Unveiling the spatial and sectoral characteristics of a high-resolution emission inventory of CO2 and air pollutants in China

et al. 2023

Preprint

Abstract. China is facing the challenge of synergistic reducing air pollutants and CO2 emissions in the coming decades. The coupled emission inventory of air pollutants and CO2 is a prerequisite to designing the synergetic emission reduction strategy. This study compiled China’s emission inventory of air pollutants and CO2 during 2005–2021 (ABaCAS-EI v2.0 dataset) based on a unified emission source framework and uniformed activity. The mitigation policies have decoupled the emissions of air pollutants and CO2 with economic development in China since 2013. In the context of growing activity levels, energy structure adjustment and energy & material saving brought a 7 % drop in the average annual growth rate of CO2 emissions after 2011; on the basis, end-of-pipe control contributed 51 %–98 % of air pollutants emission reductions after 2013. Sectors of industrial boilers and residential fossil combustion, and seven provinces (including Beijing, Tianjin, Shanghai, Jilin, Henan, Sichuan, and Qinghai) have achieved emission reductions of both air pollutants and CO2 during 2013–2021. The declining trends in both sectoral and regional emission ratios of air pollutants to CO2 indicated that the potential for synergistic emission reduction in China has declined from 2013 to 2021. The emission ratios in 2021 show that the residential fossil fuel combustion, iron and steel industry, and transportation have relatively higher co-benefits of SO2, PM2.5, NOx, and VOCs emission reductions when reducing CO2 emissions. Most of the cities with higher potential to synergistically reduce NOx, VOCs, and CO2 emissions are located within the Yangtze River Economic Belt; those with higher potential to co-control SO2 and CO2, and PM2.5 and CO2 are in southern and northeast China, respectively. What’s more, a further deconstruction of sectoral emissions in 2021 has suggested future reduction measures. For example, controlling coal consumption in the energy field; promoting innovative technologies with low air pollutant emission intensities and coal-saving effects in the iron and steel industry; combining coal and carbonate replacing technologies with separated particle control measures in the cement industry; controlling light-duty passenger vehicle, heavy-duty truck, agricultural machinery, and inland water transport in the transport field. Our dataset and analysis can provide insights into future co-control of air pollutants and CO2 emissions for China and other countries with the same demand worldwide. Our ABaCAS-EI v2.0 dataset can be accessed from https://doi.org/10.6084/m9.figshare.21777005.v1 (Li et al., 2022) by species, sector, and province.

Section: Introductionmentioning

confidence: 99%

Emission trends of air pollutants and CO₂ in China from 2005 to 2021

et al. 2023

Preprint

“…For PM emissions, our estimates were larger than MEIC, Gao et al (2022b), andAn et al (2021). The discrepancies resulted mainly from the inconsistent penetration rates and removal efficiencies of dust collectors determined at national level and from on-site surveys at provincial level.…”

Section: Comparisons With Previous Studiesmentioning

confidence: 59%

“…To further evaluate the influence of data and methods on emission estimation, we compared our provincial-level emission inventory with previous studies on emissions in Jiangsu in terms of the total and sectoral emissions through examinations of activity data, emission factor, removal efficiency and other parameters. inventories (Li et al, 2018;Sun et al,2018;Zhang et al, 2017b;Simayi et al, 2019;An et al, 2021;Gao et al 2022b). In particular, we stressed the differences in emissions by sector among our study, MEIC and An et al ( 2021) for 2017 as an example (Figure 8).…”

Section: Comparisons With Previous Studiesmentioning

confidence: 72%

High-resolution regional emission inventory contributes to the evaluation of policy effectiveness: A case study in Jiangsu province, China

et al. 2022

Preprint

Abstract. China has been conducting a series of actions on air quality improvement for the past decades, and air pollutant emissions have been changing swiftly across the country. Province is an important administrative unit for air quality management, thus reliable provincial-level emission inventory for multiple years is essential for detecting the varying sources of pollution and evaluating the effectiveness of emission controls. In this study, we selected Jiangsu, one of the most developed provinces in China, and developed the high-resolution emission inventory of nine species for 2015–2019, with improved methodologies for different emission sectors, best available facility-level information on individual sources, and real-world emission measurements. Resulting from implementation of strict emission control measures, the anthropogenic emissions were estimated to have declined 53 %, 20 %, 7 %, 2 %, 10 %, 21 %, 16 %, 6 % and 18 % for SO2, NOX, CO, NMVOCs, NH3, PM10, PM2.5, BC, and OC from 2015 to 2019, respectively. Larger abatement of SO2, NOX and PM2.5 emissions were detected for the more developed southern Jiangsu. Since 2016, the ratio of biogenic volatile organic compounds (BVOCs) to anthropogenic volatile organic compounds (AVOCs) exceeded 50 % in July, indicating the importance of biogenic sources on summer O3 formation. Our estimates in annual emissions of NOX, NMVOCs, and NH3 were generally smaller than the national emission inventory MEIC, but larger for primary particles. The discrepancies between studies resulted mainly from different methods of emission estimation (e.g., the procedure-based approach for AVOCs emissions from key industries used in this work) and inconsistent information of emission source operation (e.g., the penetrations and removal efficiencies of air pollution control devices). Regarding the different periods, more reduction of SO2 emissions was found between 2015 and 2017, but NOX, AVOCs and PM2.5 between 2017 and 2019. Among the selected 13 major measures, the ultra-low emission retrofit on power sector was the most important contributor to the reduced SO2 and NOX emissions (accounting for 38 % and 43 % of the emission abatement, respectively) for 2015–2017, but its effect became very limited afterwards as the retrofit had been commonly completed by 2017. Instead, extensive management of coal-fired boilers and upgradation and renovation of non-electrical industry were the most important measures for 2017–2019, accounted collectively for 61 %, 49 % and 57 % reduction of SO2, NOX and PM2.5, respectively. Controls on key industrial sectors maintained the most effective for AVOCs reduction for the two periods, while measures on other sources (transportation and solvent replacement) became increasingly important for more recent years. Our provincial emission inventory was demonstrated to be supportive for high-resolution air quality modeling for multiple years. Through scenario setting and modeling, worsened meteorological conditions were found from 2015 to 2019 for PM2.5 and O3 pollution alleviation. However, the efforts on emission controls were identified to largely overcome the negative influence of meteorological variation. The changed anthropogenic emissions were estimated to contribute 4.3 and 5.5 μg·m-3 of PM2.5 concentration reduction for 2015–2017 and 2017–2019, respectively. While elevated O3 by 4.9 μg·m-3 for 2015–2017, the changing emissions led to 3.1 μg·m-3 of reduction for 2017–2019, partly (not fully though) offsetting the meteorology-driven growth. The analysis justified the validity of local emission control efforts on air quality improvement, and provided scientific basis to formulate air pollution prevention and control policies for other developed regions in China and worldwide.

“…Gao et al integrated the data of population, GDP, and night lighting, and constructed a spatial allocation grid of carbon emissions of 10 × 10 km. The results showed that the carbon emissions map generated by the spatial allocation model constructed with the three kinds of data is closer to the actual situation ( 22 ). However, since the resolution of a gridded carbon emissions map is usually determined by the resolution of grid allocation parameters, it lacks accuracy when applied at the urban scale and is poorly combined with urban planning ( 23 , 24 ).…”

Section: Introductionmentioning

confidence: 66%

Vector maps and spatial autocorrelation of carbon emissions at land patch level based on multi-source data

Liao²,

Zhao³

2022

Front. Public Health

An accurate carbon emissions map is of great significance for urban planning to reduce carbon emissions, mitigate the heat island effect, and avoid the impact of high temperatures on human health. However, little research has focused on carbon emissions maps at the land patch level, which makes poor integration with small and medium-sized urban planning based on land patches. In this study, a vectorization method for spatial allocation of carbon emissions at the land patch level was proposed. The vector maps and spatial autocorrelation of carbon emissions in Zhangdian City, China were explored using multi-source data. In addition, the differences between different streets were analyzed, and the carbon emissions ratio of the land patch was compared. The results show that the vector carbon emissions map can help identify the key carbon reduction land patches and the impact factors of carbon emissions. The vector maps of Zhangdian City show that in 2021, the total carbon emissions and carbon absorptions were 4.76 × 109kg and 4.28 × 106kg respectively. Among them, industrial land accounted for 70.16% of carbon emissions, mainly concentrated in three industrial towns. Forest land carbon absorption accounted for 98.56%, mainly concentrated in the peripheral streets away from urban areas. The Moran's I of land patch level carbon emissions was 0.138, showing a significant positive spatial correlation. The proportion of land patches is an important factor in determining carbon emissions, and the adjustment of industrial structure is the most critical factor in reducing carbon emissions. The results achieved can better help governments develop different carbon reduction strategies, mitigate the heat island effect, and support low-carbon and health-oriented urban planning.