Accelerating the energy transition towards photovoltaic and wind in China.

China's goal to achieve carbon (C) neutrality by 2060 requires scaling up photovoltaic (PV) and wind power from 1 to 10-15 PWh year-1 (refs. 1-5). Following the historical rates of renewable installation1, a recent high-resolution energy-system model6 and forecasts based on China's 14th Five-year Energy Development (CFED)7, however, only indicate that the capacity will reach 5-9.5 PWh year-1 by 2060. Here we show that, by individually optimizing the deployment of 3,844 new utility-scale PV and wind power plants coordinated with ultra-high-voltage (UHV) transmission and energy storage and accounting for power-load flexibility and learning dynamics, the capacity of PV and wind power can be increased from 9 PWh year-1 (corresponding to the CFED path) to 15 PWh year-1, accompanied by a reduction in the average abatement cost from US$97 to US$6 per tonne of carbon dioxide (tCO2). To achieve this, annualized investment in PV and wind power should ramp up from US$77 billion in 2020 (current level) to US$127 billion in the 2020s and further to US$426 billion year-1 in the 2050s. The large-scale deployment of PV and wind power increases income for residents in the poorest regions as co-benefits. Our results highlight the importance of upgrading power systems by building energy storage, expanding transmission capacity and adjusting power load at the demand side to reduce the economic cost of deploying PV and wind power to achieve carbon neutrality in China.

Delayed use of bioenergy crops might threaten climate and food security.

The potential of mitigation actions to limit global warming within 2 °C (ref. 1) might rely on the abundant supply of biomass for large-scale bioenergy with carbon capture and storage (BECCS) that is assumed to scale up markedly in the future2-5. However, the detrimental effects of climate change on crop yields may reduce the capacity of BECCS and threaten food security6-8, thus creating an unrecognized positive feedback loop on global warming. We quantified the strength of this feedback by implementing the responses of crop yields to increases in growing-season temperature, atmospheric CO2 concentration and intensity of nitrogen (N) fertilization in a compact Earth system model9. Exceeding a threshold of climate change would cause transformative changes in social-ecological systems by jeopardizing climate stability and threatening food security. If global mitigation alongside large-scale BECCS is delayed to 2060 when global warming exceeds about 2.5 °C, then the yields of agricultural residues for BECCS would be too low to meet the Paris goal of 2 °C by 2200. This risk of failure is amplified by the sustained demand for food, leading to an expansion of cropland or intensification of N fertilization to compensate for climate-induced yield losses. Our findings thereby reinforce the urgency of early mitigation, preferably by 2040, to avoid irreversible climate change and serious food crises unless other negative-emission technologies become available in the near future to compensate for the reduced capacity of BECCS.

Stringent Emission Controls Are Needed to Reach Clean Air Targets for Cities in China under a Warming Climate.

Quantifying the threat that climate change poses to fine particle (PM2.5) pollution is hampered by large uncertainties in the relationship between PM2.5 and meteorology. To constrain the impact of climate change on PM2.5, statistical models are often employed in a different manner than physical-chemical models to reduce the requirement of input data. A majority of statistical models predict PM2.5 concentration (often log-transformed) as a simple function of meteorology, which could be biased due to the conversion of precursor gases to PM2.5. We reduced this bias by developing a unique statistic model where the sum of PM2.5 and the weighted precursor gases, rather than the PM2.5 alone, was predicted as a function of meteorology and a proxy of primary emissions, where the input data of PM10, CO, O3, NOx, and SO2 were obtained from routine measurements. This modification, without losing the simplicity of statistical models, reduced the mean-square error from 27 to 17% and increased the coefficient of determination from 47 to 67% in the model cross-validation using daily PM2.5 observations during 2013-2018 for 74 cities over China. We found a previously unrecognized mechanism that synoptic climate change in the past half-century might have increased low quantiles of PM2.5 more strenuously than the upper quantiles in large cities over China. Climate change during 1971-2018 was projected to increase the annual mean concentration of PM2.5 at a degree that could be comparable with the toughest-ever clean air policy during 2013-2018 had counteracted it, as inferred from the decline in the daily concentration of carbon monoxide as an inert gas. Our estimate of the impact of climate change on PM2.5 is higher than previous statistical models, suggesting that aerosol chemistry might play a more important role than previously thought in the interaction between climate change and air pollution. Our result indicated that air quality might degrade if the future synoptic climate change could continue interacting with aerosol chemistry as it had occurred in the past half-century.

Effects of Nongxiangxing baijiu (Chinese liquor) on mild alcoholic liver injury revealed by non-target metabolomics using ultra-performance liquid chromatography quadrupole-time-of-flight mass spectrometry.

Nongxiangxing baijiu (Chinese liquor) is one of the most widely consumed beverages in China. This liquor has been shown to contain large quantities of various bioactive ingredients that are beneficial to health. The goals of the present study were to examine the effects of moderate dose Nongxiangxing baijiu on alcoholic liver injury in rats, and to explore the mechanism of action of Nongxiangxing baijiu on alcoholic liver injury. To accomplish these goals, we developed a metabolomic analysis method based on ultra-performance liquid chromatography quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF/MS) analysis and multivariate statistical analysis. Our serum lipid and hepatic histopathology results demonstrate that ethanol administration induced mild alcoholic liver injury in rats. However, these ethanol-induced changes were significantly alleviated in the Nongxiangxing baijiu group. These results suggest that moderate dose Nongxiangxing baijiu might have a preventive effect on mild alcoholic liver injury. Using our metabolomics method, we were able to identify 45 differential metabolites in serum and urine which could be used to characterize mild alcoholic liver injury in rats. Of these, 15 differential metabolites, including four Lysophosphatidylethanolamines, two phosphatidylcholines, four long-chain fatty acids, one porphyrin, two esters, one ceramide, and one triol, were regulated by Nongxiangxing baijiu. KEGG metabolic pathway analysis revealed that the main metabolic pathway regulated by Nongxiangxing baijiu was the glycerolipid pathway. Together, these findings provide evidence that moderate dose Nongxiangxing baijiu can reduce mild alcoholic liver injury (including metabolic disorders). Our study also provides preliminary data on the mechanism of action of Nongxiangxing baijiu in liver injury.

Predicting the effect of confinement on the COVID-19 spread using machine learning enriched with satellite air pollution observations.

The real-time monitoring of reductions of economic activity by containment measures and its effect on the transmission of the coronavirus (COVID-19) is a critical unanswered question. We inferred 5,642 weekly activity anomalies from the meteorology-adjusted differences in spaceborne tropospheric NO2 column concentrations after the 2020 COVID-19 outbreak relative to the baseline from 2016 to 2019. Two satellite observations reveal reincreasing economic activity associated with lifting control measures that comes together with accelerating COVID-19 cases before the winter of 2020/2021. Application of the near-real-time satellite NO2 observations produces a much better prediction of the deceleration of COVID-19 cases than applying the Oxford Government Response Tracker, the Public Health and Social Measures, or human mobility data as alternative predictors. A convergent cross-mapping suggests that economic activity reduction inferred from NO2 is a driver of case deceleration in most of the territories. This effect, however, is not linear, while further activity reductions were associated with weaker deceleration. Over the winter of 2020/2021, nearly 1 million daily COVID-19 cases could have been avoided by optimizing the timing and strength of activity reduction relative to a scenario based on the real distribution. Our study shows how satellite observations can provide surrogate data for activity reduction during the COVID-19 pandemic and monitor the effectiveness of containment to the pandemic before vaccines become widely available.

Spatially explicit analysis identifies significant potential for bioenergy with carbon capture and storage in China.

As China ramped-up coal power capacities rapidly while CO2 emissions need to decline, these capacities would turn into stranded assets. To deal with this risk, a promising option is to retrofit these capacities to co-fire with biomass and eventually upgrade to CCS operation (BECCS), but the feasibility is debated with respect to negative impacts on broader sustainability issues. Here we present a data-rich spatially explicit approach to estimate the marginal cost curve for decarbonizing the power sector in China with BECCS. We identify a potential of 222 GW of power capacities in 2836 counties generated by co-firing 0.9 Gt of biomass from the same county, with half being agricultural residues. Our spatially explicit method helps to reduce uncertainty in the economic costs and emissions of BECCS, identify the best opportunities for bioenergy and show the limitations by logistical challenges to achieve carbon neutrality in the power sector with large-scale BECCS in China.

Daily CO2 Emission Reduction Indicates the Control of Activities to Contain COVID-19 in China.

Lockdown measures are essential to containing the spread of coronavirus disease 2019 (COVID-19), but they will slow down economic growth by reducing industrial and commercial activities. However, the benefits of activity control from containing the pandemic have not been examined and assessed. Here we use daily carbon dioxide (CO2) emission reduction in China estimated from statistical data for energy consumption and satellite data for nitrogen dioxide (NO2) measured by the Ozone Monitoring Instrument (OMI) as an indicator for reduced activities consecutive to a lockdown. We perform a correlation analysis to show that a 1% day-1 decrease in the rate of COVID-19 cases is associated with a reduction in daily CO2 emissions of 0.22% ± 0.02% using statistical data for energy consumption relative to emissions without COVID-19, or 0.20% ± 0.02% using satellite data for atmospheric column NO2. We estimate that swift action in China is effective in limiting the number of COVID-19 cases <100,000 with a reduction in CO2 emissions of up to 23% by the end of February 2020, whereas a 1-week delay would have required greater containment and a doubling of the emission reduction to meet the same goal. By analyzing the costs of health care and fatalities, we find that the benefits on public health due to reduced activities in China are 10-fold larger than the loss of gross domestic product. Our findings suggest an unprecedentedly high cost of maintaining activities and CO2 emissions during the COVID-19 pandemic and stress substantial benefits of containment in public health by taking early actions to reduce activities during the outbreak of COVID-19.

Spatiotemporal variation of domestic biomass burning emissions in rural China based on a new estimation of fuel consumption.

Domestic biomass burning (DBB) influences both indoor and outdoor air quality due to the multiple pollutants released during incomplete and inefficient combustion. The emissions are not well quantified because of insufficient information, which were the key parameters related to fuel consumption estimation, such as province- and year-specific percentage of domestic straw burning (Pstraw) and firewood consumption (Fc). In this study, we established the quantitative relationship between rural-related socioeconomic parameters (e.g., rural per-capita income and rural Engel's coefficient) and Pstraw/Fc. DBB emissions, including 12 crop straw types and firewood for 12 kinds of pollutants in China during the period 1995-2014, were estimated based on fuel-specific emission factors and detailed fuel consumption data. The results revealed that the national emissions generally increased initially and then decreased with the turning point around 2007-2008. Firewood burning was the major source of the NH3 and BC emissions; straw burning contributed more to SO2, NMVOC, CO, OC, and CH4 emissions; while the major contributor changed from firewood to domestic straw burning for NOx, PM10, PM2.5, CO2, and Hg emissions. The emission trends varied among the 31 provinces. The major agricultural regions of north-eastern, central, and south-western China were always characterized by high emissions. The spatial variation mainly occurred in the northeast and north China (increase), and central-south and coastal regions of China (decrease).

Investigating the contribution of shipping emissions to atmospheric PM2.5 using a combined source apportionment approach.

Many studies have been conducted focusing on the contribution of land emission sources to PM2.5 in China; however, little attention had been paid to other contributions, especially the secondary contributions from shipping emissions to atmospheric PM2.5. In this study, a combined source apportionment approach, including principle component analysis (PCA) and WRF-CMAQ simulation, was applied to identify both primary and secondary contributions from ships to atmospheric PM2.5. An intensive PM2.5 observation was conducted from April 2014 to January 2015 in Qinhuangdao, which was close to the largest energy output port of China. The chemical components analysis results showed that the primary component was the major contributor to PM2.5, with proportions of 48.3%, 48.9%, 55.1% and 55.4% in spring, summer, autumn and winter, respectively. The secondary component contributed higher fractions in summer (48.2%) and winter (36.8%), but had lower percentages in spring (30.1%) and autumn (32.7%). The hybrid source apportionment results indicated that the secondary contribution (SC) of shipping emissions to PM2.5 could not be ignored. The annual average SC was 2.7%, which was comparable to the primary contribution (2.9%). The SC was higher in summer (5.3%), but lower in winter (1.1%). The primary contributions to atmospheric PM2.5 were 3.0%, 2.5%, 3.4% and 2.7% in spring, summer, autumn and winter, respectively. As for the detailed chemical components, the contributions of shipping emissions were 2.3%, 0.5%, 0.1%, 1.0%, 1.7% and 0.1% to elements & sea salt, primary organic aerosol (POA), element carbon (EC), nitrate, sulfate and secondary organic carbon (SOA), respectively. The results of this study will further the understanding of the implications of shipping emissions in PM2.5 pollution.