Committed emissions from existing energy infrastructure jeopardize 1.5 °C climate target.

Net anthropogenic emissions of carbon dioxide (CO2) must approach zero by mid-century (2050) in order to stabilize the global mean temperature at the level targeted by international efforts1-5. Yet continued expansion of fossil-fuel-burning energy infrastructure implies already 'committed' future CO2 emissions6-13. Here we use detailed datasets of existing fossil-fuel energy infrastructure in 2018 to estimate regional and sectoral patterns of committed CO2 emissions, the sensitivity of such emissions to assumed operating lifetimes and schedules, and the economic value of the associated infrastructure. We estimate that, if operated as historically, existing infrastructure will cumulatively emit about 658 gigatonnes of CO2 (with a range of 226 to 1,479 gigatonnes CO2, depending on the lifetimes and utilization rates assumed). More than half of these emissions are predicted to come from the electricity sector; infrastructure in China, the USA and the 28 member states of the European Union represents approximately 41 per cent, 9 per cent and 7 per cent of the total, respectively. If built, proposed power plants (planned, permitted or under construction) would emit roughly an extra 188 (range 37-427) gigatonnes CO2. Committed emissions from existing and proposed energy infrastructure (about 846 gigatonnes CO2) thus represent more than the entire carbon budget that remains if mean warming is to be limited to 1.5 degrees Celsius (°C) with a probability of 66 to 50 per cent (420-580 gigatonnes CO2)5, and perhaps two-thirds of the remaining carbon budget if mean warming is to be limited to less than 2 °C (1,170-1,500 gigatonnes CO2)5. The remaining carbon budget estimates are varied and nuanced14,15, and depend on the climate target and the availability of large-scale negative emissions16. Nevertheless, our estimates suggest that little or no new CO2-emitting infrastructure can be commissioned, and that existing infrastructure may need to be retired early (or be retrofitted with carbon capture and storage technology) in order to meet the Paris Agreement climate goals17. Given the asset value per tonne of committed emissions, we suggest that the most cost-effective premature infrastructure retirements will be in the electricity and industry sectors, if non-emitting alternatives are available and affordable4,18

Variations of China's emission estimates:Response to uncertainties in energy statistics

The accuracy of China's energy statistics is of great concern because it contributes greatly to the uncertainties in estimates of global emissions. This study attempts to improve the understanding of uncertainties in China's energy statistics and evaluate their impacts on China's emissions during the period of 1990-2013. We employed the Multi-resolution Emission Inventory for China (MEIC) model to calculate China's emissions based on different official data sets of energy statistics using the same emission factors. We found that the apparent uncertainties (maximum discrepancy) in China's energy consumption increased from 2004 to 2012, reaching a maximum of 646Mtce (million tons of coal equivalent) in 2011 and that coal dominated these uncertainties. The discrepancies between the national and provincial energy statistics were reduced after the three economic censuses conducted during this period, and converging uncertainties were found in 2013. The emissions calculated from the provincial energy statistics are generally higher than those calculated from the national energy statistics, and the apparent uncertainty ratio (the ratio of the maximum discrepancy to the mean value) owing to energy uncertainties in 2012 took values of 30.0, 16.4, 7.7, 9.2 and 15.6%, for SO2, NOx, VOC, PM2.5 and CO2 emissions, respectively. SO2 emissions are most sensitive to energy uncertainties because of the high contributions from industrial coal combustion. The calculated emission trends are also greatly affected by energy uncertainties - from 1996 to 2012, CO2 and NOx emissions, respectively, increased by 191 and 197% according to the provincial energy statistics but by only 145 and 139% as determined from the original national energy statistics. The energy-induced emission uncertainties for some species such as SO2 and NOx are comparable to total uncertainties of emissions as estimated by previous studies, indicating variations in energy consumption could be an important source of China's emission uncertainties

Impacts of climate change on future air quality and human health in China.

In recent years, air pollution has caused more than 1 million deaths per year in China, making it a major focus of public health efforts. However, future climate change may exacerbate such human health impacts by increasing the frequency and duration of weather conditions that enhance air pollution exposure. Here, we use a combination of climate, air quality, and epidemiological models to assess future air pollution deaths in a changing climate under Representative Concentration Pathway 4.5 (RCP4.5). We find that, assuming pollution emissions and population are held constant at current levels, climate change would adversely affect future air quality for >85% of China's population (∼55% of land area) by the middle of the century, and would increase by 3% and 4% the population-weighted average concentrations of fine particulate matter (PM2.5) and ozone, respectively. As a result, we estimate an additional 12,100 and 8,900 Chinese (95% confidence interval: 10,300 to 13,800 and 2,300 to 14,700, respectively) will die per year from PM2.5 and ozone exposure, respectively. The important underlying climate mechanisms are changes in extreme conditions such as atmospheric stagnation and heat waves (contributing 39% and 6%, respectively, to the increase in mortality). Additionally, greater vulnerability of China's aging population will further increase the estimated deaths from PM2.5 and ozone in 2050 by factors of 1 and 3, respectively. Our results indicate that climate change and more intense extremes are likely to increase the risk of severe pollution events in China. Managing air quality in China in a changing climate will thus become more challenging

Reduced carbon emission estimates from fossil fuel combustion and cement production in China.

Nearly three-quarters of the growth in global carbon emissions from the burning of fossil fuels and cement production between 2010 and 2012 occurred in China. Yet estimates of Chinese emissions remain subject to large uncertainty; inventories of China's total fossil fuel carbon emissions in 2008 differ by 0.3 gigatonnes of carbon, or 15 per cent. The primary sources of this uncertainty are conflicting estimates of energy consumption and emission factors, the latter being uncertain because of very few actual measurements representative of the mix of Chinese fuels. Here we re-evaluate China's carbon emissions using updated and harmonized energy consumption and clinker production data and two new and comprehensive sets of measured emission factors for Chinese coal. We find that total energy consumption in China was 10 per cent higher in 2000-2012 than the value reported by China's national statistics, that emission factors for Chinese coal are on average 40 per cent lower than the default values recommended by the Intergovernmental Panel on Climate Change, and that emissions from China's cement production are 45 per cent less than recent estimates. Altogether, our revised estimate of China's CO2 emissions from fossil fuel combustion and cement production is 2.49 gigatonnes of carbon (2 standard deviations = ±7.3 per cent) in 2013, which is 14 per cent lower than the emissions reported by other prominent inventories. Over the full period 2000 to 2013, our revised estimates are 2.9 gigatonnes of carbon less than previous estimates of China's cumulative carbon emissions. Our findings suggest that overestimation of China's emissions in 2000-2013 may be larger than China's estimated total forest sink in 1990-2007 (2.66 gigatonnes of carbon) or China's land carbon sink in 2000-2009 (2.6 gigatonnes of carbon).This is the author accepted manuscript. The final version is available from NPG via http://dx.doi.org/10.1038/nature1467

To what extent can China’s near-term air pollution control policy protect air quality and human health? A case study of the Pearl River Delta region

Following a series of extreme air pollution events, the Chinese government released the Air Pollution Prevention and Control Action Plan in 2013 (China's State Council 2013). The Action Plan sets clear goals for key regions (i.e. cities above the prefecture level, Beijing-Tianjin-Hebei Province, the Yangtze River Delta and the Pearl River Delta) and establishes near-term control efforts for the next five years. However, the extent to which the Action Plan can direct local governments' activities on air pollution control remains unknown. Here we seek to evaluate the air quality improvement and associated health benefits achievable under the Action Plan in the Pearl River Delta (PRD) area from 2012 to 2017. Measure-by-measure quantification results show that the Action Plan would promise effective emissions reductions of 34% of SO2, 28% of NOx, 26% of PM2.5 (particulate matter less than 2.5 μm in diameter), and 10% of VOCs (volatile organic compounds). These emissions abatements would lower the PM2.5 concentration by 17%, surpassing the 15% target established in the Action Plan, thereby avoiding more than 2900 deaths and 4300 hospital admissions annually. We expect the implementation of the Action Plan in the PRD would be productive; the anticipated impacts, however, fall short of the goal of protecting the health of local residents, as there are still more than 33 million people living in places where the annual mean ambient PM2.5 concentrations are greater than 35 μg m−3, the interim target-3 of the World Health Organization (WHO). We therefore propose the next steps for air pollution control that are important not only for the PRD but also for all other regions of China as they develop and implement effective air pollution control policies

Global patterns of daily CO2 emissions reductions in the first year of COVID-19

Day-to-day changes in CO2 emissions from human activities, in particular fossil-fuel combustion and cement production, reflect a complex balance of influences from seasonality, working days, weather and, most recently, the COVID-19 pandemic. Here, we provide a daily CO2 emissions dataset for the whole year of 2020, calculated from inventory and near-real-time activity data. We find a global reduction of 6.3% (2,232 MtCO2) in CO2 emissions compared with 2019. The drop in daily emissions during the first part of the year resulted from reduced global economic activity due to the pandemic lockdowns, including a large decrease in emissions from the transportation sector. However, daily CO2 emissions gradually recovered towards 2019 levels from late April with the partial reopening of economic activity. Subsequent waves of lockdowns in late 2020 continued to cause smaller CO2 reductions, primarily in western countries. The extraordinary fall in emissions during 2020 is similar in magnitude to the sustained annual emissions reductions necessary to limit global warming at 1.5°C. This underscores the magnitude and speed at which the energy transition needs to advance

Evaporation process dominates vehicular NMVOC emissions in China with enlarged contribution from 1990 to 2016

Non-methane volatile organic compounds (NMVOC) are important precursors of ozone and secondary organic aerosols in PM _2.5 (particulate matter with aerodynamic diameters smaller than 2.5 μ m), both of which cause severe climate, ecosystem, and human health damages. As one of the major anthropogenic sources, onroad vehicles are subject to relatively large errors and uncertainties in the estimation of NMVOC emissions due to complicated methods and parameters involved and a lack of comprehensive evaluation of influencing factors. Here, based on our previous work with necessary improvement, we estimate China’s vehicular NMVOC emissions by county and by month during 1990–2016 with a consideration of meteorological influence on the spatial-temporal dynamics of emission factors. Our estimate suggests that vehicular NMVOC emissions in China have peaked around 2008 and then declined up to 2016 with an enlarged contribution of the evaporative process to vehicular NMVOC emissions. Vehicular NMVOC emissions have been dominated by the evaporative process at present. Meteorological factors alter spatial-temporal distributions of NMVOC emissions, especially evaporative emissions, which are enhanced in South China and in summer. Emissions and ozone formation potential of the major chemical groups (i.e. Alkenes, Aromatics, and Alkanes) also increase substantially due to meteorological influences. Our analysis suggests that mitigation strategies for vehicle pollutions should be designed based on a sophisticated emission inventory accounting for the meteorological impact on emission factors to correct the potential underestimation of NMVOC emissions, especially those from the evaporative process

Particle Swarm Optimization-Based Variational Mode Decomposition for Ground Penetrating Radar Data Denoising

Ground Penetrating Radar (GPR) has become a widely used technology in geophysical prospecting. The Variational Mode Decomposition (VMD) method is a fully non-recursive signal decomposition method with noise robustness for GPR data processing. The VMD algorithm determines the central frequency and bandwidth of each Intrinsic Mode Function (IMF) by iteratively searching for the optimal solution of the variational mode and is capable of adaptively and effectively dividing the signal in the frequency domain into the many IMFs. However, the penalty parameter α and the number of IMFs K in VMD processing are determined depending on manual experience, which are important parameters affecting the decomposition results. In this paper, we propose a method to automatically search the parameters α and K optimally by Particle Swarm Optimization (PSO) algorithm. Then the signal-to-noise ratio (SNR) and root-mean-square error (RMSE) are used to judge the best superposition of the IMFs for data reconstruction, and the process is data-driven without human subjective intervention. The proposed method is used to process the field data, and the reconstruction data show that this innovative VMD processing can effectively improve the SNR and highlight the target reflections, even some targets not found in pre-processing are also revealed

Source-Independent Waveform Inversion Method for Ground Penetrating Radar Based on Envelope Objective Function

For the full waveform inversion, it is necessary to provide an accurate source wavelet for forwarding modeling in the iteration. The source wavelet estimation method based on deconvolution technology can solve this problem to some extent, but we find that the estimated source wavelet is not accurate and needs to be manually corrected repeatedly in the iteration. This process is highly operator-intensive, and the update process is time-consuming and increases the potential for errors. We propose a source-independent waveform inversion (SIEWI) scheme for cross-hole GPR data, and use the envelope objective function combined with this method to effectively reduce the nonlinearity of inversion. The residual field used by SIEWI to construct the gradient inherits the characteristics of the envelope wavefield. Compared with full waveform inversion (FWI), SIEWI is more robust and less sensitive to frequency components and inaccurate source wavelet. To avoid cycle jumping, the multi-scale strategy effectively utilizes the properties of convolutional wavefields. In one iteration, the wavefield is decomposed into multiple frequency bands through multiple convolutions in the time domain to construct a multi-scale inversion strategy that preferentially inverts low-frequency information

Underwater Spectral Imaging System Based on Liquid Crystal Tunable Filter

In the past decade, underwater spectral imaging (USI) has shown great potential in underwater exploration for its high spectral and spatial resolution. This proposal presents a stare-type USI system combined with the liquid crystal tunable filter (LCTF) spectral splitting device. Considering the working features of LCTF and the theoretical model of USI, the core structure containing “imaging lens-LCTF-imaging sensor” is designed and developed. The system is compact, and the optical geometry is constructed minimally. The spectral calibration test analysis proved that the spectral response range of the system covers a full band of 400 nm to 700 nm with the highest spectral resolution between 6.7 nm and 18.5 nm. The experiments show that the system can quickly collect high-quality spectral image data by switching between different spectral bands arbitrarily. The designed prototype provides a feasible and reliable spectral imaging solution for in situ underwater targets observation with high spectrum collecting efficiency