Global trends in GOSAT observations

Figure 1 displays the trend in GOSAT observations between September 2009 and September 2015 for aggregate 2.0° × 2.5° latitude–longitude grid boxes. The trend in Fig. 1 is relative to the trend in the National Oceanic and Atmospheric Administration (NOAA) globally averaged marine monthly mean data20. These trends are also independent of changes in global average hydroxyl radical mixing ratios because they are relative to a global background trend.

Fig. 1 Trend in nadir GOSAT observations. The figure displays the trend between September 2009 and September 2015 minus the trend in the NOAA global marine observations. The GOSAT observations are averaged into 2.0° × 2.5° latitude–longitude boxes before fitting the trend, and the figure only displays boxes with more than 250 total observations. Red colors indicate that the GOSAT observations are increasing faster than the NOAA global marine average while green and blue colors indicate an increase slower than the NOAA average. China, India, tropical Africa, and tropical Asia show increases that are faster than the global average Full size image

The trend in atmospheric CH 4 mixing ratios in tropical Africa, sub-tropical Asia, and temperate Asia is large relative to the global mean trend (Fig. 1). Atmospheric CH 4 has been increasing globally since 2007 (e.g., refs. 21,22). This trend in sub-tropical Asia and tropical Africa is consistent with existing studies that show tropical regions have been driving these recent global CH 4 increases (e.g., refs. 21,22), and variability in these tropical fluxes is not well captured in existing bottom-up models (e.g., ref. 23).

By contrast, other regions of the globe do not exhibit such clear trends relative to the global mean. For example, the trend in many areas of the US is likely nominal relative to the global marine trend (Fig. 1). Additional analysis of US CH 4 trends are beyond the scope of the present study. Additionally, there is no clear pattern in Fig. 1 across the Amazon or adjacent regions. Large fires in the Amazon in 2010 emitted a pulse of CH 4 to the atmosphere, making it difficult to fit a simple multi-year trend for the region (e.g., ref. 24). Further, note that trends at high latitudes are highly uncertain due to data sparsity (e.g., ref. 25), and we do not discuss these regions in detail here.

Trends in emissions from China and Asia

Changes in atmospheric CH 4 mixing ratios over a particular region can be due to CH 4 emissions in that or any upwind regions, and an inverse modeling framework can be used to attribute patterns in atmospheric CH 4 to patterns in surface emissions.

We incorporate atmospheric CH 4 observations into an inverse model and find an increasing trend in CH 4 emissions across much of Asia, including in China and India (Figs. 2 and 3, Supplementary Figs. 2 and 3). The trend in total emissions from China is 1.1 ± 0.4 Tg CH 4 yr−1 (p = 0.058). Globally, CH 4 emissions have been increasing at a rate of ~5 to 8 Tg CH 4 yr−1 since 2007 (e.g., refs. 10,20). The emissions increase from China accounts for ~11–24% of this total global trend (95% confidence interval). The estimated trend in Indian emissions is less certain, on the other hand, at 0.7 ± 0.5 Tg CH 4 yr−1 (p = 0.25).

Fig. 2 Methane emissions estimates for China and India. a CH 4 emissions estimates for China from this study, Bergamaschi et al.10, Thompson et al.12, UNFCCC35, Peng et al.6, the EDGAR v4.3 inventory4, and the US EPA9; and b for India from this study, Ganesan et al. 28, UNFCCC53, and US EPA9. We find a trend in emissions from both countries for 2010–2015, though the trend for India is uncertain. Note that uncertainty estimates for this study are 95% confidence intervals, and uncertainty bounds for Bergamaschi et al.10 reflect the range of different inversions that use different datasets (e.g., in situ, satellite). Estimates marked with an asterisk are for anthropogenic emissions only. Furthermore, the dashed green line represents the posterior emissions estimate after subtracting the wetland emissions model, biomass burning inventory (GFED), and termite emissions Full size image

Fig. 3 Map of CH 4 emissions estimates. Total CH 4 emissions (anthropogenic plus natural) estimated using GOSAT observations and the inverse model (2010–2015 mean). CH 4 emissions from China are highest in provinces with large coal production and coal formations that contain high amounts of CH 4 (e.g., Shanxi, Guizhou, and Anhui; refer to Supplementary Fig. 1). Note that the inverse modeling emissions estimate is highly uncertain for any individual grid box, but those uncertainties decrease at increasing spatial scales (Supplementary Fig. 2) Full size image

These results show that the reported trend in China’s CH 4 emissions prior to 2010 has continued in subsequent years, in spite of regulations aimed at substantially reducing coal mining emissions. Top–down atmospheric studies generally indicate an annual trend of 1.0–1.2 Tg CH 4 for the 2000s (e.g., refs. 10,11,12), and we find that a trend of the same magnitude has continued past 2010. The results of this and earlier studies collectively indicate that China’s annual CH 4 emissions increased by ~50% between 2000 and 2015 (~20 Tg CH 4 ), an increase comparable to total annual anthropogenic CH 4 emissions from countries like Russia and Brazil4. By comparison, this increase is more modest than reported in earlier versions of EDGAR (3.3 Tg CH 4 yr−1 mean trend in EDGAR v4.2 for 2000–2010) but is similar to the newer version of EDGAR (1.6 Tg CH 4 yr−1 average trend in EDGAR v4.3 for 2000–2012).

The estimated emissions for India and China are also in good agreement with available in situ CH 4 observations across Asia. Modeled total column CH 4 using the estimated emissions have a smaller bias and correlate better with in situ observations relative to the prior emissions. Figure 4 displays model–data comparisons at four in situ sites—in China, Korea, and Japan. Two of these sites are mountain-top sites (panels a and c) while two are marine sites (panels b and d). None of these sites are included in the inverse model, providing an independent check on the emissions estimated using GOSAT observations.

Fig. 4 Observed and modeled CH 4 in situ mixing ratios. The sites shown are in situ monitoring stations from the NOAA Global Greenhouse Gas Reference Network20. Modeled CH 4 using the posterior emissions shows a lower root mean squared error (RMSE) at all sites. Note that all panels display monthly model and data means, and the blue modeled time series in this plot use EDGAR v4.2, the same inventory version used in the inverse model Full size image

Note that the total emissions estimate for India is in the mid-range of existing, global inverse modeling studies26,27. These earlier studies compare results from multiple global inverse models and report multi-model averages of 33 and 39 Tg yr−1, respectively (for 2000–2009 and 2003–2012, respectively). The emissions reported here (36 ± 2.5 Tg yr−1) are consistent with those studies. By contrast, a recent, regional inverse modeling study of India is an outlier compared to these studies at 22 Tg yr−1 (Fig. 2, 2010–2015 mean)28.

Contribution of various source sectors

We find a clear trend in CH 4 emissions from China’s coal sector while other source sectors do not show a corresponding trend (Fig. 5). By contrast, no source sector in India shows an obvious trend; the trend in total emissions from India is uncertain, and it is not clear what could be driving that trend, if one exists. This attribution is based upon the emissions estimate from the inverse model and the spatial distribution of different source sectors within the EDGAR emissions inventory. Specifically, we attribute emissions within each individual model grid box based upon the relative fraction of emissions that are due to each sector within that grid box in the EDGAR emissions inventory. Refer to the Methods for additional detail.

Fig. 5 Emissions trends by sector. a, b show the estimated emissions trend by sector for China and India, respectively. The coal sector appears to be driving the trend in China. No clear trends are obvious for India. Note that all trends in this figure are driven by the GOSAT observations, not by the EDGAR anthropogenic emissions inventory used in the inverse model; the inventory estimate is constant with time. The EDGAR inventory does not include uncertainty estimates for the sector-specific breakdown of emissions, and uncertainty estimates are therefore not included here. Figure 3 and Supplementary Fig. 2 present uncertainties for the total emissions Full size image

Additional lines of evidence also indicate that coal is likely driving the overall trend in China’s emissions. Coal production in China increased between 2010 and 2015 (from 3400 to 4000 million metric tons1) whereas ruminant populations and rice production have remained flat or grown only slightly. For example, milled rice production grew from 137,000 thousand metric tons in 2010/2011 to 140,850 thousand metric tons in 2016/201729. Beef production increased by only 8% between 2011 and 2016, and China’s dairy cattle inventory declined due to both decreasing dairy demand and increasing dairy and beef imports30,31.

Implications for coal mine methane

Overall, results indicate that CH 4 emissions from China have been increasing since 2010 and that this increase has not slowed. Existing bottom–up and top–down studies disagree on the magnitude and trend in CH 4 emissions from China (e.g., refs. 26,27), and the present study sheds additional light on these emissions. We find that, although China has set ambitious benchmarks, regulations, and incentives for CMM drainage and utilization since the mid-2000s, emissions continue to increase following a business-as-usual scenario. This increase in emissions is most likely driven by the coal sector, implying that China’s ambitious coal CH 4 actions have not produced a detectable change in the rate of increase in CH 4 emissions.

Existing studies from the US EPA and the International Energy Agency (IEA) have identified three broad barriers that China would need to overcome to meet its CMM targets (e.g., refs. 14,15,32). One or more of these barriers has presumably hampered China’s progress, and these studies help place the results presented here within a broader policy context.

First, insufficient infrastructure makes it difficult to bring CMM to market, and the US EPA cites this challenge as a potential barrier to achieving China’s CMM goals15,32. Most coal mines are located in remote mountainous areas, areas that are poorly connected to cities or natural gas infrastructure (ref. 32, ch. 7). Furthermore, the US EPA describes China’s gas market as “underdeveloped”, and only 22% of China’s non-rural population had access to natural gas as of 201015.

Second, inadequate technology likely presents an obstacle. Most coal mines in China are deep, and the coal seams are highly impermeable, unlike many mines in the US and Australia. The CMM drainage technology often used in China is poorly suited for these conditions14,32. As a result, the resulting CMM is often of poor quality (i.e., low CH 4 content), according to US EPA15. In addition, the IEA explains that operators of small and medium mines often lack the technical expertize to utilize the CH 4 for heating or electricity production14.

Third, inadequate or poorly-designed policies may stand in the way of reaching CMM utilization targets. US EPA explains that existing regulations and incentives have not been fully realized, and some may have backfired15. Utility companies often resist accepting electricity generated from CMM, in spite of policies that require utilities to give priority to this electricity. According to US EPA15, “The incentive program for CMM power plant utilization proved particularly difficult to implement due to resistance from power grid companies uneager to manage the complexities of dispatch of the fluctuating output of small CMM plants, and lacking a policy mechanism to pass the premiums through to consumers.” CMM utilization requirements may have also backfired. Government policy requires that all mines utilize drained gas with greater than 30% CH 4 content. EPA has anecdotal evidence that mine operators may be diluting drained gas to circumvent the requirement15. These actions not only render CMM unusable but also unsafe. In addition, the IEA points out that most local and provincial governments have limited power to enforce CMM regulations, limiting overall enforcement action14.

Existing inventories diverge on how China’s coal CH 4 emissions have changed since 2010 (e.g., refs. 4,9). Emissions factors (i.e., leak rates) provide a convenient means to compare these inventory emissions and estimated trends. Emissions factors in existing inventories range from ~5 to 11 m3 of CH 4 per metric ton of coal mined (weighted national average) (Fig. 6). We find emissions factors of ~6–7 m3 of CH 4 per metric ton, depending upon the year, by dividing coal emissions estimates presented here (Fig. 5) by China’s total coal production1,8. Note that we divide China’s official emissions estimate by its own coal production numbers while we divide the EDGAR emissions inventory by coal production numbers from the Energy Information Administration (EIA)1,8. These two coal production estimates contain similar trends but differ by up to ~5–10% in some years.

Fig. 6 Coal CH 4 emissions factors. Emissions factors implied by this study, EDGAR v4.34, US EPA9, UNFCCC35, Zhang et al.54, Schwietzke et al.55, Peng et al.6, and Zhu et al.7. The factors implied by this study are in the lower mid-range of existing estimates. In some cases, we divide total emissions by coal production numbers to derive emissions factors for this figure. For example, we divide the EDGAR emissions estimate by EIA coal production estimates1, and we divide Peng et al.6 and China’s UNFCCC report by production numbers from China’s Statistical Yearbook8, the production numbers used in those estimates. The two green lines in this figure are the inversion estimate divided by production numbers from EIA and China’s Statistical Yearbook, respectively. Note that Schwietzke et al.55 and Zhang et al.54 use time-invariant emissions factors, and these studies are therefore represented with solid lines Full size image

These emissions factors are similar to those in several existing inventories, but the trend is not. The emissions factors in many inventories have been declining with time and are forecasted to continue declining in future years (Fig. 6). These declines are often due to assumptions about improved energy technology and the forecasted effects of environmental regulations (e.g., ref. 7). By contrast, the emissions factors implied by this study show a slight upward trend from 2011 onward. This upward trend could be real, it could be due to uncertainties or errors in the inverse model and the associated source attribution, or it could point to inaccuracies in China’s coal production statistics. The EIA’s coal production numbers for China are uncertain, and there have recently been large discrepancies and an upward revision in China’s coal production and consumption statistics33,34. If the EIA were to underestimate China’s production trend, it could alias into the emissions factors and create a spurious upward trend. Note that the emissions factors from China’s National Bureau of Statistics also show a slight upward trend between 2005 and 2012. China submitted an explanation of its provincial coal emissions factors in 2012 along with its emissions estimate for 200535. We suspect that China used the same emissions factors in its 2012 emissions update, and changes in the emissions factor between 2005 and 2012 in Fig. 6 more likely reflect a shift in coal production to provinces with higher coal CH 4 content.