Satellite-based estimates of decline and rebound in China's CO2 emissions during COVID-19 pandemic

Insights into the spatiotemporal heterogeneity, sectoral contributions and drivers of provincial CO2 emissions in China from 2019 to 2022

CO2 emissions (CEs) pose a growing threat to environmental changes and global warming, attracting extensive attention. Here, we leveraged near-real-time monitoring data spanning 2019 to 2022 to investigate spatiotemporal heterogeneity, sectoral contributions, provincial spatial correlation, and driving factors influencing CEs at the provincial level in China. Our analysis, integrating Moran's Index analysis, Spearman correlation analysis, and the Geographically Weighted Regression model, unveiled China's consistent world-leading CEs, surpassing 10,000 Mt over the study period. Spatially, CEs exhibited a heterogeneous distribution, with markedly higher emissions in eastern and northern regions compared to western and southern areas. Temporally, CEs displayed significant fluctuations, peaking in the fourth quarter before declining in subsequent quarters. Chinese New Year and COVID-19 had the biggest effects on CEs, with average daily reductions of -20.8 % and -18.9 %, respectively, compared to the four-year average and the same period in 2019. Sectoral analysis highlighted the power and industry sectors as primary contributors to CEs in China, jointly accounting for 37.9 %-40.2 % and 43.5 %-46.4 % of total CEs, respectively. Spatial clustering analysis identified a distinct High-High agglomeration region, predominantly encompassing provinces such as Inner Mongolia, Shandong and Jiangsu. Furthermore, total energy consumption and electricity consumption emerged as significant drivers of CEs, exhibiting correlation coefficients exceeding 0.9, followed by exhaust emissions, population size, and gross domestic product. Moreover, the influence of drivers on provincial CEs exhibited notable spatial heterogeneity, with regression coefficients displaying a decreasing gradient from north to south. These findings provide scientific and technological support to realize the provincial dual-carbon goals in China.

Observed CO2 concentration reveals steep decrease of anthropogenic emissions in winters of 2021 and 2022 in Hangzhou and Yangtze River Delta region, China

After the initial prevention of COVID-19 in early 2020, China gradually lifted its control measures in the latter half of 2020, leading to a rebound in anthropogenic CO2 emissions. However, the emergence of a COVID-19 variant in late 2021, particularly in densely populated and economically developed areas, prompted the reimplementation of stringent confinement measures. Furthermore, with the policy shift towards achieving "herd immunity", China fully lifted pandemic control measures in December 2022, resulting in the unrestricted movement of residents and facilitating the widespread transmission of COVID-19 in the ensuing months. But to our knowledge, no studies have yet quantified the relative changes in CO2 emissions during these successive phases, representing a significant knowledge gap in understanding the impact of varying control measures on anthropogenic CO2 emissions at both city and regional scales. Consequently, we selected Hangzhou city and the Yangtze River Delta (YRD) region as our study area due to their status as economically developed and densely populated regions in China. In order to mitigate the influence of biological CO2 flux, we utilized wintertime atmospheric CO2 observations at two urban and rural sites, along with their gradient, across three years (December 2020-February 2023). We employed two distinct methods with the WRF-STILT model to quantify the relative changes in CO2 emissions. Our findings indicate that (1) atmospheric CO2 concentrations at both sites and their gradients in 2022 were significantly lower than those observed in 2020, with modeled simulations using consistent emissions suggesting that changes in emissions were the predominant factor rather than variations in atmospheric transport processes; (2) After applying the source region partition method, anthropogenic CO2 emissions during the winter of 2021 decreased to 69.8 % ± 1.6 % in Hangzhou city when compared with 2020, while emissions in the YRD region dropped to 92.5 % ± 6.2 %. In winter 2022, emissions in Hangzhou city decreased to 79.9 % ± 1.9 %, and YRD region decreased to 82.0 % ± 7.2 % relative to 2020, highlighting substantial spatial heterogeneity from the city to the regional scale; (3) notably, the observed decreases in CO2 emissions in both Hangzhou and the YRD were not reflected in prior inventories, which indicated an annual increase of 8 % for 2021 and 2022, suggesting that even the most recent inventories fail to account for the prolonged emission reduction effects occurring over the preceding three years.

Insights into transportation CO2 emissions with big data and artificial intelligence

The ever-increasing stream of big data offers potential for deep decarbonization in the transportation sector but also presents challenges in extracting interpretable insights due to its complexity and volume. This overview discusses the application of transportation big data to help understand carbon dioxide emissions and introduces how artificial intelligence models, including machine learning (ML) and deep learning (DL), are used to assimilate and understand these data. We suggest using ML to interpret low-dimensional data and DL to enhance the predictability of data with spatial connections across multiple timescales. Overcoming challenges related to algorithms, data, and computation requires interdisciplinary collaboration on both technology and data.

Enhancing Space-Based Tracking of Fossil Fuel CO2 Emissions via Synergistic Integration of OCO-2, OCO-3, and TROPOMI Measurements

Top-down estimates of fossil fuel CO2 (FFCO2) emissions are crucial for tracking emissions and evaluating mitigation strategies. However, their practical application is hindered by limited data coverage and overreliance on NOx-to-CO2 emission ratios from emission inventories. We developed the Machine Learning-Driven Mapping Satellite-based XCO2en (ML-MSXE) model using the column-averaged dry-air mole fraction of CO2 enhancement (XCO2en) derived from OCO-2 and OCO-3 measurements to reconstruct the XCO2en distribution for monitoring FFCO2 emissions. Compared to the previous Machine Learning-Driven Deriving XCO2en from Mapped XCO2 (ML-DXEMX) model, ML-MSXE enhances the utilization of TROPOMI NO2 measurements, increasing their relative contribution from 4.3 to 21.7%, thereby improving XCO2en reconstruction accuracy and enhancing the ability to track emissions. Despite the COVID-19 lockdown, XCO2en levels in China rose from 1.33 ± 1.06 in 2019 to 1.39 ± 1.01 ppm in 2021. In February 2020, while the national average rate of XCO2en decline (16.3%) aligned with the reduction in FFCO2 emissions estimated by inventories, XCO2en further revealed varying rates of decline between cities. Furthermore, the spatial distribution of XCO2en identified hotspots where FFCO2 emissions might be underestimated by inventories. This study presents a space-based approach for monitoring FFCO2 emissions, offering valuable insights for assessing carbon neutrality progress and informing policy.

A novel carbon emission monitoring method for power generation enterprises based on hybrid transformer model

Accurate carbon accounting is essential for power generation enterprises to participate in carbon markets and implement carbon reduction strategies. However, due to excessive reliance on detailed energy data and manual calculations, carbon emission accounting in power generation enterprises suffers from low frequency, significant lag, and poor reliability. Some evidences suggest a strong correlation between internal carbon emissions and electricity consumption in power generation enterprises. Inspired by them, this paper proposes a novel model, named ICEEMDAN-Inception-Transformer, to thoroughly explore the relationship between power data and carbon emissions, providing precise hourly carbon emission acquisition for power enterprises. This model first utilizes ICEEMDAN to extract the significant characteristics of power data, then employs advanced Inception and Transformer structures to capture the complex high-dimensional features of the "electricity-carbon" correlation, thereby realizing enterprise carbon emissions monitoring. The model was extensively validated on three datasets from three different types of power enterprises. The average performance on indicators of RMSE, MAE, MAPE, and R2 of the model on the three datasets reached 11.69 tCO2, 9.58 tCO2, 2.44%, and 96.42%, respectively. The results demonstrate that the proposed monitoring model possesses certain advantages in terms of the accuracy and robustness of acquiring enterprise carbon emissions, providing valuable insights for high-frequency accurate carbon monitoring in power generation enterprises.

Ship emission variations during the COVID-19 from global and continental perspectives

The COVID-19 pandemic and the International Maritime Organization's (IMO) 2020 fuel-switching policy have profoundly impacted global maritime activities, leading to unprecedented changes in shipping emissions. This study aimed to examine the effects from different scales and investigate the underlying drivers. The big data model Ship Emission Inventory Model (SEIM) was updated and applied to analyze the spatiotemporal pattern of global ship emissions as well as the main contributors in 2019 and 2020. Overall, ships emitted NOx, CO, HC, CO2, and N2O declined by 7.4 %-13.8 %, while SO2, PM2.5, and BC declined by 40.9 %-81.9 % in 2020 compared with 2019. The decline in CO2 emissions indicated a comparable reduction across vessel tonnages. Ship emissions occurring at cruising status accounted for over 90 % of the ship's CO2 emission reduction. Container ships, chemical tankers, and Ro-Ro vessels were the primary contributors to the emission reductions, with container ships alone responsible for 39.4 % of the CO2 decrease. The ship's CO2 emissions variations revealed the decline-rebound patterns in response to the pandemic. Asian-related routes saw emissions drop in February 2020, followed by a rebound in May, while European and American routes experienced declines starting in May, with a recovery in August. Further analysis of CO2 emission in Exclusive Economic Zones (EEZs) showed high temporal consistency between vessel CO2 emissions, sailing speeds, and international trade volumes across continents, and exhibited heterogeneity in main contributing ship type of emission reduction on continental scale. Our study reveals the short-term fluctuation characteristics of global ship emissions during the pandemic, particularly focusing on their spatiotemporal evolution and the inherent disparities. The results highlight the correlation between global ship emissions and trade, as well as the operational status of ships, and their rigidity.

Trends and drivers of anthropogenic NOx emissions in China since 2020

Nitrogen oxides (NOx), significant contributors to air pollution and climate change, form aerosols and ozone in the atmosphere. Accurate, timely, and transparent information on NOx emissions is essential for decision-making to mitigate both haze and ozone pollution. However, a comprehensive understanding of the trends and drivers behind anthropogenic NOx emissions from China-the world's largest emitter-has been lacking since 2020 due to delays in emissions reporting. Here we show a consistent decline in China's NOx emissions from 2020 to 2022, despite increased fossil fuel consumption, utilizing satellite observations as constraints for NOx emission estimates through atmospheric inversion. This reduction is corroborated by data from two independent spaceborne instruments: the TROPOspheric Monitoring Instrument (TROPOMI) and the Ozone Monitoring Instrument (OMI). Notably, a reduction in transport emissions, largely due to the COVID-19 lockdowns, slightly decreased China's NOx emissions in 2020. In subsequent years, 2021 and 2022, reductions in NOx emissions were driven by the industry and transport sectors, influenced by stringent air pollution controls. The satellite-based inversion system developed in this study represents a significant advancement in the real-time monitoring of regional air pollution emissions from space.

Quantifying the Resilience of Coal Energy Supply in China Toward Carbon Neutrality

Facing the challenge of achieving the goal of carbon neutrality, China is decoupling the currently close dependence of its economy on coal use. The energy supply and demand decarbonization has substantial influence on the resilience of the coal supply. However, a general understanding of the precise impact of energy decarbonization on the resilience of the coal energy supply is still lacking. Here, from the perspective of network science, we propose a theoretical framework to explore the resilience of the coal market of China. We show that the processes of increasing the connectivity and the competition between the coal enterprises, which are widely believed to improve the resilience of the coal market, can undermine the sustainability of the coal supply. Moreover, our results reveal that the policy of closing small-sized coal mines may not only reduce the safety accidents in the coal production but also improve the resilience of the coal market network. Using our model, we also suggest a few practical policies for minimizing the systemic risk of the coal energy supply.

China's Fossil Fuel CO2 Emissions Estimated Using Surface Observations of Coemitted NO2

Accurate estimates of fossil fuel CO2 (FFCO2) emissions are of great importance for climate prediction and mitigation regulations but remain a significant challenge for accounting methods relying on economic statistics and emission factors. In this study, we employed a regional data assimilation framework to assimilate in situ NO2 observations, allowing us to combine observation-constrained NOx emissions coemitted with FFCO2 and grid-specific CO2-to-NOx emission ratios to infer the daily FFCO2 emissions over China. The estimated national total for 2016 was 11.4 PgCO2·yr-1, with an uncertainty (1σ) of 1.5 PgCO2·yr-1 that accounted for errors associated with atmospheric transport, inversion framework parameters, and CO2-to-NOx emission ratios. Our findings indicated that widely used "bottom-up" emission inventories generally ignore numerous activity level statistics of FFCO2 related to energy industries and power plants in western China, whereas the inventories are significantly overestimated in developed regions and key urban areas owing to exaggerated emission factors and inexact spatial disaggregation. The optimized FFCO2 estimate exhibited more distinct seasonality with a significant increase in emissions in winter. These findings advance our understanding of the spatiotemporal regime of FFCO2 emissions in China.

An overview for monitoring and prediction of pathogenic microorganisms in the atmosphere

Corona virus disease 2019 (COVID-19) has exerted a profound adverse impact on human health. Studies have demonstrated that aerosol transmission is one of the major transmission routes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Pathogenic microorganisms such as SARS-CoV-2 can survive in the air and cause widespread infection among people. Early monitoring of pathogenic microorganism transmission in the atmosphere and accurate epidemic prediction are the frontier guarantee for preventing large-scale epidemic outbreaks. Monitoring of pathogenic microorganisms in the air, especially in densely populated areas, may raise the possibility to detect viruses before people are widely infected and contain the epidemic at an earlier stage. The multi-scale coupled accurate epidemic prediction system can provide support for governments to analyze the epidemic situation, allocate health resources, and formulate epidemic response policies. This review first elaborates on the effects of the atmospheric environment on pathogenic microorganism transmission, which lays a theoretical foundation for the monitoring and prediction of epidemic development. Secondly, the monitoring technique development and the necessity of monitoring pathogenic microorganisms in the atmosphere are summarized and emphasized. Subsequently, this review introduces the major epidemic prediction methods and highlights the significance to realize a multi-scale coupled epidemic prediction system by strengthening the multidisciplinary cooperation of epidemiology, atmospheric sciences, environmental sciences, sociology, demography, etc. By summarizing the achievements and challenges in monitoring and prediction of pathogenic microorganism transmission in the atmosphere, this review proposes suggestions for epidemic response, namely, the establishment of an integrated monitoring and prediction platform for pathogenic microorganism transmission in the atmosphere.

Vertical Evolution of Ozone Formation Sensitivity Based on Synchronous Vertical Observations of Ozone and Proxies for Its Precursors: Implications for Ozone Pollution Prevention Strategies

Photochemical ozone (O3) formation in the atmospheric boundary layer occurs at both the surface and elevated altitudes. Therefore, the O3 formation sensitivity is needed to be evaluated at different altitudes before formulating an effective O3 pollution prevention and control strategy. Herein, we explore the vertical evolution of O3 formation sensitivity via synchronous observations of the vertical profiles of O3 and proxies for its precursors, formaldehyde (HCHO) and nitrogen dioxide (NO2), using multi-axis differential optical absorption spectroscopy (MAX-DOAS) in urban areas of the Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) regions in China. The sensitivity thresholds indicated by the HCHO/NO2 ratio (FNR) varied with altitude. The VOC-limited regime dominated at the ground level, whereas the contribution of the NOx-limited regime increased with altitude, particularly on heavily polluted days. The NOx-limited and transition regimes played more important roles throughout the entire boundary layer than at the surface. The feasibility of extreme NOx reduction to mitigate the extent of the O3 pollution was evaluated using the FNR-O3 curve. Based on the surface sensitivity, the critical NOx reduction percentage for the transition from a VOC-limited to a NOx-limited regime is 45-72%, which will decrease to 27-61% when vertical evolution is considered. With the combined effects of clean air action and carbon neutrality, O3 pollution in the YRD and PRD regions will transition to the NOx-limited regime before 2030 and be mitigated with further NOx reduction.

Time and frequency analysis of daily-based nexus between global CO2 emissions and electricity generation nexus by novel WLMC approach

The studies have focused on changes in CO2 emissions over different periods, including the COVID-19 pandemic. Even if CO2 emissions are temporarily reduced during the pandemic according to annual figures, this may be misleading. Considering annual figures is important to understand the overall trend, but using data with much higher frequency (e.g., daily) is much better suited to investigate dynamic relationships and external effects. Therefore, this study comprehensively analyzes the association between CO2 emissions and disaggregated electricity generation (EG) sources across the globe by employing the novel wavelet local multiple correlation (WLMC) approach on daily data from 1st January 2020 to 31st March 2023. The results demonstrate that (1) based on the main statistics, daily CO2 emissions range between 69 MtCO2 and 116 MtCO2, indicating that there is an oscillation, but no sharp changes over the analyzed period. (2) based on the baseline regression using the dynamic ordinary least squares (DOLS) approach, the constructed estimation models have a high predictive ability of CO2 emissions, reaching ~ 94%; (3) in the further analysis employing the WLMC approach, there are significant externalities between EG resources, which affect CO2 emissions. The results present novel insights about time- and frequency-varying effects as well as a disaggregated analysis of the effect of EG on CO2 emissions, demonstrating the significance of the energy transition towards clean sources around the world.

Toward a low carbon path: Do E-commerce reduce CO2 emissions? Evidence from China

To address global climate change, achieving carbon peak and carbon neutrality has become a global consensus. However, the means to simultaneously achieve carbon reduction and promote green economic development, particularly in developing countries, require further investigation. This study evaluates the impact of e-commerce on CO2 emissions. Through an examination of the effects of the National E-Commerce Demonstration City (NEDC) policy from 2006 to 2017, this paper reveals that e-commerce growth facilitated by the NEDC policy resulted in a 7.89% reduction in total CO2 emissions and a per capita reduction of 1.1146 tons in the pilot cities. Mechanism analysis demonstrates that the upgrading of industrial structure, development of digital finance, and the growth of innovation and entrepreneurship serve as primary pathways for this impact. The robustness of the findings is supported by parallel trend tests, placebo tests, and additional sensitivity analyses. Furthermore, the research reveals that the NEDC policy exhibits a more significant reduction in CO2 emissions in cities with higher levels of economic development and non-resource-based cities. Welfare analyses show that the NEDC policy has significant socio-economic effects. These findings provide new evidence on the environmental effects of the digital economy and offer insights into achieving carbon neutrality.

Factors Driving China's Carbon Emissions after the COVID-19 Outbreak

The outbreak of the coronavirus disease 2019 (COVID-19) may exert profound impacts on China's carbon emissions via structural changes. Due to a lack of data, previous studies have focused on quantifying the changes in carbon emissions but have failed to identify structural changes in the determinants of carbon emissions. Here, we use China's latest input-output table and apply structural decomposition analyses to understand the dynamic changes in the determinants of carbon emissions from 2012 to 2020, specifically the impact of COVID-19 on carbon emissions. We find that final demand per capita contributed to emissions growth at a slower pace, but production structure drove a greater carbon emissions increase than before the pandemic. Export-led emissions growth rebounded, and investment-led emissions were more concentrated in the construction sector. The carbon intensity of several heavy industries increased, e.g., the nonmetallic products sector, the metal products sector, and the petroleum, coking, and nuclear fuel sector. In addition, lower production efficiency and increased reliance on carbon-intensive inputs indicated a deterioration in production structure. For policy implications, efforts should be undertaken to increase investment in low-carbon industries and increase the proportion of consumption in GDP to shift investment-led growth to consumption-led growth for an inclusive and green recovery from the pandemic.

Ongoing CO2 monitoring verify CO2 emissions and sinks in China during 2018-2021

Accurate estimating CO2 emissions and sinks is crucial in achieving carbon neutrality in China. However, CO2 emissions from bottom-up inventories are uncertain at regional scales and lack independent verification from atmospheric perspectives. Here we integrated 39 high-precision CO2 stations in China to top-down invert emission-sink variations at 45 km × 45 km and achieved a full range of inventories verification. The results show that China's CO2 emissions are 15% higher than those of five bottom-up inventories, to an annual total of 3.40 Pg C a-1 for 2018-2021. After deducting human and livestock respiration, the annual CO2 emissions were 3.13 Pg C a-1 (11.48 Pg CO2 a-1). The annual increase in emissions slowed from 3.7% in 2019 to 1.1% in 2020 and resumed growth to 4.0% in 2021, consistent with observed CO2 growth rates in China. China's land CO2 sink, deducting farmland sinks and lateral fluxes, was 0.57 Pg C a-1 (2.09 Pg CO2 a-1) for 2018-2021 (higher than most global inverse models), accounting for ∼16.9% of anthropogenic CO2 emissions. The land sink in China decreased by -19.3% in 2019 due to a weak El Niño event and increased by 3.2% in 2020 and 13.7% in 2021. It is worth noting that inverse CO2 emissions and sinks in western China still face large uncertainty due to limited CO2 monitoring. Overall, our top-down estimates demonstrate spatiotemporal variations in CO2 emissions and sinks from atmospheric perspectives and highlight challenges for different provinces in achieving 2060 carbon neutrality with verified estimates.

Climate predicts wildland fire extent across China

Wildland fire extent varies seasonally and interannually in response to climatic and landscape-level drivers, yet predicting wildfires remains a challenge. Existing linear models that characterize climate and wildland fire relationships fail to account for non-stationary and non-linear associations, thus limiting prediction accuracy. To account for non-stationary and non-linear effects, we use time-series climate and wildfire extent data from across China with unit root methods, thus providing an approach for improved wildfire prediction. Results from this approach suggest that wildland area burned is sensitive to vapor pressure deficit (VPD) and maximum temperature changes over short and long-term scenarios. Moreover, repeated fires constrain system variability resulting in non-stationarity responses. We conclude that an autoregressive distributed lag (ARDL) approach to dynamic simulation models better elucidates interactions between climate and wildfire compared to more commonly used linear models. We suggest that this approach will provide insights into a better understanding of complex ecological relationships and represents a significant step toward the development of guidance for regional planners hoping to address climate-driven increases in wildfire incidence and impacts.

Hindered visibility improvement despite marked reduction in anthropogenic emissions in a megacity of southwestern China: An interplay between enhanced secondary inorganics formation and hygroscopic growth at prevailing high RH conditions

The PM2.5-bound visibility improvement remains challenging in China despite vigorous control on anthropogenic emissions in recent years. One critical issue could exist in the distinct physicochemical properties especially of secondary aerosol components. Taken the COVID-19 lockdown as an extreme case, we focus on the relationship between visibility, emission cuts, and secondary formation of inorganics with changing optical and hygroscopic behaviors in Chongqing, a representative city characterized with humid weather and poor diffusion conditions in Sichuan Basin, southwest of China. It is found that the increased secondary aerosol abundance (e.g., PM2.5/CO and PM2.5/PM10 as a proxy) with enhanced atmospheric oxidative capacity (e.g., O3/Ox, Ox = O3 + NO2), combined with insignificant meteorological dilution effect, might partly offset the benefit on the improved visibility from substantial reduction in anthropogenic emissions during the COVID-19 lockdown. This is in line with the efficient oxidation rates of sulfur and nitrogen (i.e., SOR, NOR), increasing more significantly with PM2.5 and relative humidity (RH) in comparison to O3/Ox. The resulted larger fraction of nitrate and sulfate (i.e., fSNA) would promote the optical enhancement (i.e., f(RH)) and mass extinction efficiency (MEE) of PM2.5, especially under highly humid conditions (e.g., RH > 80 %, with approximately half of the occurrence frequency). This could further facilitate secondary aerosol formation via aqueous-phase reaction and heterogeneous oxidation, likely due to enhanced water uptake and enlarged size/surface area upon hydration. In combination of gradually increased atmospheric oxidative capacity, this positive feedback would in turn inhibit the visibility improvement particularly at high RH environment. Considering the current air pollution complex status over China, further work on the formation mechanisms of major secondary species (e.g., sulfate, nitrate, and secondary organics), size-resolved chemical and hygroscopic properties, together with their interactions are highly recommended. Our results are hoping to assist in the atmospheric pollution complex mitigation and prevention in China.

Year-round changes in tropospheric nitrogen dioxide caused by COVID-19 in China using satellite observation

The lockdown policy deals a severe blow to the economy and greatly reduces the nitrogen oxides (NOx) emission in China when the coronavirus 2019 spreads widely in early 2020. Here we use satellite observations from Tropospheric Monitoring Instrument to study the year-round variation of the nitrogen dioxide (NO₂) tropospheric vertical column density (TVCD) in 2020. The NO₂ TVCD reveals a sharp drop, followed by small fluctuations and then a strong rebound when compared to 2019. By the end of 2020, the annual average NO₂ TVCD declines by only 3.4% in China mainland, much less than the reduction of 24.1% in the lockdown period. On the basis of quantitative analysis, we find the rebound of NO₂ TVCD is mainly caused by the rapid recovery of economy especially in the fourth quarter, when contribution of industry and power plant on NO₂ TVCD continues to rise. This revenge bounce of NO₂ indicates the emission reduction of NOx in lockdown period is basically offset by the recovery of economy, revealing the fact that China's economic development and NOx emissions are still not decoupled. More efforts are still required to stimulate low-pollution development.

Satellite reveals a steep decline in China's CO2 emissions in early 2022

Response actions to the coronavirus disease 2019 perturbed economies and carbon dioxide (CO2) emissions. The Omicron variant that emerged in 2022 caused more substantial infections than in 2020 and 2021 but it has not yet been ascertained whether Omicron interrupted the temporary post-2021 rebound of CO2 emissions. Here, using satellite nitrogen dioxide observations combined with atmospheric inversion, we show a larger decline in China's CO2 emissions between January and April 2022 than in those months during the first wave of 2020. China's CO2 emissions are estimated to have decreased by 15% (equivalent to -244.3 million metric tons of CO2) during the 2022 lockdown, greater than the 9% reduction during the 2020 lockdown. Omicron affected most of the populated and industrial provinces in 2022, hindering China's CO2 emissions rebound starting from 2021. China's emission variations agreed with downstream CO2 concentration changes, indicating a potential to monitor CO2 emissions by integrating satellite and ground measurements.

Subways and CO2 emissions: A global analysis with satellite data

This paper estimates a global CO2 emissions model using satellite data at 25 km resolution. The model incorporates industrial sources (including power, steel, cement, and refineries), fires, and non-industrial population-related factors associated with household incomes and energy requirements. It also tests the impact of subways in the 192 cities where they operate. We find highly significant effects with the expected signs for all model variables, including subways. In a counterfactual exercise estimating CO2 emissions with and without subways, we find they have reduced population-related CO2 emissions by about 50 % for the 192 cities and about 11 % globally. Extending the analysis to future subways for other cities, we estimate the magnitude and social value of CO2 emissions reductions with conservative assumptions about population and income growth and a range of values for the social cost of carbon and investment costs. Even under pessimistic assumptions for these costs, we find that hundreds of cities realize a significant climate co-benefit, along with benefits from reduced traffic congestion and local air pollution, which have traditionally motivated subway construction. Under more moderate assumptions, we find that, on climate grounds alone, hundreds of cities realize high enough social rates of return to warrant subway construction.

Inter-regional environmental inequality under lasting pandemic exacerbated by residential response

Pandemics greatly affect transportation, economic and household activities and their associated air pollutant emissions. In less affluent regions, household energy use is often the dominant pollution source and is sensitive to the affluence change caused by a persisting pandemic. Air quality studies on COVID-19 have shown declines in pollution levels over industrialized regions as an immediate response to pandemic-caused lockdown and weakened economy. Yet few have considered the response of residential emissions to altered household affluence and energy choice supplemented by social distancing. Here we quantify the potential effects of long-term pandemics on ambient fine particulate matter pollution (PM_2.5) and resulting premature mortality worldwide, by comprehensively considering the changes in transportation, economic production and household energy use. We find that a persisting COVID-like pandemic would reduce the global gross domestic product by 10.9 % and premature mortality related to black carbon, primary organic aerosols and secondary inorganic aerosols by 9.5 %. The global mortality decline would reach 13.0 % had the response of residential emissions been excluded. Among the 13 aggregated regions worldwide, the least affluent regions exhibit the greatest fractional economic losses with no comparable magnitudes of mortality reduction. This is because their weakened affluence would cause switch to more polluting household energy types on top of longer stay-at-home time, largely offsetting the effect of reduced transportation and economic production. International financial, technological and vaccine aids could reduce such environmental inequality.

Aircraft and satellite observations reveal historical gap between top-down and bottom-up CO2 emissions from Canadian oil sands

Measurement-based estimates of greenhouse gas (GHG) emissions from complex industrial operations are challenging to obtain, but serve as an important, independent check on inventory-reported emissions. Such top-down estimates, while important for oil and gas (O&G) emissions globally, are particularly relevant for Canadian oil sands (OS) operations, which represent the largest O&G contributor to national GHG emissions. We present a multifaceted top-down approach for estimating CO₂ emissions that combines aircraft-measured CO₂/NO_x emission ratios (ERs) with inventory and satellite-derived NO_x emissions from Ozone Monitoring Instrument (OMI) and TROPOspheric Ozone Monitoring Instrument (TROPOMI) and apply it to the Athabasca Oil Sands Region (AOSR) in Alberta, Canada. Historical CO₂ emissions were reconstructed for the surface mining region, and average top-down estimates were found to be >65% higher than facility-reported, bottom-up estimates from 2005 to 2020. Higher top-down vs. bottom-up emissions estimates were also consistently obtained for individual surface mining and in situ extraction facilities, which represent a growing category of energy-intensive OS operations. Although the magnitudes of the measured discrepancies vary between facilities, they combine such that the observed reporting gap for total AOSR emissions is ≥(31 ± 8) Mt for each of the last 3 years (2018-2020). This potential underestimation is large and broadly highlights the importance of continued review and refinement of bottom-up estimation methodologies and inventories. The ER method herein offers a powerful approach for upscaling measured facility-level or regional fossil fuel CO₂ emissions by taking advantage of satellite remote sensing observations.

Recent Progress in Atmospheric Chemistry Research in China: Establishing a Theoretical Framework for the "Air Pollution Complex"

Atmospheric chemistry research has been growing rapidly in China in the last 25 years since the concept of the "air pollution complex" was first proposed by Professor Xiaoyan TANG in 1997. For papers published in 2021 on air pollution (only papers included in the Web of Science Core Collection database were considered), more than 24 000 papers were authored or co-authored by scientists working in China. In this paper, we review a limited number of representative and significant studies on atmospheric chemistry in China in the last few years, including studies on (1) sources and emission inventories, (2) atmospheric chemical processes, (3) interactions of air pollution with meteorology, weather and climate, (4) interactions between the biosphere and atmosphere, and (5) data assimilation. The intention was not to provide a complete review of all progress made in the last few years, but rather to serve as a starting point for learning more about atmospheric chemistry research in China. The advances reviewed in this paper have enabled a theoretical framework for the air pollution complex to be established, provided robust scientific support to highly successful air pollution control policies in China, and created great opportunities in education, training, and career development for many graduate students and young scientists. This paper further highlights that developing and low-income countries that are heavily affected by air pollution can benefit from these research advances, whilst at the same time acknowledging that many challenges and opportunities still remain in atmospheric chemistry research in China, to hopefully be addressed over the next few decades.

Near-real-time estimation of fossil fuel CO2 emissions from China based on atmospheric observations on Hateruma and Yonaguni Islands, Japan

We developed a near-real-time estimation method for temporal changes in fossil fuel CO₂ (FFCO₂) emissions from China for 3 months [January, February, March (JFM)] based on atmospheric CO₂ and CH₄ observations on Hateruma Island (HAT, 24.06° N, 123.81° E) and Yonaguni Island (YON, 24.47° N, 123.01° E), Japan. These two remote islands are in the downwind region of continental East Asia during winter because of the East Asian monsoon. Previous studies have revealed that monthly averages of synoptic-scale variability ratios of atmospheric CO₂ and CH₄ (ΔCO₂/ΔCH₄) observed at HAT and YON in JFM are sensitive to changes in continental emissions. From the analysis based on an atmospheric transport model with all components of CO₂ and CH₄ fluxes, we found that the ΔCO₂/ΔCH₄ ratio was linearly related to the FFCO₂/CH₄ emission ratio in China because calculating the variability ratio canceled out the transport influences. Using the simulated linear relationship, we converted the observed ΔCO₂/ΔCH₄ ratios into FFCO₂/CH₄ emission ratios in China. The change rates of the emission ratios for 2020-2022 were calculated relative to those for the preceding 9-year period (2011-2019), during which relatively stable ΔCO₂/ΔCH₄ ratios were observed. These changes in the emission ratios can be read as FFCO₂ emission changes under the assumption of no interannual variations in CH₄ emissions and biospheric CO₂ fluxes for JFM. The resulting average changes in the FFCO₂ emissions in January, February, and March 2020 were 17 ± 8%, - 36 ± 7%, and - 12 ± 8%, respectively, (- 10 ± 9% for JFM overall) relative to 2011-2019. These results were generally consistent with previous estimates. The emission changes for January, February, and March were 18 ± 8%, - 2 ± 10%, and 29 ± 12%, respectively, in 2021 (15 ± 10% for JFM overall) and 20 ± 9%, - 3 ± 10%, and - 10 ± 9%, respectively, in 2022 (2 ± 9% for JFM overall). These results suggest that the FFCO₂ emissions from China rebounded to the normal level or set a new high record in early 2021 after a reduction during the COVID-19 lockdown. In addition, the estimated reduction in March 2022 might be attributed to the influence of a new wave of COVID-19 infections in Shanghai.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s40645-023-00542-6.

Air quality improvement through vehicle electrification in Hainan province, China

To improve the ecological environment, provinces in China have set ambitious goals for the electrification of fossil-fuel-powered vehicles (FVs) and the promotion of electric vehicles (EVs). Hainan is the first province to propose a clean energy target that schedules the banning of new FVs sales from 2030. Therefore, Hainan is a good case study to illustrate how this policy might improve regional air quality over the coming years. This study first developed an anthropogenic emission inventory of seven major air pollutants in 2017 in Hainan. The total emissions of CO, NOx, NH3, volatile organic compounds (VOCs), PM10 and PM2.5 and SO2 in 2017 were estimated as 247.56, 69.61, 61.87, 41.38, 37.02, 19.82, and 8.55 kt, respectively. Using the developed emission inventory, multiple scenarios of economic development were considered to assess the benefits to air quality from Hainan's goal of electrification. In comparison with 2017, the reductions in emissions of SO2, NOx, CO, PM10, PM2.5, VOCs, and NH3 by 2045 were projected to be 5.45 (11.11%), 275.07 (57.32%), 675.51 (34.07%), 8.39 (5.73%), 7.73 (8.24%), 81.15 (9.76%), and 4.89 (0.91%) kt, respectively, under the all-electric vehicle scenarios. These results indicate that this policy will not only reduce the emission of air pollutants but also avoid complex O3 pollution in the future. The findings of this work elucidate the effects of vehicle electrification policies on regional air quality and provide scientific support for policymakers in developing pollution control strategies.

Changes in Air Quality during the Period of COVID-19 in China

This paper revisits the heterogeneous impacts of COVID-19 on air quality. For different types of Chinese cities, we analyzed the different degrees of improvement in the concentrations of six air pollutants (PM2.5, PM10, SO2, NO2, CO, and O3) during COVID-19 by analyzing the predictivity of air quality. Specifically, we divided the sample into three groups: cities with severe outbreaks, cities with a few confirmed cases, and cities with secondary outbreaks. Ensemble empirical mode decomposition (EEMD), recursive plots (RPs), and recursive quantitative analysis (RQA) were used to analyze these heterogeneous impacts and the predictivity of air quality. The empirical results indicated the following: (1) COVID-19 did not necessarily improve air quality due to factors such as the rebound effect of consumption, and its impacts on air quality were short-lived. After the initial outbreak, NO2, CO, and PM2.5 emissions declined for the first 1-3 months. (2) For the cities with severe epidemics, air quality was improved, but for the cities with second outbreaks, air quality was first enhanced and then deteriorated. For the cities with few confirmed cases, air quality first deteriorated and then improved. (3) COVID-19 changed the stability of the air quality sequence. The predictability of the air quality index (AQI) declined in cities with serious epidemic situations and secondary outbreaks, but for the cities with a few confirmed cases, the AQI achieved a stable state sooner. The conclusions may facilitate the analysis of differences in air quality evolution characteristics and fluctuations before and after outbreaks from a quantitative perspective.

Wetland emission and atmospheric sink changes explain methane growth in 2020

Atmospheric methane growth reached an exceptionally high rate of 15.1 ± 0.4 parts per billion per year in 2020 despite a probable decrease in anthropogenic methane emissions during COVID-19 lockdowns1. Here we quantify changes in methane sources and in its atmospheric sink in 2020 compared with 2019. We find that, globally, total anthropogenic emissions decreased by 1.2 ± 0.1 teragrams of methane per year (Tg CH4 yr-1), fire emissions decreased by 6.5 ± 0.1 Tg CH4 yr-1 and wetland emissions increased by 6.0 ± 2.3 Tg CH4 yr-1. Tropospheric OH concentration decreased by 1.6 ± 0.2 per cent relative to 2019, mainly as a result of lower anthropogenic nitrogen oxide (NOx) emissions and associated lower free tropospheric ozone during pandemic lockdowns2. From atmospheric inversions, we also infer that global net emissions increased by 6.9 ± 2.1 Tg CH4 yr-1 in 2020 relative to 2019, and global methane removal from reaction with OH decreased by 7.5 ± 0.8 Tg CH4 yr-1. Therefore, we attribute the methane growth rate anomaly in 2020 relative to 2019 to lower OH sink (53 ± 10 per cent) and higher natural emissions (47 ± 16 per cent), mostly from wetlands. In line with previous findings3,4, our results imply that wetland methane emissions are sensitive to a warmer and wetter climate and could act as a positive feedback mechanism in the future. Our study also suggests that nitrogen oxide emission trends need to be taken into account when implementing the global anthropogenic methane emissions reduction pledge5.

Assessing the "negative effect" and "positive effect" of COVID-19 in China

The COVID-19 pandemic lockdowns led to a sharp drop in socio-economic activities in China in 2020, including reductions in fossil fuel use, industry productions, and traffic volumes. China's economy suffered a serious negative effect from COVID-19. However, there is a "positive effect" on CO₂ emissions reduction. Here, for the first time, this paper constructs a new model named "Weighted Multi-regional Hypothetical Extraction Method (WMHEM)" based on a multiregional input-output model. It not only solves the problems of traditional HEM methods such as improper use of assumptions, excessive reliance on industry intermediate input, but also accurately reflects the impact of external shocks on the inter-industry linkages. By using the monthly economic data of each provinces in China during COVID-19 (except Hong Kong，Macao and Taiwan) an the latest Multi-regional input-output tables, the "economic negative effect" and "CO₂ emission positive effect" under COVID-19 in China are measured. Results show that COVID-19 lockdown was estimated to have reduced China's CO₂ emissions substantially between January and March in 2020, with the largest reductions in February. With the spread of coronavirus controlled, China's CO₂ emissions rebounded in April. In addition, key emission reduction sectors and key development encouraged sectors are selected by combining "economic negative effect" and "CO₂ emission positive effect" during COVID-19. Therefore, policies recommendations are put forward based on forward and backward linkages respectively which are from two ends of the supply chain to turn pandemic-related CO₂ emissions declines into firm climate action.

The influence of urban form compactness on CO2 emissions and its threshold effect: Evidence from cities in China

Although compact urban form plays an important role in constraining emissions of carbon dioxide (CO₂), the boundary for the impact of compact urban form on these emissions has nevertheless received little attention. We consequently applied the entropy weight method and several key landscape metrics to a dataset from 295 cities in China to quantify urban form compactness (UFC) between 2000 and 2015. The STIRPAT model then was employed to estimate the impact of UFC on CO₂ emissions, and a panel threshold regression model was used to estimate threshold effects capable of limiting the impact of compact urban form on emissions. Although CO₂ emissions increased sharply over the 15-year study period, a significant negative relationship between UFC and CO₂ emissions was detected. Two thresholds of UFC were detected, and this allowed three categories to be differentiated: before the first threshold, between the two thresholds, and after the second threshold. These categories were respectively associated with no impact, strong impact, and weak impact of UFC on reduction of carbon emissions in the 295 cities. Carbon emissions reduction consequently becomes effective when the UFC exceeds the first threshold and effectiveness persists but at a reduced level when the UFC exceeds the second threshold. Further temporal analysis confirmed that an increasing number of mostly small- and medium-sized cities could constrain their future carbon emissions by adopting a compact urban form. Thus, government policies should emphasize UFC as a strategy to reduce CO₂ emissions. Moreover, by defining the range of compact urban form that has the greatest impact on CO₂ emissions, our study deepens the overall understanding of the influence of UFC on carbon emission reductions, so as to make contributions to the design of low-carbon cities.

Air pollution rebound and different recovery modes during the period of easing COVID-19 restrictions

Although COVID-19 lockdown policies have improved air quality in numerous countries, there is a lack of empirical evidence on the extent to which recovery has resulted in air pollution rebound, and the differences and similarities among regions' recovery modes during the period of easing COVID-19 restrictions. Here, we used daily air quality data and the recovery index constructed by a city-pair inflow index for 119 cities in China to quantify the impact of recovery on air pollution from March 2 to October 30, 2020. Findings show that recovery has significantly increased air pollution. When the recovery level increased by 10 %, the concentration of PM_2.5, SO₂, and NO₂ respectively deteriorated by 1.10, 0.33, 1.25 μg/m³, and the average growth rates of three air pollutants were about 3 %-6 %. Moreover, we used the counterfactual framework and time series clustering with wavelet transform to cluster the rebound trajectory of air pollution for 17 provinces into five recovery modes. Results show that COVID-19 has further intensified regional differentiations in economic development ability and green recovery trend. Three northwestern provinces dependent on their resource endowments belong to energy-intensive recovery mode, which have experienced a sharp rebound of air pollution for two months, thereby making green recovery more challenging to achieve. Three regions with a diversified industrial structure are in industrial-restructuring recovery mode, which has effectively returned to a normal level through adjusting industrial structure and technological innovation. Owing to local policies and the outbreak of COVID-19 in other countries, six provinces in policy-oriented and international trade-oriented recovery modes have not fully recovered to the level without COVID-19 until October 2020. The result highlights the importance of diversifying industrial structure, technological innovation, policy flexibility and industrial upgrading for different recovery modes to achieve long-term green recovery in the future.

New sights on the impact of spatial composition of production factors for socioeconomic recovery in the post-epidemic era: a case study of cities in central and eastern China

The COVID-19 pandemic led to a sharp economic contraction. A comprehensive understanding of the relationship between the spatial composition of production factor (SCPF) and socioeconomic recovery is still missing. Here, we applied the contrasting status of nitrogen dioxide (NO₂) concentrations in cities in central and eastern China as natural laboratories. From the perspective of the spatial composition of land (SCL) and the dependence on the inflow population (DIP), four quantifiable indicators (resilience, impact, sensitivity, recovery speed) were used to analyze the adaptability of SCPF to the epidemic lockdown. The results indicate that appropriate SCPF is a prerequisite for a complete "land-population-industry" nexus. The built-up area proportion is below 74.38%, with higher adaptability to epidemic shocks. The range of rural built-up proportion conducive to economic recovery is 10.18%-15.18%. The proportions of various land types inside the city's defense unit should also be constrained. Similarly, DIP is advocated to be maintained below 17.5%. For urban-rural fringe areas, the response to epidemic prevention and socioeconomic recovery are rapid. This observation-driven study indicated that COVID-19 is a shocking reminder for policymakers, to improve the socioeconomic recovery ability from the spatial composition of production factor perspective in the post-COVID-19 era.

Sensing global changes in local patterns of energy consumption in cities during the early stages of the COVID-19 pandemic

COVID-19, and the wider social and economic impacts that a global pandemic entails, led to unprecedented reductions in energy consumption globally. Whilst estimates of changes in energy consumption have emerged at the national scale, detailed sub-regional estimates to allow for global comparisons are less developed. Using night-time light satellite imagery from December 2019-June 2020 across 50 of the world's largest urban conurbations, we provide high resolution estimates (450 m2) of spatio-temporal changes in urban energy consumption in response to COVID-19. Contextualising this imagery with modelling based on indicators of mobility, stringency of government response, and COVID-19 rates, we provide novel insights into the potential drivers of changes in urban energy consumption during a global pandemic. Our results highlight the diversity of changes in energy consumption between and within cities in response to COVID-19, moderating dominant narratives of a shift in energy demand away from dense urban areas. Further modelling highlights how the stringency of the government's response to COVID-19 is likely a defining factor in shaping resultant reductions in urban energy consumption.

Anthropogenic CO2 emission reduction during the COVID-19 pandemic in Nanchang City, China

China is the largest CO2 emitting country on Earth. During the COVID-19 pandemic, China implemented strict government control measures on both outdoor activity and industrial production. These control measures, therefore, were expected to significantly reduce anthropogenic CO2 emissions. However, large discrepancies still exist in the estimated anthropogenic CO2 emission reduction rate caused by COVID-19 restrictions, with values ranging from 10% to 40% among different approaches. Here, we selected Nanchang city, located in eastern China, to examine the impact of COVID-19 on CO2 emissions. Continuous atmospheric CO2 and ground-level CO observations from January 1st to April 30th, 2019 to 2021 were used with the WRF-STILT atmospheric transport model and a priori emissions. And a multiplicative scaling factor and Bayesian inversion method were applied to constrain anthropogenic CO2 emissions before, during, and after the COVID-19 pandemic. We found a 37.1-40.2% emission reduction when compared to the COVID-19 pandemic in 2020 with the same period in 2019. Carbon dioxide emissions from the power industry and manufacturing industry decreased by 54.5% and 18.9% during the pandemic period. The power industry accounted for 73.9% of total CO2 reductions during COVID-19. Further, emissions in 2021 were 14.3-14.9% larger than in 2019, indicating that economic activity quickly recovered to pre-pandemic conditions.

CO2 emissions persistence: Evidence using fractional integration

The main cause of climate change are carbon dioxide emissions. In the context of the COVID-19 pandemic, the number of emissions has been significantly reduced for the first time in many years. Now it is necessary to answer the question of whether CO2 emissions are stationary or not, because the results will let us know whether environmental policies have to be strengthened rather than relaxed in intensity. To this end, this paper investigates the persistence in CO2 emissions in a group of countries to determine if shocks in the series have permanent or transitory effects. The results, based on fractional integration indicate evidence of mean reversion, with values of the differencing parameter constrained between 0 and 1 in all cases, independently of the assumption made about the error term (white noise or autocorrelation). Focusing on the areas under examination, it is obtained that the EU27+UK, Japan and the US present the lowest degrees of integration, while Russia, China and India display the highest values. Decreasing time trends are only observed for the EU27+UK and US.

Co-benefits of CO2 emission reduction from China's clean air actions between 2013-2020

Climate change mitigation measures can yield substantial air quality improvements while emerging clean air measures in developing countries can also lead to CO₂ emission mitigation co-benefits by affecting the local energy system. Here, we evaluate the effect of China's stringent clean air actions on its energy use and CO₂ emissions from 2013-2020. We find that widespread phase-out and upgrades of outdated, polluting, and inefficient combustion facilities during clean air actions have promoted the transformation of the country's energy system. The co-benefits of China's clean air measures far outweigh the additional CO₂ emissions of end-of-pipe devices, realizing a net accumulative reduction of 2.43 Gt CO₂ from 2013-2020, exceeding the accumulated CO₂ emission increase in China (2.03 Gt CO₂) during the same period. Our study indicates that China's efforts to tackle air pollution induce considerable climate benefit, and measures with remarkable CO₂ reduction co-benefits deserve further attention in future policy design.

Integrated process analysis retrieval of changes in ground-level ozone and fine particulate matter during the COVID-19 outbreak in the coastal city of Kannur, India

The Community Multi-Scale Air Quality (CMAQ) model was applied to evaluate the air quality in the coastal city of Kannur, India, during the 2020 COVID-19 lockdown. From the Pre1 (March 1-24, 2020) period to the Lock (March 25-April 19, 2020) and Tri (April 20-May 9, 2020) periods, the Kerala state government gradually imposed a strict lockdown policy. Both the simulations and observations showed a decline in the PM2.5 concentrations and an enhancement in the O3 concentrations during the Lock and Tri periods compared with that in the Pre1 period. Integrated process rate (IPR) analysis was employed to isolate the contributions of the individual atmospheric processes. The results revealed that the vertical transport from the upper layers dominated the surface O3 formation, comprising 89.4%, 83.1%, and 88.9% of the O3 sources during the Pre1, Lock, and Tri periods, respectively. Photochemistry contributed negatively to the O3 concentrations at the surface layer. Compared with the Pre1 period, the O3 enhancement during the Lock period was primarily attributable to the lower negative contribution of photochemistry and the lower O3 removal rate by horizontal transport. During the Tri period, a slower consumption of O3 by gas-phase chemistry and a stronger vertical import from the upper layers to the surface accounted for the increase in O3. Emission and aerosol processes constituted the major positive contributions to the net surface PM2.5, accounting for a total of 48.7%, 38.4%, and 42.5% of PM2.5 sources during the Pre1, Lock, and Tri periods, respectively. The decreases in the PM2.5 concentrations during the Lock and Tri periods were primarily explained by the weaker PM2.5 production from emission and aerosol processes. The increased vertical transport rate of PM2.5 from the surface layer to the upper layers was also a reason for the decrease in the PM2.5 during the Lock periods.

Daily Emission Patterns of Coal-Fired Power Plants in China Based on Multisource Data Fusion

Daily emission estimates are essential for tracking the dynamic changes in emission sources. In this work, we estimate daily emissions of coal-fired power plants in China during 2017-2020 by combining information from the unit-based China coal-fired Power plant Emissions Database (CPED) and real-time measurements from continuous emission monitoring systems (CEMS). We develop a step-by-step method to screen outliers and impute missing values for data from CEMS. Then, plant-level daily profiles of flue gas volume and emissions obtained from CEMS are coupled with annual emissions from CPED to derive daily emissions. Reasonable agreement is found between emission variations and available statistics (i.e., monthly power generation and daily coal consumption). Daily power emissions are in the range of 6267-12,994, 0.4-1.3, 6.5-12.0, and 2.5-6.8 Gg for CO₂, PM_2.5, NO _x , and SO₂, respectively, with high emissions in winter and summer caused by heating and cooling demand. Our estimates can capture sudden decreases (e.g., those associated with COVID-19 lockdowns and short-term emission controls) or increases (e.g., those related to a drought) in daily power emissions during typical socioeconomic events. We also find that weekly patterns from CEMS exhibit no obvious weekend effect compared to those in previous studies. The daily power emissions will help to improve chemical transport modeling and facilitate policy formulation.

Tracking PM2.5 and O3 Pollution and the Related Health Burden in China 2013-2020

Based on the exposure data sets from the Tracking Air Pollution in China (TAP, http://tapdata.org.cn/), we characterized the spatiotemporal variations in PM_2.5 and O₃ exposures and quantified the long- and short-term exposure related premature deaths during 2013-2020 with respect to the two-stage clean air actions (2013-2017 and 2018-2020). We find a 48% decrease in national PM_2.5 exposure during 2013-2020, although the decrease rate has slowed after 2017. At the same time, O₃ pollution worsened, with the average April-September O₃ exposure increased by 17%. The improved air quality led to 308 thousand and 16 thousand avoided long- and short-term exposure related deaths, respectively, in 2020 compared to the 2013 level, which was majorly attributed to the reduction in ambient PM_2.5 concentration. It is also noticed that with smaller PM_2.5 reduction, the avoided long-term exposure associated deaths in 2017-2020 (13%) was greater than that in 2013-2017 (9%), because the exposure-response curve is nonlinear. As a result of the efforts in reducing PM_2.5-polluted days with the daily average PM_2.5 higher than 75 μg/m³ and the considerable increase in O₃-polluted days with the daily maximum 8 h average O₃ higher than 160 μg/m³, deaths attributable to the short-term O₃ exposure were greater than those due to PM_2.5 exposure since 2018. Future air quality improvement strategies for the coordinated control of PM_2.5 and O₃ are urgently needed.

Strategy evaluation and optimization with an artificial society towards a Pareto optimum

Strategy evaluation and optimization in response to troubling urban issues has become a challenging issue due to increasing social uncertainty, unreliable predictions, and poor decision-making. To address this problem, we propose a universal computational experiment framework with a fine-grained artificial society that is integrated with data-based models. The purpose of the framework is to evaluate the consequences of various combinations of strategies geared towards reaching a Pareto optimum with regards to efficacy versus costs. As an example, by modeling coronavirus 2019 mitigation, we show that Pareto frontier nations could achieve better economic growth and more effective epidemic control through the analysis of real-world data. Our work suggests that a nation’s intervention strategy could be optimized based on the measures adopted by Pareto frontier nations through large-scale computational experiments. Our solution has been validated for epidemic control, and it can be generalized to other urban issues as well.

Novel quantification of regional fossil fuel CO2 reductions during COVID-19 lockdowns using atmospheric oxygen measurements

It is not currently possible to quantify regional-scale fossil fuel carbon dioxide (ffCO₂) emissions with high accuracy in near real time. Existing atmospheric methods for separating ffCO₂ from large natural carbon dioxide variations are constrained by sampling limitations, so that estimates of regional changes in ffCO₂ emissions, such as those occurring in response to coronavirus disease 2019 (COVID-19) lockdowns, rely on indirect activity data. We present a method for quantifying regional signals of ffCO₂ based on continuous atmospheric measurements of oxygen and carbon dioxide combined into the tracer "atmospheric potential oxygen" (APO). We detect and quantify ffCO₂ reductions during 2020-2021 caused by the two U.K. COVID-19 lockdowns individually using APO data from Weybourne Atmospheric Observatory in the United Kingdom and a machine learning algorithm. Our APO-based assessment has near-real-time potential and provides high-frequency information that is in good agreement with the spread of ffCO₂ emissions reductions from three independent lower-frequency U.K. estimates.

What is the impact of COVID-19 pandemic on global carbon emissions?

The coronavirus 2019 (COVID 19, or SARS-CoV-2) pandemic that started in December 2019 has caused an unprecedented impact in most countries globally and continues to threaten human lives worldwide. The COVID-19 and strict lockdown measures have had adverse effects on human health and national economies. These lockdown measures have played a critical role in improving air quality, water quality, and the ozone layer and reducing greenhouse gas emissions. Using Soil Moisture Active Passive (SMAP) Level 4 carbon (SMAP LC4) satellite products, this study investigated the impacts of COVID-19 lockdown measures on annual carbon emissions globally, focusing on 47 greatly affected countries and their 105 cities by December 2020. It is shown that while the lockdown measures significantly reduced carbon emissions globally, several countries and cities observed this reduction as temporary because strict lockdown measures were not imposed for extended periods in 2020. Overall, the total carbon emissions of select 184 countries reduced by 438 Mt in 2020 than in 2019. Since the global economic activities are slowly expected to return to the non-COVID-19 state, the reduction in carbon emissions during the pandemic will not be sustainable in the long run. For sustainability, concerned authorities have to put significant efforts to change transportation, climate, and environmental policies globally that fuel carbon emissions. Overall, the presented results provide directions to the stakeholders and policymakers to develop and implement measures to control carbon emissions for a sustainable environment.

Long-term ecological research and the COVID-19 anthropause: A window to understanding social-ecological disturbance

The period of disrupted human activity caused by the COVID-19 pandemic, coined the "anthropause," altered the nature of interactions between humans and ecosystems. It is uncertain how the anthropause has changed ecosystem states, functions, and feedback to human systems through shifts in ecosystem services. Here, we used an existing disturbance framework to propose new investigation pathways for coordinated studies of distributed, long-term social-ecological research to capture effects of the anthropause. Although it is still too early to comprehensively evaluate effects due to pandemic-related delays in data availability and ecological response lags, we detail three case studies that show how long-term data can be used to document and interpret changes in air and water quality and wildlife populations and behavior coinciding with the anthropause. These early findings may guide interpretations of effects of the anthropause as it interacts with other ongoing environmental changes in the future, particularly highlighting the importance of long-term data in separating disturbance impacts from natural variation and long-term trends. Effects of this global disturbance have local to global effects on ecosystems with feedback to social systems that may be detectable at spatial scales captured by nationally to globally distributed research networks.

Can Cooperative Supports and Adoption of Improved Technologies Help Increase Agricultural Income? Evidence from a Recent Study

Global climate change may result in major environmental issues that have already affected and will continue to affect agricultural sector in the future. A continuing effort to utilize and adopt new agricultural technologies is necessary to mitigate climate change and increase agricultural income. Agricultural cooperatives are gradually being used in emerging countries to encourage improved technology and reduce food insecurity and poverty. This research analyses the influence of cooperative supports (CS) and technology adoption (TA) on agricultural income in Pakistan. It applied the propensity score matching (PSM) technique to evaluate the productivity on survey data from 498 wheat growers to conduct counterfactual analysis for farmers in Pakistan. In addition, a dual selection model (DSM) was applied to resolve the bias in sample selection caused by observed and unobserved aspects of survey data. The results showed that, contrasted with non-membership and non-adopters, growers who joined CS and TA could boost agricultural income by 2.78% and 2.35%, respectively. Stimulatingly, the influence of less-revenue farmers on agricultural income was more substantial than that of high-income farmers. Agricultural income of growers who attached cooperatives and adopted improved agricultural technology enhanced by 5.45% and 4.51%, respectively. These results, among others, emphasize the optimistic role of growing CS and TA in boosting wheat farmer’s income. The findings of the study showed strong relationships among education, age, skill, training, gender with CS and TA, and agricultural income. Overall, this study can be helpful in conducting similar studies in other emerging/developing countries for wheat or any other crop growers.

Impacts of COVID-19 on Energy Expenditures of Local Self-Government Units in Poland

Measures taken by the public administration to prevent the spread of the COVID-19 pandemic have led to drastic consequences for the economy. The full identification of its effects is hindered due to the delay in publishing the results of public statistics. The use of financial reports prepared by self-government authorities of all municipalities in Poland made it possible to obtain preemptive information in relation to the public statistics regarding the impact of COVID-19-related limitations on the energy expenditures incurred by local government units (LGUs), as well as an assessment of to what extent the LGUs had rationalized the energy consumption. By contrast, data from reports of energy companies made it possible to determine the impact of restrictions arising from the pandemic on the amount of energy sold and revenues from sales made by these companies. The analyses use indexes of the dynamics of changes in energy prices as well as indexes of the dynamics of changes in energy expenditures incurred by LGUs. Additionally, distributions of these indexes for the populations of municipalities are analyzed. To assess the effect of economic activity on energy expenditures incurred by LGUs, classification trees are utilized. It is established that the total production and sales of energy in Poland, in volume, in each quarter of 2020 were lower than in the corresponding period of the preceding year. However, as a result of an increase in energy prices by approximately 25%, the sales of electric power generating companies, in amounts, were higher in 2020 than in 2019. The increase in energy prices was also a cause of slightly increased total expenditures for purchasing energy in LGUs in Poland, which increased by 2.15% in 2020 compared to 2019. However, a substantial diversity in expenditure indexes was observed. That concerned both total expenditures and expenditures within individual sections of the budgets of municipalities.

Abrupt emissions reductions during COVID-19 contributed to record summer rainfall in China

Record rainfall and severe flooding struck eastern China in the summer of 2020. The extreme summer rainfall occurred during the COVID-19 pandemic, which started in China in early 2020 and spread rapidly across the globe. By disrupting human activities, substantial reductions in anthropogenic emissions of greenhouse gases and aerosols might have affected regional precipitation in many ways. Here, we investigate such connections and show that the abrupt emissions reductions during the pandemic strengthened the summer atmospheric convection over eastern China, resulting in a positive sea level pressure anomaly over northwestern Pacific Ocean. The latter enhanced moisture convergence to eastern China and further intensified rainfall in that region. Modeling experiments show that the reduction in aerosols had a stronger impact on precipitation than the decrease of greenhouse gases did. We conclude that through abrupt emissions reductions, the COVID-19 pandemic contributed importantly to the 2020 extreme summer rainfall in eastern China.

Monitoring the CO2 Emission Trajectory and Reduction Effects by ETS and Its Market Performances for Pre- and Post-pandemic China

The COVID-19 pandemic has caused great shocks on economic activities and carbon emissions. This paper aims to monitor the CO2 emission trajectory in China before and after the pandemic outbreak, and analyze the emission reduction effects by ETS and its market performances, which are important determinants underlying the trajectory and key drivers for emission reductions. We firstly find out a rather consistent trajectory of CO2 emissions in pre- and post-pandemic China over a 2-year time horizon, using the near-real-time datasets of daily CO2 emissions by Carbon Monitor and applying the Cox-Stuart trend test and mean equality test. We then examine the emission reduction effects by China's carbon ETS and its pilot market performances, using the methodologies of DID and PSM-DID as well as pre-pandemic region-level emission datasets by CEADs. Furthermore, it's found that the ETS pilot markets, which are immature with defects, have been performing more vulnerably in terms of liquidity and transaction continuity under pandemic shocks, thus undermining the emission reduction effects by ETS. These findings are providing insights into further mechanism design of the carbon ETS to the end of steady emission reductions even under shocks for post-pandemic China. It's of particular importance now that the nationwide market has been launched and needs to be enhanced based on lessons learned.

Potency of the pandemic on air quality: An urban resilience perspective

Since the outbreak of COVID-19 pandemic, the lockdown policy across the globe has brought improved air quality while fighting against the coronavirus. After the closure, urban air quality was subject to emission reduction of air pollutants and rebounded to the previous level after the potency period of recession. Different response patterns exhibit divergent sensitivities of urban resilience in regard to air pollution. In this paper, we investigate the post-lockdown AQI values of 314 major cities in China to analyse their differential effects on the influence factors of urban resilience. The major findings of this paper include: 1) Cities exhibit considerable range of resilience with their AQI values which are dropped by 21.1% per day, took 3.97 days on average to reach the significantly decreased trough point, and reduced by 49.3% after the lockdown initiatives. 2) Mega cities and cities that locate as the focal points of transportation for nearby provinces, together with those with high AQI values, were more struggling to maintain a good air quality with high rebounds. 3) Urban resilience shows divergent spatial sensitivities to air pollution controls. Failing to consider multi-dimensional factors besides from geomorphological and economical activities could lead to uneven results of environmental policies. The results unveil key drivers of urban air pollution mitigation, and provide valuable insights for prediction of air quality in response to anthropogenic interference events under different macro-economic contexts. Research findings in this paper can be adopted for prevention and management of public health risks from the perspective of urban resilience and environmental management in face of disruptive outbreak events in future.

COVID-19 lockdown closures of emissions sources in India: Lessons for air quality and climate policy

Reduced anthropogenic activities during the COVID-19 pandemic caused significant reductions in ambient fine particulate matter (PM_2.5), SO₂ and NO_x concentrations across India. However, tropospheric O₃ concentrations spiked over many urban regions. Moreover, reductions in SO₂ and NO_x (atmospheric cooling agents) emissions unmask heating exerted by warming forcers. Basing governmental guidelines, we model daily emissions reductions in CO₂ and short-lived climate forcers (SLCFs) during different lockdown periods using bottom-up regional emission inventory. The transport sector, with maximum level of closure, followed by power plants and industry reduced nearly -50% to -75% emissions of CO₂, primary PM_2.5, SO₂ and NO_x, while warming SLCFs (black carbon, CH₄, CO and non-methane VOCs) showed insignificant reduction from continuing activity in residential and agricultural sectors. Consequently, the analysis indicates that reduction in the emission ratio of NO_x to NMVOC coincided spatially with observed increases in O₃, consistent with reduced uptake of O₃ from night-time NO_x reactions. Also, similar reductions, occurring for longer timescales (say, a year), can potentially increase the annual warming rate over India from the positive regional temperature response, estimated using climate metric. Further, by linking ongoing policies to sectoral reductions during lockdown, this study shows that the relative pacing of implementation among policies is crucial to avoid counter-productive results. A key policy recommendation is introduction and improving efficacy of programs targeting reduction of NMVOC and warming SLCF emissions (shifts away from biomass cooking technologies, household electrification and curbing open burning of crop residues), must precede the strengthening of policies targeting NO_x and SO₂ dominated sectors.

Difference and Cluster Analysis on the Carbon Dioxide Emissions in China During COVID-19 Lockdown via a Complex Network Model

The continuous increase of carbon emissions is a serious challenge all over the world, and many countries are striving to solve this problem. Since 2020, a widespread lockdown in the country to prevent the spread of COVID-19 escalated, severely restricting the movement of people and unnecessary economic activities, which unexpectedly reduced carbon emissions. This paper aims to analyze the carbon emissions data of 30 provinces in the 2020 and provide references for reducing emissions with epidemic lockdown measures. Based on the method of time series visualization, we transform the time series data into complex networks to find out the hidden information in these data. We found that the lockdown would bring about a short-term decrease in carbon emissions, and most provinces have a short time point of impact, which is closely related to the level of economic development and industrial structure. The current results provide some insights into the evolution of carbon emissions under COVID-19 blockade measures and valuable insights into energy conservation and response to the energy crisis in the post-epidemic era.

Identification of NOx hotspots from oversampled TROPOMI NO2 column based on image segmentation method

Satellite-based measures of NO₂ have become increasingly available for resolving the limitation on insufficient spatial and temporal coverage of ground-level monitoring networks. Oversampled NO₂ column density can obtain more detailed features of NO₂ column with a spatial resolution as high as 2 km × 2 km, while it is still challenging to identify hotspots of NO_x pollution plume in city-scale due to background interference. In this study, we proposed a method for detecting the NO_x hotspot grids from oversampled satellite NO₂ column based on the image segmentation method, and identifying major source types using Term frequency-inverse document frequency (TF-IDF). A fractal model was used to evaluate and eliminate the background portion of the NO₂ column and an adaptive threshold method was adopted to identify the region of interest (ROI) of local hotspot NO₂ column. Hot-grid index, counting the frequency of NO₂ hotspot ROI in each grid, was conducted to identify the hotspot grids. TF-IDF was used to semantically analyze the major source types of NO₂ hotspot grids. Taking Central and Eastern China as the studied domain, the hotspot grids of NO₂ and the relevant major source types were identified based on the proposed method. The major non-road mobile sources (such as Beijing Capital International Airport), industrial areas (such as Caofeidian Industrial Park) and urban areas were clearly distinguished. The power plant, Coke and Iron and Steel were identified as major source types in the whole year in the corresponding NO_x hotspot grids. Notably, the identification of hotspot grids indicated a higher probability of a local high-intensity NO_x pollution plume rather than a quantitative NO_x emission; the key source types were the semantic keywords in hotspot grids, which does not mean there were no other exiting emission sources. This proposed method has strong implications on rapidly identifying the NO_x hotspot grids based on oversampled TROPOMI NO₂ column and the list of industrial enterprises.