Regional impacts of COVID-19 on carbon dioxide detected worldwide from space

Sustained Reductions of Bay Area CO2 Emissions 2018-2022

Cities represent a significant and growing portion of global carbon dioxide (CO2) emissions. Quantifying urban emissions and trends over time is needed to evaluate the efficacy of policy targeting emission reductions as well as to understand more fundamental questions about the urban biosphere. A number of approaches have been proposed to measure, report, and verify (MRV) changes in urban CO2 emissions. Here we show that a modest capital cost, spatially dense network of sensors, the Berkeley Environmental Air Quality and CO2 Network (BEACO2N), in combination with Bayesian inversions, result in a synthesis of measured CO2 concentrations and meteorology to yield an improved estimate of CO2 emissions and provide a cost-effective and accurate assessment of CO2 emissions trends over time. We describe nearly 5 years of continuous CO2 observations (2018-2022) in a midsized urban region (the San Francisco Bay Area). These observed concentrations constrain a Bayesian inversion that indicates the interannual trend in urban CO2 emissions in the region has been a modest decrease at a rate of 1.8 ± 0.3%/year. We interpret this decrease as primarily due to passenger vehicle electrification, reducing on-road emissions at a rate of 2.6 ± 0.7%/year.

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.

A multi-satellite framework to rapidly evaluate extreme biosphere cascades: The Western US 2021 drought and heatwave

The increasing frequency and intensity of climate extremes and complex ecosystem responses motivate the need for integrated observational studies at low latency to determine biosphere responses and carbon-climate feedbacks. Here, we develop a satellite-based rapid attribution workflow and demonstrate its use at a 1-2-month latency to attribute drivers of the carbon cycle feedbacks during the 2020-2021 Western US drought and heatwave. In the first half of 2021, concurrent negative photosynthesis anomalies and large positive column CO2 anomalies were detected with satellites. Using a simple atmospheric mass balance approach, we estimate a surface carbon efflux anomaly of 132 TgC in June 2021, a magnitude corroborated independently with a dynamic global vegetation model. Integrated satellite observations of hydrologic processes, representing the soil-plant-atmosphere continuum (SPAC), show that these surface carbon flux anomalies are largely due to substantial reductions in photosynthesis because of a spatially widespread moisture-deficit propagation through the SPAC between 2020 and 2021. A causal model indicates deep soil moisture stores partially drove photosynthesis, maintaining its values in 2020 and driving its declines throughout 2021. The causal model also suggests legacy effects may have amplified photosynthesis deficits in 2021 beyond the direct effects of environmental forcing. The integrated, observation framework presented here provides a valuable first assessment of a biosphere extreme response and an independent testbed for improving drought propagation and mechanisms in models. The rapid identification of extreme carbon anomalies and hotspots can also aid mitigation and adaptation decisions.

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.

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.

The Impact of COVID-19 on Investors’ Investment Intention of Sustainability-Related Investment: Evidence from China

This paper investigates how investors respond to the COVID-19 pandemic, particularly regarding their intention to invest in sustainability-related investment (SRI) funds. We conduct two experiments online with participants who have experience with stock and fund investments. The first one includes 292 participants, which aims to explore investors’ attitudes and investment intention of different sustainability-related components, and the second one includes 432 participants, which aims to examine how the COVID-19 pandemic affects individuals’ attitudes and investment intention. Our results show that investors tend to invest in SRI funds when the threat of the COVID-19 pandemic is salient. Specifically, we find that although investors perceive environmental issues to be more important than economic and social issues, their investment intention of economic-focused SRI funds significantly increases in response to the COVID-19 pandemic threat. These findings suggest that fund managers can focus on particular types of investors when designing SRI funds, such as active investors with a preference for technical analysis and young female investors with a high level of income and education.

Impact of COVID-19 containment and closure policies on tropospheric nitrogen dioxide: A global perspective

The containment and closure policies adopted in attempts to contain the spread of the 2019 coronavirus disease (COVID-19) have impacted nearly every aspect of our lives including the environment we live in. These influences may be observed when evaluating changes in pollutants such as nitrogen dioxide (NO2), which is an important indicator for economic, industrial, and other anthropogenic activities. We utilized a data-driven approach to analyze the relationship between tropospheric NO2 and COVID-19 mitigation measures by clustering regions based on pollution levels rather than constraining the study units by predetermined administrative boundaries as pollution knows no borders. Specifically, three clusters were discovered signifying mild, moderate, and poor pollution levels. The most severely polluted cluster saw significant reductions in tropospheric NO2, coinciding with lockdown periods. Based on the clustering results, qualitative and quantitative analyses were conducted at global and regional levels to investigate the spatiotemporal changes. In addition, panel regression analysis was utilized to quantify the impact of policy measures on the NO2 reduction. This study found that a 23.58 score increase in the stringency index (ranging from 0 to 100) can significantly reduce the NO2 TVCD by 3.2% (p < 0.05) in the poor cluster in 2020, which corresponds to a 13.1% maximum reduction with the most stringent containment and closure policies implemented. In addition, the policy measures of workplace closures and close public transport can significantly decrease the tropospheric NO2 in the poor cluster by 6.7% (p < 0.1) and 4.5% (p < 0.1), respectively. An additional heterogeneity analysis found that areas with higher incomes, CO2 emissions, and fossil fuel consumption have larger NO2 TVCD reductions regarding workplace closures and public transport closures.

On the Detection of COVID-Driven Changes in Atmospheric Carbon Dioxide

We assess the detectability of COVID-like emissions reductions in global atmospheric CO2 concentrations using a suite of large ensembles conducted with an Earth system model. We find a unique fingerprint of COVID in the simulated growth rate of CO2 sampled at the locations of surface measurement sites. Negative anomalies in growth rates persist from January 2020 through December 2021, reaching a maximum in February 2021. However, this fingerprint is not formally detectable unless we force the model with unrealistically large emissions reductions (2 or 4 times the observed reductions). Internal variability and carbon-concentration feedbacks obscure the detectability of short-term emission reductions in atmospheric CO2. COVID-driven changes in the simulated, column-averaged dry air mole fractions of CO2 are eclipsed by large internal variability. Carbon-concentration feedbacks begin to operate almost immediately after the emissions reduction; these feedbacks reduce the emissions-driven signal in the atmosphere carbon reservoir and further confound signal detection.