Factors driving China’s carbon emissions after the COVID-19 outbreak

The outbreak of the coronavirus (COVID-19) may exert profound impacts on China's economic development and 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 the latest input‒output table of China's economy and apply structural decomposition analysis to understand the dynamic changes in the determinants of carbon emissions from 2002 to 2020, specifically the impact of COVID-19 on carbon emissions. We find that the contribution of production structure to carbon emission growth was enlarged due to the pandemic, after a continuous decline since 2007. Lower production efficiency and reliance on carbon intensive inputs indicated the deterioration in production structure. The contribution of per capita consumption to emission growth was decreased because of the economic contraction in the first half of 2020. For policy implications, efforts should be undertaken to increase investment in low-carbon industries and increase the proportion of consumption in GDP to shift the investment-led growth to consumption-led growth for an inclusive and green recovery from the pandemic

Factors driving China’s carbon emissions after the COVID-19 outbreak

The outbreak of the coronavirus (COVID-19) may exert profound impacts on China's economic development and 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 the latest input‒output table of China's economy and apply structural decomposition analysis to understand the dynamic changes in the determinants of carbon emissions from 2002 to 2020, specifically the impact of COVID-19 on carbon emissions. We find that the contribution of production structure to carbon emission growth was enlarged due to the pandemic, after a continuous decline since 2007. Lower production efficiency and reliance on carbon intensive inputs indicated the deterioration in production structure. The contribution of per capita consumption to emission growth was decreased because of the economic contraction in the first half of 2020. For policy implications, efforts should be undertaken to increase investment in low-carbon industries and increase the proportion of consumption in GDP to shift the investment-led growth to consumption-led growth for an inclusive and green recovery from the pandemic

Using crowdsourced data to estimate the carbon footprints of global cities

Cities are at the forefront of the battle against climate change. However, intercity comparisons and responsibility allocations among cities are hindered because cost- and time-effective methods to calculate the carbon footprints of global cities have yet to be developed. Here, we establish a hybrid method integrating top-down input–output analysis and bottom-up crowdsourced data to estimate the carbon footprints of global cities. Using city purchasing power as the main predictor of the carbon footprint, we estimate the carbon footprints of 465 global cities in 2020. Those cities comprise 10% of the global population but account for 18% of the global carbon emissions showing a significant concentration of carbon emissions. The Gini coefficients are applied to show that global carbon inequality is less than income inequality. In addition, the increased carbon emissions that come from high consumption lifestyles offset the carbon reduction by efficiency gains that could result from compact city design and large city scale. Large climate benefits could be obtained by achieving a low-carbon transition in a small number of global cities, emphasizing the need for leadership from globally important urban centres

Using crowdsourced data to estimate the carbon footprints of global cities

Cities are at the forefront of the battle against climate change. However, intercity comparisons and responsibility allocations among cities are hindered because cost- and time-effective methods to calculate the carbon footprints of global cities have yet to be developed. Here, we establish a hybrid method integrating top-down input–output analysis and bottom-up crowdsourced data to estimate the carbon footprints of global cities. Using city purchasing power as the main predictor of the carbon footprint, we estimate the carbon footprints of 465 global cities in 2020. Those cities comprise 10% of the global population but account for 18% of the global carbon emissions showing a significant concentration of carbon emissions. The Gini coefficients are applied to show that global carbon inequality is less than income inequality. In addition, the increased carbon emissions that come from high consumption lifestyles offset the carbon reduction by efficiency gains that could result from compact city design and large city scale. Large climate benefits could be obtained by achieving a low-carbon transition in a small number of global cities, emphasizing the need for leadership from globally important urban centres

Preliminary Study:Learning the Impact of Simulation Time on Reentry Location and Morphology Induced by Personalized Cardiac Modeling

Personalized cardiac modeling is widely used for studying the mechanisms of cardiac arrythmias. Due to the high demanding of computational resource of modeling, the arrhythmias induced in the models are usually simulated for just a few seconds. In clinic, it is common that arrhythmias last for more than several minutes and the morphologies of reentries are not always stable, so it is not clear that whether the simulation of arrythmias for just a few seconds is long enough to match the arrhythmias detected in patients. This study aimed to observe how long simulation of the induced arrhythmias in the personalized cardiac models is sufficient to match the arrhythmias detected in patients. A total of 5 contrast enhanced MRI datasets of patient hearts with myocardial infarction were used in this study. Then, a classification method based on Gaussian mixture model was used to detect the infarct tissue. For each reentry, 3 s and 10 s were simulated. The characteristics of each reentry simulated for different duration were studied. Reentries were induced in all 5 ventricular models and sustained reentries were induced at 39 stimulation sites in the model. By analyzing the simulation results, we found that 41% of the sustained reentries in the 3 s simulation group terminated in the longer simulation groups (10 s). The second finding in our simulation was that only 23.1% of the sustained reentries in the 3 s simulation did not change location and morphology in the extended 10 s simulation. The third finding was that 35.9% reentries were stable in the 3 s simulation and should be extended for the simulation time. The fourth finding was that the simulation results in 10 s simulation matched better with the clinical measurements than the 3 s simulation. It was shown that 10 s simulation was sufficient to make simulation results stable. The findings of this study not only improve the simulation accuracy, but also reduce the unnecessary simulation time to achieve the optimal use of computer resources to improve the simulation efficiency and shorten the simulation time to meet the time node requirements of clinical operation on patients

A Novel Iron Transporter SPD_1590 in Streptococcus pneumoniae Contributing to Bacterial Virulence Properties

Streptococcus pneumoniae, a Gram-positive human pathogen, has evolved three main transporters for iron acquisition from the host: PiaABC, PiuABC, and PitABC. Our previous study had shown that the mRNA and protein levels of SPD_1590 are significantly upregulated in the ΔpiuA/ΔpiaA/ΔpitA triple mutant, suggesting that SPD_1590 might be a novel iron transporter in S. pneumoniae. In the present study, using spd1590-knockout, -complemented, and -overexpressing strains and the purified SPD_1590 protein, we show that SPD_1590 can bind hemin, probably supplementing the function of PiuABC, to provide the iron necessary for the bacterium. Furthermore, the results of iTRAQ quantitative proteomics and cell-infection studies demonstrate that, similarly to other metal-ion uptake proteins, SPD_1590 is important for bacterial virulence properties. Overall, these results provide a better understanding of the biology of this clinically important bacterium

The global mismatch between equitable carbon dioxide removal liability and capacity

Limiting climate change to 1.5°C and achieving net-zero emissions would entail substantial carbon dioxide removal (CDR) from the atmosphere by mid-century, but how much CDR is needed at country level over time is unclear. The purpose of this paper is to provide a detailed description of when and how much CDR is required at country level to take in order to achieve 1.5°C and how much CDR countries can carry out domestically. We allocate global CDR pathways among 170 countries according to six equity principles and assess these allocations with respect to countries' biophysical and geophysical capacity to deploy CDR. Allocating global CDR to countries based on these principles suggests that CDR will, on average, represent ∼4% of nations' total emissions in 2030, rising to ∼17% in 2040. Moreover, equitable allocations of CDR, in many cases, exceed implied land and carbon storage capacities. We estimate ∼15% of countries (25) would have insufficient land to contribute an equitable share of global CDR, and ∼40% of countries (71) would have insufficient geological storage capacity. Unless more diverse CDR technologies are developed, the mismatch between CDR liabilities and land-based CDR capacities will lead to global demand for 6 GtCO2 carbon credits from 2020 to 2050. This demonstrates an imperative demand for international carbon trading of CDR