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.

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.

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.