx, PM2.5, and non-methane volatile organic compounds – NMVOCs) in China will be reduced by 34 %–66 % in 2030 and 58 %–87 % in 2050 compared to 2015. End-of-pipe control measures are more effective for reducing air pollutant emissions before 2030, while low-carbon policy will play a more important role in continuous emission reduction until 2050. In contrast, China's emissions will remain at a high level until 2050 under a reference scenario without active actions (i.e. SSP3-70-BAU). Compared to similar scenarios set from the CMIP6 (Coupled Model Intercomparison Project Phase 6), our estimates of emission ranges are much lower than the estimates from the harmonized CMIP6 emissions dataset in 2020–2030, but their emission ranges become similar in the year 2050.

Air pollution emissions from Chinese power plants based on the continuous emission monitoring systems network

“…( a – c ), The estimated Chinese power emissions (Tg) for PM ( a ) SO 2 ( b ) and NO X ( c ) in our database (purple bars) and in existing databases (Refs. 5 , 10 , 11 , 20 , 53 – 55 ; the Greenhouse Gas and Air Pollution Interactions and Synergies database (GAINS) ( https://gains.iiasa.ac.at/models/gains_models3.html ); the Multi-resolution Emission Inventory for China (MEIC) ( http://meicmodel.org/ ); non-purple bars). The error bars indicate the associated uncertainty ranges.…”

Impacts of the meteorological condition versus emissions reduction on the PM2.5 concentration over Beijing–Tianjin–Hebei during the COVID-19 lockdown

“…Anthropogenic emissions of carbon monoxide, nitrogen oxide, sulfur dioxide, volatile organic compounds, black carbon (BC), organic carbon (OC), PM 2.5 , and PM 10 were based on the 2016 monthly Multi-resolution Emission Inventory for China (MEIC-2016) ( http://www.meicmodel.org ). Then, MEIC-2016 was projected to the year 2019 (MEIC-2019) according to the anthropogenic emission trends provided by Tong et al (2020) to make the emissions more reasonable. Since there is no anthropogenic emissions information for the year 2020, the emissions for 2020 (MEIC-2020) were calculated according to the suggestion from the Chinese Research Academy of Environmental Sciences ( http://www.craes.cn/zjhky/zzjg/kydw/gjdqwrfz/ ), as follows: transportation, industry and power emissions decreased by 80%, 40%, and 50%, respectively; residential emissions increased by 30%; and agricultural emissions remained unchanged.…”

Aerosol radiative forcings induced by substantial changes in anthropogenic emissions in China from 2008 to 2016

“…As mentioned in Sect. 3.1, the reduction of BC emissions made a negligible contribution to changes of CCN numbers, while the reduction of SO 2 emissions and associated decline in sulfate caused an important indirect effect by serving as CCN (Twomey, 1974;Andreae and Rosenfeld, 2008). By separating the contributions made by SO 2 and BC emissions to the CRF (the Exp16SO2 and Exp16BC cases), we found that the annual mean CRF was +0.93 W m −2 due to the decrease of SO 2 emissions and −0.18 W m −2 due to the decrease of BC emissions.…”

“…Aerosols perturb the global energy balance through the scattering and absorption of sunlight (Charlson et al, 1992) and serve as cloud condensation nuclei (CCN) particles, which impact both the cloud albedo and cloud lifetime (Twomey, 1974;Andreae and Rosenfeld, 2008). Changes in anthropogenic aerosol concentrations from preindustrial times to the present day are estimated to induce a global mean net cooling effect of −0.4 to −1.5 W m −2 at the top of the atmosphere (TOA) that partly masks the warming effects by increased carbon dioxide concentrations (Boucher et al, 2013).…”