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Persistent emissions of ozone-depleting carbon tetrachloride from China during 2011–2021

Nature Geoscience volume 18, pages 593–598 (2025)Cite this article

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Abstract

Lingering global emissions of carbon tetrachloride (CCl4) are slowing ozone layer recovery. Estimates of global CCl4 emissions based on observed atmospheric mole fractions and inverse modelling (top down) exceed the emissions derived from known sources (bottom up) by ~30–40%. Here we derived CCl4 emissions from China for 2011–2021 using long-term atmospheric observations from a network of sites from across China and a top-down approach. Mean annual CCl4 emissions in China during 2011–2021 were between approximately 16 Gg yr−1 and 25 Gg yr−1, substantially larger than previous bottom-up inventories for China of less than 6 Gg yr−1 since 2011. Expressed in terms of ozone depletion potential (ODP)-weighted emissions, CCl4 annual emissions from China are comparable to global annual ‘unexpected’ trichlorofluoromethane (CFC-11) emissions during 2013–2018, or global total annual hydrochlorofluorocarbon emissions in 2020. The CCl4 emissions from China accounted for approximately half of the reported global total during 2011–2020, with neither showing a significant decreasing trend during this period. Substantial CCl4 emissions in China from allowed feedstock use, during the renewed production of CFC-11 between 2013 and 2018, and from by-production could close some of the emissions gap. However, ~4–15 Gg yr−1 of CCl4 emissions in China remain unexplained during 2011–2021, potentially accounting for more than half of the remaining global gap.

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Fig. 1: Emissions of CCl4 in China.
Fig. 2: Emissions of CCl4 in China and globally.
Fig. 3: Estimated emissions of CCl4 in China from potential sources.

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Data availability

The data used in the regional inversion, along with the source data for Figs. 1–3, Extended Data Figs. 2–5 and Extended Data Table 1, are available via Zenodo at https://doi.org/10.5281/zenodo.15033508 (ref. 57).

Code availability

Code for the hierarchical Bayesian inverse modelling framework NAME-HBMCMC used in this study is available via Zenodo at https://doi.org/10.5281/zenodo.10929382 (ref. 58).

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (grant no. 2019YFC0214500), Shanghai B&R Joint Laboratory Project (no. 22230750300), and China Meteorological Administration ‘The Major Technology R&D and Application of Greenhouse Gas Observation’ Youth Innovation Team (team no. CMA2023QN13). Support also came from Zhejiang Provincial Natural Science Foundation of China (grant no. LTGS23D050001). M.A., R.G.P., A.L.G. and M.R. were funded by NASA grant 80NSSC21K1369 to MIT. L.M.W. received funding from the European Union’s Horizon 2020 research and innovation programme under Marie Skłodowska-Curie grant agreement no. 101030750. A.L.G. and M.R. received funding from the Investigating HALocarbon impacts on the Global Environment (InHALE) NERC Highlight Topic (NE/X00452X/1). J.M., C.M.H. and R.F.W. were supported by NASA grants 80NSSC21K1210 and 80NSSC21K1201 to Scripps Institution of Oceanography. We acknowledge NOAA for the global emissions derived from their global network. We acknowledge the support from members of the Atmospheric Chemistry Research Group at University of Bristol and thank the UK Met Office for the support and licensing for NAME.

Author information

Authors and Affiliations

  1. College of Environmental Sciences and Engineering, Peking University, Beijing, China

    Minde An, Xingchen Zhao & Jianxin Hu

  2. Center for Sustainability Science and Strategy, Massachusetts Institute of Technology, Cambridge, MA, USA

    Minde An, Ronald G. Prinn, Anita L. Ganesan & Matthew Rigby

  3. School of Chemistry, University of Bristol, Bristol, UK

    Minde An, Luke M. Western, Simon O’Doherty & Matthew Rigby

  4. Department of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences, Fudan University, Shanghai, China

    Bo Yao

  5. Meteorological Observation Centre of China Meteorological Administration, Beijing, China

    Bo Yao & Wenxue Chi

  6. Shanghai Key Laboratory of Ocean–Land–Atmosphere Boundary Dynamics and Climate Change, Shanghai, China

    Bo Yao

  7. Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA

    Jens Mühle, Christina M. Harth & Ray F. Weiss

  8. Empa, Laboratory for Air Pollution/Environmental Technology, Swiss Federal Laboratories for Materials Science and Technology, Dübendorf, Switzerland

    Stefan Reimann & Martin K. Vollmer

  9. Zhejiang Institute of Meteorological Sciences, Hangzhou, China

    Honghui Xu & Yan Yu

  10. School of Geographical Sciences, University of Bristol, Bristol, UK

    Anita L. Ganesan

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Contributions

J.H., B.Y. and M.A. designed the research. M.A., supported by L.M.W., A.L.G. and M.R., conducted the regional inversion. B.Y., H.X. and Y.Y. provided measurement data. J.M., C.M.H. and R.F.W. contributed to the measurement data calibration. X.Z. and J.H. gathered some of the industry data. M.R. and L.M.W. provided the AGAGE 12-box model output of background mole fractions. M.A. led the writing of the paper, with contributions from R.G.P., L.M.W., M.R., S.R., J.M., M.K.V., W.C, S.O’D., B.Y. and all other authors.

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Correspondence to Minde An, Bo Yao or Jianxin Hu.

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Extended data

Extended Data Fig. 1 Spatial distribution of CCl4 emissions.

Each plot of (a-k) shows the spatial distribution of the top-down CCl4 emissions in Eastern Asia with focus on China in each year. The black dots represent the active measurement sites in the years. The purple triangles represent the chloromethane factories in China.

Extended Data Fig. 2 Subregional CCl4 emissions in China.

The emissions for each subregion were the mean values of the 2 × 105 samples from the MCMC method during inversion, with the error bars representing the corresponding 68% uncertainty intervals. The subregions in China were defined following a previous study39, North: Inner Mongolia, Hebei, Tianjin, Beijing, Shanxi; Northeast: Liaoning, Jilin, Heilongjiang; East: Shandong, Jiangsu, Anhui, Shanghai, Zhejiang, Jiangxi, Fujian; Central: Hunan, Hubei, Henan; South: Guangdong, Guangxi, Hainan; Southwest: Yunnan, Xizang, Sichuan, Guizhou, Chongqing; Northwest: Xinjiang, Qinghai, Gansu, Ningxia, Shaanxi. Hong Kong, Macao, Taiwan and ocean regions are not included in this study. Note that the definition of the subregion “east of China” here differs from the definition of “eastern China” used in the main text as defined by Park et al.11.

Extended Data Fig. 3 Emissions of CCl4 in eastern China.

The derived CCl4 emissions in eastern China in this study (red line) were compared to those derived in Park et al.11 (blue line). The emission values for this study and their error bars are the mean values and 68% uncertainty intervals of the 2 × 105 MCMC samples, respectively. The CCl4 emissions in eastern China in this study using measurements from the Chinese network exhibit a different trend without a distinct maxima from 2013-2017 (but similar magnitude) compared to Park et al.11 (using observations outside of China). The decline in CCl4 emissions from eastern China derived in this study started around 2018-2019, later than the decrease of CCl4 from eastern China in Park et al.11, but at a similar time to the decline in global emissions of CCl4 and CFC-111,28. This decline in CCl4 emissions from eastern China since 2019, including from regions identified with elevated CFC-11 emissions11,26 (see Extended Data Fig. 1), could be related to the end of CFC-11 production after 2019 in China. There was an additional drop in CCl4 emissions in this study for 2015-2016, which was also observed in reported global CCl4 emissions1 (Fig. 2a), but the reasons for this decline are not known. The difference between this study and Park et al.11 could be caused by the different sensitivities of observations to emissions in China between the site outside of China (South Korean site) and the Chinese network (Supplementary Figs. S3-4).

Extended Data Fig. 4 Unaccounted-for CCl4 emissions (a) and their proportion to top-down emissions (b) in China.

The red and grey lines represent the unexplained emissions considering or not considering ~5.1 Gg yr−1 of emissions due to legacy emissions and by-product emissions related to chlorine production and usage. All values shown are the mean values derived from the MCMC method, with error bars for the 68% uncertainty intervals. The linear regression trend for the unexplained CCl4 emissions in China (lines in plot (a)) is −0.73 ± 0.23 Gg yr−2 over the 11 years during 2011-2021. The linear regression trend for the proportion of unexplained emissions during the 11-year period from 2011 to 2021 is −0.023 ± 0.007 yr−1 when considering the legacy and chlorine-related emissions (red line in plot (b)), and −0.022 ± 0.006 yr−1 without these emissions (grey line in plot (b)). The negative linear regression trends for both the unaccounted-for CCl4 emissions and their proportion to top-down emissions in China are significantly different from zero at the 5% level (p < 0.05). Note that only the uncertainties from the top-down emissions are considered here. Including the uncertainties in the bottom-up emissions, which are currently unknown, would likely reduce the confidence in the calculated trends. However, we anticipate that systematic errors in the bottom-up emissions may dominate over random errors, meaning that the negative trends would persist, but with larger uncertainty in the magnitude of the emissions gap.

Extended Data Fig. 5 Market price of CCl4 in China.

The price data was the annual mean price for each year in Chinese Yuan per tonne, obtained from ChinaIOL30. Only data since 2019 were available.

Extended Data Table 1 Correlations between the mole fraction enhancements above baselines of CCl4 and CFC-12 at all sites
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Supplementary information

Supplementary Information (download PDF )

Supplementary Figs. 1–10, Texts 1–3 and Tables 1 and 2.

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An, M., Yao, B., Western, L.M. et al. Persistent emissions of ozone-depleting carbon tetrachloride from China during 2011–2021. Nat. Geosci. 18, 593–598 (2025). https://doi.org/10.1038/s41561-025-01721-4

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