China's post-coal growth

Slowing GDP growth, a structural shift away from heavy industry, and more proactive policies on air pollution and clean energy have caused China's coal use to peak. It seems that economic growth has decoupled from growth in coal consumption

China CO2 emission accounts 1997–2015

China is the world’s top energy consumer and CO2 emitter, accounting for 30% of global emissions. Compiling an accurate accounting of China’s CO2 emissions is the first step in implementing reduction policies. However, no annual, officially published emissions data exist for China. The current emissions estimated by academic institutes and scholars exhibit great discrepancies. The gap between the different emissions estimates is approximately equal to the total emissions of the Russian Federation (the 4th highest emitter globally) in 2011. In this study, we constructed the time-series of CO2 emission inventories for China and its 30 provinces. We followed the Intergovernmental Panel on Climate Change (IPCC) emissions accounting method with a territorial administrative scope. The inventories include energy-related emissions (17 fossil fuels in 47 sectors) and process-related emissions (cement production). The first version of our dataset presents emission inventories from 1997 to 2015. We will update the dataset annually. The uniformly formatted emission inventories provide data support for further emission-related research as well as emissions reduction policy-making in China

Key indicators to track current progress and future ambition of the Paris Agreement

Current emission pledges to the Paris Agreement appear insufficient to hold the global average temperature increase to well below 2 °C above pre-industrial levels. Yet, details are missing on how to track progress towards the â € Paris goal', inform the five-yearly â € global stocktake', and increase the ambition of Nationally Determined Contributions (NDCs). We develop a nested structure of key indicators to track progress through time. Global emissions track aggregated progress, country-level decompositions track emerging trends that link directly to NDCs, and technology diffusion indicates future reductions. We find the recent slowdown in global emissions growth is due to reduced growth in coal use since 2011, primarily in China and secondarily in the United States. The slowdown is projected to continue in 2016, with global CO 2 emissions from fossil fuels and industry similar to the 2015 level of 36 GtCO 2. Explosive and policy-driven growth in wind and solar has contributed to the global emissions slowdown, but has been less important than economic factors and energy efficiency. We show that many key indicators are currently broadly consistent with emission scenarios that keep temperatures below 2 °C, but the continued lack of large-scale carbon capture and storage threatens 2030 targets and the longer-term Paris ambition of net-zero emissions

Unequal household carbon footprints in China

Households’ carbon footprints are unequally distributed among the rich and poor due to differences in the scale and patterns of consumption. We present distributional focused carbon footprints for Chinese households and use a carbon-footprint-Gini coefficient to quantify inequalities. We find that in 2012 the urban very rich, comprising 5% of population, induced 19% of the total carbon footprint from household consumption in China, with 6.4 tCO2/cap. The average Chinese household footprint remains comparatively low (1.7 tCO2/cap), while those of the rural population and urban poor, comprising 58% of population, are 0.5–1.6 tCO2/cap. Between 2007 and 2012 the total footprint from households increased by 19%, with 75% of the increase due to growing consumption of the urban middle class and the rich. This suggests that a transformation of Chinese lifestyles away from the current trajectory of carbon-intensive consumption patterns requires policy interventions to improve living standards and encourage sustainable consumption

Global Carbon Budget 2020

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) and terrestrial CO2 sink (SLAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2010–2019), EFOS was 9.6 ± 0.5 GtC yr−1 excluding the cement carbonation sink (9.4 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 1.6 ± 0.7 GtC yr−1. For the same decade, GATM was 5.1 ± 0.02 GtC yr−1 (2.4 ± 0.01 ppm yr−1), SOCEAN 2.5 ±  0.6 GtC yr−1, and SLAND 3.4 ± 0.9 GtC yr−1, with a budget imbalance BIM of −0.1 GtC yr−1 indicating a near balance between estimated sources and sinks over the last decade. For the year 2019 alone, the growth in EFOS was only about 0.1 % with fossil emissions increasing to 9.9 ± 0.5 GtC yr−1 excluding the cement carbonation sink (9.7 ± 0.5 GtC yr−1 when cement carbonation sink is included), and ELUC was 1.8 ± 0.7 GtC yr−1, for total anthropogenic CO2 emissions of 11.5 ± 0.9 GtC yr−1 (42.2 ± 3.3 GtCO2). Also for 2019, GATM was 5.4 ± 0.2 GtC yr−1 (2.5 ± 0.1 ppm yr−1), SOCEAN was 2.6 ± 0.6 GtC yr−1, and SLAND was 3.1 ± 1.2 GtC yr−1, with a BIM of 0.3 GtC. The global atmospheric CO2 concentration reached 409.85 ± 0.1 ppm averaged over 2019. Preliminary data for 2020, accounting for the COVID-19-induced changes in emissions, suggest a decrease in EFOS relative to 2019 of about −7 % (median estimate) based on individual estimates from four studies of −6 %, −7 %, −7 % (−3 % to −11 %), and −13 %. Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2019, but discrepancies of up to 1 GtC yr−1 persist for the representation of semi-decadal variability in CO2 fluxes. Comparison of estimates from diverse approaches and observations shows (1) no consensus in the mean and trend in land-use change emissions over the last decade, (2) a persistent low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) an apparent discrepancy between the different methods for the ocean sink outside the tropics, particularly in the Southern Ocean. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set (Friedlingstein et al., 2019; Le Quéré et al., 2018b, a, 2016, 2015b, a, 2014, 2013). The data presented in this work are available at https://doi.org/10.18160/gcp-2020 (Friedlingstein et al., 2020)

Quantum optics: Micro meets macro

[No abstract available

Towards real-time verification of CO2 emissions

This is the author accepted manuscript. The final version is available from Springer Nature via the DOI in this recordThe Paris Agreement has increased the incentive to verify reported anthropogenic carbon dioxide emissions with independent Earth system observations. Reliable verification requires a step change in our understanding of carbon cycle variability.This work was possible thanks to substantial contributions by many scientists and funding agencies that supported the Global Carbon Budget 2017 compiled by the Global Carbon Project. G.P.P., R.M.A., J.I.K. acknowledge the support of the Norwegian Research Council (project number 209701). J.G.C. acknowledges the support of the Australian Government’s National Environmental Science Programme’s (NESP) Earth Systems and Climate Change Hub. P.F. acknowledges support from the European Commission Horizon 2020 project CRESCENDO (grant number 641816). F.J. acknowledges support from the Swiss National Science Foundation (number 200020_172476). G.A.M. acknowledges support from the Center for Climate and Life at Columbia University

Quantum entanglement

10.1038/nphys2904Nature Physics104256-25