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

Liu, Z; Guan, Dabo; Wei, Wei; Davis, Steven J.; Ciais, Philippe; Bai, Jin; Peng, Shushi; Zhang, Qiang; Hubacek, Klaus; Marland, Gregg; Andres, R. J.; Crawford‐Brown, Douglas; Lin, Jintai; Zhao, Hongyan; Hong, Chaopeng; Boden, Thomas A.; Feng, Kuishuang; Peters, Glen P.; Xi, Fengming; Liu, Junguo; Li, Yuan; Zhao, Yu; Zeng, Ning; He, Kebin

doi:10.1038/nature14677

Cited by 1,232 publications

(617 citation statements)

References 28 publications

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“…2). A downward revision of global F is consistent with the correction of the emissions for China based on evidence of the lower carbon content for coal burned in that country (29). Compared with LQ15, the optimized ocean sink during 2000-2004 is larger by 0.22 Pg C·y −1 but lower by 0.18 ± 0.10 Pg C·y −1 during all of the other periods.…”

Section: Resultssupporting

confidence: 72%

Reducing uncertainties in decadal variability of the global carbon budget with multiple datasets

Ciais

Wang

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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Conventional calculations of the global carbon budget infer the land sink as a residual between emissions, atmospheric accumulation, and the ocean sink. Thus, the land sink accumulates the errors from the other flux terms and bears the largest uncertainty.Here, we present a Bayesian fusion approach that combines multiple observations in different carbon reservoirs to optimize the land (B) and ocean (O) carbon sinks, land use change emissions (L), and indirectly fossil fuel emissions (F) from 1980 to 2014. Compared with the conventional approach, Bayesian optimization decreases the uncertainties in B by 41% and in O by 46%. The L uncertainty decreases by 47%, whereas F uncertainty is marginally improved through the knowledge of natural fluxes. Both ocean and net land uptake (B + L) rates have positive trends of 29 ± 8 and 37 ± 17 Tg C·y −2 since 1980, respectively. Our Bayesian fusion of multiple observations reduces uncertainties, thereby allowing us to isolate important variability in global carbon cycle processes.global carbon budget | carbon cycle | decadal variations | Bayesian fusion

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Section: Resultssupporting

confidence: 72%

Reducing uncertainties in decadal variability of the global carbon budget with multiple datasets

Ciais

Wang

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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“…When combined with recent improvements in understanding baseline historical data (e.g. Liu et al, 2015), our analysis of recent data and likely trends could help inform new models and modeling assumptions that yield a much more realistic range of China's future emissions scenarios than at present. Second, whereas this article focused on energy CO 2 emissions, the analysis of structural change in China's economy could usefully inform analysis and projections of China's non-energy CO 2 Our analysis and conclusions have a number of important implications for policy.…”

Section: Resultsmentioning

confidence: 99%

China's changing economy: implications for its carbon dioxide emissions

2016

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As China's government finalises the country's 13th Five Year Plan for economic development (2016)(2017)(2018)(2019)(2020), this article takes stock of recent changes in China's economy and energy system since the turn of the century, and looks ahead to the likely trajectory of Policy Relevance StatementThe article suggests a number of important areas of Chinese policy focus to mitigate risks and challenges that might otherwise prolong the peak date for CO2 emissions. Our analysis and conclusions also have more general implications for Chinese and international climate policy.They suggest that China's international commitment to peak emissions 'around 2030' should be seen as a highly conservative upper limit from a government that prefers to under-promise and over-deliver. They also reinforce the virtue of a 'dynamic' approach to international climate cooperation, as envisaged under the Paris Agreement, whereby countries' targets and policies are regularly updated in light of new information. The importance of macroeconomic analysis for emissions projections climate policy development is also highlighted.

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“…While in the budget we consider a fixed uncertainty of ±5 % for all years, in reality the uncertainty, as a percentage of the emissions, is growing with time because of the larger share of global emissions from non-Annex B countries (emerging economies and developing countries) with less precise statistical systems . For example, the uncertainty in Chinese emissions has been estimated at around ±10 % (for ±1σ ; Gregg et al, 2008), and important potential biases have been identified that suggest China's emissions could be overestimated in published studies (Liu et al, 2015). Generally, emissions from mature economies with good statistical bases have an uncertainty of only a few percent (Marland, 2008).…”

Section: Emissions From Fossil Fuels and Industry And Their Uncertaintymentioning

confidence: 99%

Global Carbon Budget 2015

Quéré

Moriarty

Andrew

et al. 2015

Earth Syst. Sci. Data

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Abstract. Accurate assessment of anthropogenic carbon dioxide (CO 2 ) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates as well as consistency within and among components, alongside methodology and data limitations. CO 2 emissions from fossil fuels and industry (E FF ) are based on energy statistics and cement production data, while emissions from land-use change (E LUC ), mainly deforestation, are based on combined evidence from land-cover-change data, fire activity associated with deforestation, and models. The global atmospheric CO 2 concentration is measured directly and its rate of growth (G ATM ) is computed from the annual changes in concentration. The mean ocean CO 2 sink (S OCEAN ) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in S OCEAN is evaluated with data products based on surveys of ocean CO 2 measurements. The global residual terrestrial CO 2 sink (S LAND ) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models forced by observed climate, CO 2 , and land-cover change (some including nitrogen-carbon interactions). We compare the mean land and ocean fluxes and their variability to estimates from three atmospheric inverse methods for three broad latitude bands. All uncertainties are reported as ±1σ , reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014), E FF was 9.0 ± 0.5 GtC yr −1 , E LUC was 0.9 ± 0.5 GtC yr −1 , G ATM was 4.4 ± 0.1 GtC yr −1 , S OCEAN was 2.6 ± 0.5 GtC yr −1 , and S LAND was 3.0 ± 0.8 GtC yr −1 . For the year 2014 alone, E FF grew to 9.8 ± 0.5 GtC yr −1 , 0.6 % above 2013, continuing the growth trend in these emissions, albeit at a slower rate compared to the average growth of 2.2 % yr −1 that took place during 2005-2014. Also, for 2014, E LUC was 1.1 ± 0.5 GtC yr −1 , G ATM was 3.9 ± 0.2 GtC yr −1 , S OCEAN was 2.9 ± 0.5 GtC yr −1 , and S LAND was 4.1 ± 0.9 GtC yr −1 . G ATM was lower in 2014 compared to the past decade (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014), reflecting a larger S LAND for that year. The global atmospheric CO 2 concentration reached 397.15 ± 0.10 ppm averaged over 2014. For 2015, preliminary data indicate that the growth in E FF will be near or slightly below zero, with a projection of −0.6 [range of −1.6 to +0....

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

Cited by 1,232 publications

References 28 publications

Reducing uncertainties in decadal variability of the global carbon budget with multiple datasets

Reducing uncertainties in decadal variability of the global carbon budget with multiple datasets

China's changing economy: implications for its carbon dioxide emissions

Global Carbon Budget 2015

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