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

Synergistic reduction of air pollutants and carbon dioxide emissions in Shanxi Province, China from 2013 to 2020

Synergistic reduction of air pollutants and carbon dioxide (CO2) emissions is currently a key environmental policy in China, yet provincial-level studies remain scarce. To fill the gap, this study developed a coupled emission inventory from 2013 to 2020 in Shanxi, a coal-dependent province critical to China's energy security. This facilitated the investigation of emission trends, primary sources, synergistic effects, and spatial distribution. The results show that, while air pollutant emissions decreased significantly during the study period, CO2 emissions increased slightly. The main emitters of SO2, NOx, and CO2 were identified as power, heating, industrial boilers, and residential coal combustion. The iron and steel industry contributed significantly to PM2.5 emissions, coke production to VOCs, and vehicles to NOx and VOCs. NH3 emissions were mainly attributed to fertilizer use and livestock. Synergistic reductions were evident in coal-related sources, especially industrial boilers and residential coal combustion, underlining the importance of optimizing the energy structure. Anthropogenic emissions were concentrated in basins with poor dispersion conditions. Taiyuan, Yuncheng, and Linfen emerged as key areas for synergistic reduction efforts. This study provides important insights for environmental policy development in Shanxi and other coal-dependent regions.

Synergistic effects of a carbon emissions trading scheme on carbon emissions and air pollution: The case of China

Facing the dual pressures of the exacerbation of global climate change and the deterioration of the domestic environment caused by pollution, China has clearly adopted environmental regulatory measures to improve the climate environment. One measure is the carbon emissions trading scheme (CETS), which serves as a notable example of the country's efforts to improve the climate environment. We gathered panel data from 285 prefecture-level cities in China from 2005 to 2018 and used the Difference-in-Differences (DID) model to empirically examine the synergistic effects of the CETS on carbon emissions and air pollution. The results indicate that CETS have been effective in reducing urban carbon emissions by approximately 9.8%. Additionally, the schemes have caused a simultaneous reduction in particulate matter (PM)2.5 emissions by 11.7% and sulfur dioxide (SO2) emissions by approximately 9.7%, mitigating urban air pollution in China. It demonstrates that the scheme has significant synergistic effects on carbon emissions and air pollution. To achieve synergistic effects of CETS, effective measures include reducing energy intensity and upgrading the industrial structure. The implementation of CETS had heterogeneity in different conditions, and the synergistic effect of the scheme is more significant in eastern regions, large cities, and the final industrial stage. Our findings offer innovative solutions for the integrated management of carbon emissions and air pollution and provide valuable insights for policymakers to enhance China's CETS. Integr Environ Assess Manag 2024;20:1112-1124. © 2023 SETAC.

Unveiling the importance of VOCs from pesticides applicated in main crops for elevating ozone concentrations in China

Volatile organic compounds (VOCs) are considered as important precursors of ozone in the air, while the contribution of VOCs from pesticide application (PVOCs) to ozone production is unknown. Utilizing data from the Ministry of Agriculture and Rural Affairs of the People's Republic of China and ChinaCropPhen1km, this paper developed PVOC emission inventories with a resolution of 1 km for the main crops (rice, maize, and wheat) from 2012 to 2019 in China. The results revealed that pesticide application is an important VOC emission source in China. Specially, the PVOC emissions from the major grain-producing regions in June accounted for approximately 30% of the annual total PVOC emissions in the local regions. The simulation with the Weather Research and Forecasting Community Multiscale Air Quality model (WRF-CMAQ) indicated that the PVOC emissions increased the mean maximum daily 8-hour average (MDA8) ozone concentration across China by 2.5 ppb in June 2019. During the same period, PVOCs in the parts of North China Plain contributed 10% of the ozone formation. Under the comprehensive emission reduction scenario, it is anticipated that by 2025, the joint implementation of measures including reducing pesticide application, improving pesticide utilization efficiency and promoting solvent substitution will decrease PVOC emissions by 60% compared with 2019, thereby mitigating ozone pollution.

Proposal of policies based on temporal-spatial dynamic characteristics and co-benefits of CO2 and air pollutants from vehicles in Shanghai, China

In megacities, vehicle emissions face urgent challenges related to air pollution and CO2 control. To achieve the refinement of vehicle control policies for the co-control of air pollutants and CO2, this study established a vehicle emission inventory with high spatial and temporal resolution based on the hourly traffic flow in Shanghai and analyzed the spatial and temporal distribution characteristics of the real-time vehicle emissions. Meanwhile, a policy evaluation framework was constructed by combining pollutant emission predictions with quantitative co-control effect assessments. The results indicated that spatio-temporal variations in different air pollutants and CO2 could mainly be attributed to primary contributing vehicle types. The pollutants (CO2, CO and VOCs) primarily contributed by private cars exhibited a bimodal pattern in 24-h time series and their spatial distribution was concentrated in the urban city center. The spatial distribution of NOx and PM primarily contributed by heavy trucks was still obvious on non-urban center areas. Furthermore, the results of synergistic effect analysis revealed that the alternative energy replacement scenario demonstrated the most significant potential for the co-control. Based on temporal-spatial and co-benefit analysis, the precise control policy of vehicle emissions can be established through time-, region-, and model-control. This study provides references and research methods for the formulation of the vehicle refinement control policies in worldwide megacities.

Synergistic effect of combating air pollutants and carbon emissions in the Yangtze River Delta of China: spatial and temporal divergence analysis and key influencing factors

Synergizing the reduction of air pollutants and carbon emissions (APCE) has become a critical tactic alternative to address the issue of climate change. Taking the Yangtze River Delta (YRD) region of China as a case study, this paper explores the spatial and temporal distribution pattern of the coupling coordination degree (CCD) of combating APCE from 2011 to 2022, analyzes the dynamic change in CCD using the convergence test, and investigates the key factors affecting CCD via the Tobit regression model. The results show that (1) from 2011 to 2022, the air pollutants (AP) and CO2 emission (CE) in the YRD region decrease at the annual rate of 10.32% and 0.85%, respectively; (2) the CCD of reducing APCE in the YRD presents a W-shaped fluctuation before 2016 and then steps into a steady increase status after 2016; (3) the order of CCD in four provincial-level units by 2022 is Shanghai > Zhejiang > Jiangsu > Anhui. The proportion of cities where the CCD of reducing APCE enters the high-coordination period has reached 87.8%; and (4) the Tobit regression results affirm that economic growth, industrial structure, and green technological innovation exacerbate the CCD of combating APCE, while opening-up level mitigates it. The findings offer policymakers valuable insights into the importance of pursuing collaborative governance over APCE and ensuring sustainable development.

How to perceive and map the synergy between CO2 and air pollutants: Observation, measurement, and validation from a case study of China

Cities occupy a central position in addressing climate change and promoting sustainable regional development. Synergistic control of urban gas emissions at the city level is one of the main issues typically explored. The confounding effect and the interactions between the urban indicators of population and area have been ignored in previous studies. In this study, we examined the spatial distribution characteristics and synergy between greenhouse gases (CO2) and air pollutants (SO2 and NOX) using spatial population and gas emission data. By upgrading the city clustering algorithm (CCA), we established a method for defining active areas of gas emissions (spatial element-coupled clustering, SECC) and identified active areas of gas emissions in China. In this study, we created a research framework that can simultaneously consider the effects of population and area, as well as the possible interactions between these indicators in active areas. The superlinear scaling relationship between the above three gases was revealed at the active zone level, and the existence of synergy between the emission patterns of the three gases was confirmed. Via further model application, we measured the synergistic efficiency of the three gases. It was found that for every 1% increase in SO2 and NOX in an active zone, CO2 increases by 0.86%. In this study, we explored a new perspective and approach to explain the synergy between greenhouse gases and air pollutants. This is essential to promote national competition among cities to achieve synergistic control of CO2 and local air pollutants.

Trade-driven changes in China's air pollutant emissions during 2012-2017

Trade plays an important role in driving regional production and the associated pollutant emissions. Revealing the patterns and underlying driving forces of trade may be critical for informing future mitigation actions among regions and sectors. In this study, we focused on the "Clean Air Action" period from 2012 to 2017 and revealed the changes and driving forces in trade-related air pollutant emissions (including sulfur dioxide (SO₂), particulate matter with a diameter equal to or less than 2.5 μm (PM_2.5), nitrogen oxides (NO_x), volatile organic compound (VOC), and carbon dioxide (CO₂)) among regions and sectors in China. Our results showed that emissions embodied in domestic trade decreased considerably in absolute volume nationwide (23-61 %, except for VOC and CO₂), but the relative contribution ratios from consumption in central and southwestern China increased (from 13 to 23 % to 15-25 % for various species), and those for eastern China decreased (from 39 to 45 % to 33-41 % for various species). From the sector perspective, trade-driven emissions from the power sector decreased in relative contribution ratios, while those from other sectors (including chemical, metal, nonmetal and services) were outstanding for specific regions, and became new targeted sectors when seeking mitigation through domestic supply chains. For changes in trade-related emissions, reduction in emission factor dominated the decreasing trends for almost all regions (27-64 % for the national total, except for VOC and CO₂), and optimization in trade and/or energy structures also played marked reduction roles in specific regions, far offsetting the increasing effect of increasing trade volume (26-32 %, except for VOC and CO₂). Our study provides a comprehensive picture of how trade-associated pollutant emissions changed during the "Clean Air Action" period, which may facilitate the formulation of more effective trade-associated policies to mitigate future emissions.

Exploring spatiotemporal pattern and agglomeration of road CO2 emissions in Guangdong, China

Road transport is a prominent source of carbon emissions. However, fine-grained regional estimations on road carbon dioxide (CO2) emissions are still lacking. This study estimates road CO2 emissions in Guangdong Province, China, at high spatiotemporal resolution, with a bottom-up framework leveraging massive vehicle trajectory data. We unveil the spatiotemporal pattern of regional road CO2 emissions and highlight the contrasts among cities. The Greater Bay Area (GBA) is found to produce 76 % of the total emissions, wherein Guangzhou emits the most while Shenzhen has the highest emission intensity. Emission agglomeration is still an under-explored field, which we advance in this paper. We propose Quantile-based Hierarchical DBSCAN (QH-DBSCAN) to explore road CO2 emission agglomeration in GBA. Our method is the first one to identify the specific location and scope of emission hotspots. Emission hotspots exhibit significant concentration on major urban centers. Considering emission characteristics from multiple perspectives, we derive six emission categories, including four emission zones and two emission connectors. The density-based property of our method results in spatially contiguous regions with similar emission patterns. Accordingly, we divide policy zones and propose targeted strategies for road carbon reduction. The study provides new technologies and insights to achieve regional sustainable development.

Three-dimensional spatiotemporal variability of CO2 in suburban and urban areas of Shaoxing City in the Yangtze River Delta, China

Metropolitan areas are the most anthropogenically active places but there is a lack of knowledge in carbon dioxide (CO₂) spatial distribution in suburban and urban areas. In this study, the CO₂ three-dimensional distributions were obtained from 92 times vertical unmanned aerial vehicle (UAV) flight observations in Shaoxing suburbs and 90 times ground mobile observations in Shaoxing urban areas from Nov. 2021 to Nov. 2022. The vertical distribution showed that CO₂ concentrations gradually decreased from 450 to 420 ppm with altitude from 0 to 500 m. CO₂ vertical profile concentrations can be influenced by transport from multiple regions. Based on the vertical observation data combining a potential source contribution function (PSCF) model, Shaoxing suburban CO₂ were to be derived from urban areas in spring and autumn, while in winter and autumn were mainly from the long-transports from neighboring cities. Further the CO₂ concentrations of urban horizontal distribution were observed in the range of 460-510 ppm through the mobile campaigns. Urban CO₂ were partly emitted from traffic exhausts and residential combustion. Overall, CO₂ concentrations were observed to be lower in spring and summer due to the CO₂ uptake by plant photosynthesis. This uptake was initially quantified and accounted for 4.2 % of total CO₂ in suburbs and 3.3 % in urban areas by calculating the decrease in CO₂ concentration from peak to trough in the daytime. Compared with the CO₂ observed in the Lin'an background station, the maximum regional CO₂ enhancement in Shaoxing urban areas reached to 8.9 % while the maximum in suburbs only 4.4 %. The contribution differences between urban and suburban areas to regional CO₂ were relatively constant at 1.6 % in four seasons may be mainly ascribed to the contribution of long-range CO₂ transport to the suburbs.

Reconfirmation of the symbiosis on carbon emissions and air pollution: A spatial spillover perspective

Many studies have confirmed the co-emission characteristics of air pollution and carbon emissions. However, studies on the evolution and synergistic factors of the symbiosis of air pollution and carbon emissions over long time scales from a spatial spillover perspective are rare. Here, we identify the spatial evolution and agglomeration characteristics of carbon emissions and air pollution symbiosis by applying local autocorrelation analysis and geographical concentration and by using the dynamic spatial autoregressive model for multiple synergistic factors at city levels during 2006-2019 in China. The results are: (1) The spatial agglomeration and symbiosis of carbon emission and air pollution are similar and show strong spatial locking, as well as path-dependent properties. (2) The spatial imbalance of carbon emission agglomeration and pollution agglomeration gradually improved over time; the concentration centers are all located in Henan province, shifting northward. (3) The symbiosis between both carbon emission agglomeration and pollution agglomeration has significant "spatial and temporal scale effects", and the economic growth is nonlinear. Additionally, innovation vitality has a negative synergistic driving effect on this relationship. In addition to the results above, rapid industrialization and urbanization are taking place in China. Hence, serious actions against greenhouse gases and air pollutants are imminently needed.

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

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.