The WRF-STILT particle transport framework and optimal configuration have been extensively tested in several studies using midlatitude receptors (e.g., Sargent et al., 2018; McKain et al., 2015; Kort et al., 2013; McKain et al., 2012; Miller et al., 2012). WRF is configured with 41 vertical levels and two-way nesting in three domains, with the outermost domain covering nearly seven administrative regions (Figs. 1 and 2), defined according to convention in Piao et al. (2009). The domain resolutions from coarsest to finest are 27 km (d01), 9 km (d02), and 3 km (d03). Initial and lateral WRF boundary conditions are provided by NCEP FNL Operational Model Global Tropospheric Analyses at 1∘×1∘ spatial 6-hourly temporal resolution (NCEP, 1999). Nudging of fields is implemented in the outer domain only, and never within the planetary boundary layer (PBL). WRF output is evaluated against publicly accessible 24-hourly averaged observational datasets from the Chinese Meteorological Administration (CMA); finer temporal resolution meteorological data are not publicly available. WRF run details are presented in Dayalu (2017) and at 10.7910/DVN/OJESO0. A snapshot of results from comparison with China Meteorological Administration ground-station measurements is presented Sect. S1 and Figs. S1–S4 in the Supplement.

The STILT model is configured in backward time mode. The particle release point is set as the Miyun measurement sample inlet (the receptor). The inlet height is 158 ma.s.l., corresponding to 6 ma.g.l. In our study, the hilltop site was located in an area where the surrounding land was not very productive or intensively cultivated (Fig. S2). There is a long history of using short towers in low-productivity areas for regional studies (e.g., NOAA Earth Systems Research Laboratory – NOAA ESRL Barrow, Alaska, observatory at 11 ma.g.l.). In addition, the station is located on a small hilltop, so even though the actual inlet height above ground is low, it has a topographic advantage in that it effectively samples air from a greater height relative to the surroundings. Topographic advantage was exploited in a similar manner in Karion et al. (2016) in the context of an Alaskan CO2 study. However, Karion et al. (2016) were able to use a suite of additional data to confirm the validity of their assumption including comparisons to concurrent aircraft measurements and multiple inlets at 31.7, 17.1, and 4.9 ma.g.l. In our study, independent verification from concurrent aircraft measurements (for example) or multi-level inlet locations were not available to quantify the impact of absolute and relative inlet location on transport uncertainty.

Each hourly footprint (CO2 concentration attributed to each unit of flux as ppmµmol-1m2s) provides an estimate of surface influence on the measurement and is calculated from releasing 500 particles from the measurement site (receptor) until they reach the outer domain boundaries up to 7 d back in time. The STILT 0.25∘×0.25∘ footprint map for each measurement hour up to 7 d back in time enables assessment of regions in the study domain to which the receptor is most sensitive. These entire gridded footprints are convolved with anthropogenic and biogenic CO2 flux estimates to provide a final modeled concentration (ppm) of CO2 at the receptor. For clarity, we display the regions of importance to the receptor based on contours calculated from the overall STILT footprints at the 50th (L_0.50 region), 75th (L_0.75 region), and 90th (L_0.90 region) percentile levels (Fig. 2). The percentile contours are calculated as follows: the average (seasonal, annual) footprints from 2005 to 2009 are ordered from high to low. We multiply each fraction (0.5, 0.75, 0.9) with the summed footprints and use cumulative sums of the ordered footprints as a guide to select all points with influence magnitude equal to or greater than this cutoff value. Figure S11 illustrates a single footprint map along with the average influence and a plot of cumulative influence to demonstrate the percentile-level selection process. We emphasize that we use the entire STILT footprint convolved with fluxes to estimate the receptor CO2 concentration. We only use the L_0.90 region to provide a reasonable area across which to ascribe the effective inventory adjustment (converted from parts per million model–observation mismatch to mass units). As Fig. S11c shows, the L_0.90 region strikes a balance between capturing sufficient influence while avoiding an unrealistically large adjustment region for a single observation site. Conversely, corrections based on the smaller L_0.75 region would include larger uncertainties from the diffuse influence of emissions outside the L_0.75 region (not accounting for 25 % of average surface sensitivity), yet the model–observation mismatch would be ascribed to a region approximately half the area of the L_0.90 region. Deriving correction factors based on integration over the entire L_0.90 region is a more conservative approach where the model–observation mismatch in mass units is distributed over a larger area.

Further model details are available in Sect. S2. Complete WRF-STILT settings and STILT footprint files are available from 10.7910/DVN/OJESO0.

ZHAO, EDGAR, and CDIAC report estimates of total annual emissions of CO2 at 0.25∘×0.25∘, 0.1∘×0.1∘, and 1∘×1∘ original grid resolutions, respectively. We regridded the EDGAR and CDIAC inventories to the 0.25∘×0.25∘ resolution, using the NCAR Command Language version 6.2.1 Earth System Modeling Framework “conserve” regridding algorithm to preserve the integral of emissions (Brown et al., 2012). Differences between annual total emissions for EDGAR and CDIAC inventories introduced by regridding are smaller than the interannual trends or differences between the inventories (Sect. S3 and Fig. S5). We present the main components and defining features of the three anthropogenic CO2 inventories below.

The ZHAO inventory provides estimates of total annual emissions for 2005 through 2009. In addition, the spatial location of emissions is given for years 2005 and 2009 on a 0.25∘×0.25∘ grid. Using 2005 and 2009 gridded values, we calculate an average percent contribution of each grid cell to the total emissions. The average contributions are used as weights to spatially allocate 2006, 2007, and 2008 total annual emissions. We evaluate and justify this assumption in detail in Sect. S3 and Fig. S6. The ZHAO inventory represents one of the first statistically rigorous bottom-up CO2 inventories for China. It relies on provincial- and facility-level data rather than national-level data, which has been noted previously as a major uncertainty in Chinese emission inventories; total CO2 emissions estimates based on provincial data are typically higher than those using national statistics (Zhao et al., 2013). Satellite observations of criteria air pollutants (e.g., nitrogen dioxide, which serves as a proxy for fossil fuel combustion) show greater agreement with provincial statistics (Zhao et al., 2012). The increased use of China-specific emission factors and activity levels based on domestic field studies is a shift from other inventories that rely heavily on global averages to estimate processes occurring in China. Despite the increased incorporation of China-specific field data, the largest sources of uncertainty to the ZHAO inventory are industrial emission factors, and activity levels across all sectors. Total uncertainty in the inventory is estimated as -9 % to +11 % (Zhao et al., 2012).

The EDGAR emissions database continues to be a major prior in atmospheric studies, and the CO2 inventory is used to inform key global scientific results considered by the UNFCCC Conference of Parties. The EDGAR global inventory (atemporal EDGAR v4.2 FT2010 gridded emissions) takes total annual estimates of national emissions and downscales emissions to a 0.1∘×0.1∘ as a function of road and shipping networks, population density, energy and manufacturing point sources, and agricultural land. Estimates for China are available for all 5 years as gridded inventories. Reported uncertainties for global emissions are ±10 % (https://themasites.pbl.nl/tridion/en/themasites/edgar/documentation/uncertainties/index-2.html, last access: 10 February 2020). However, this applies to global averaged uncertainty; we expect uncertainty for China to be much higher.

We include the CDIAC inventory here due to its historical prevalence as a benchmark inventory for global indicators, including evaluations of carbon intensity provided by the World Bank (World Bank, 2017). The CDIAC inventory (v2016; 10.3334/CDIAC/ffe.ndp058.2016) allocates estimates of national emissions to a 1∘×1∘ grid, primarily distributed according to human population density. A thorough assessment of 2σ uncertainties in the CDIAC spatial allocation of emissions shows considerable spread in regional uncertainties (Andres et al., 2016).

Our study is not intended to be an exhaustive sampling of inventory approaches but serves to demonstrate the utility of continuous high-accuracy observations as a top-down constraint on emissions evaluations. Our inventory list notably does not include emerging spatially resolved global inventories (e.g., Open Data Inventory for Anthropogenic Carbon Dioxide, ODIAC) (Oda et al., 2018) that were not readily available at the time this work was conducted. At 1×1 km, ODIAC does have a high spatial resolution of nightlight proxy-based emissions; while this is a valuable method for regions in Europe and North America for example, it is less valuable for China where it is analogous to the CDIAC population-based proxy. In China, power plant emissions are typically located far from end-use regions and the night-light proxy can often break down (R. Wang et al., 2013). Furthermore, ODIAC power plant emissions use the 2012 Carbon Monitoring for Action (CARMA) database, which notably does not incorporate China-specific power plant data; in these instances, CARMA categorizes China's power plants as “non-disclosed plants” and reports using estimates derived from statistical models using averaged emissions factors – comparable to methods in global inventories subset over China (Ummel, 2012). One of our main goals is to quantify model–observation mismatch associated with use of China-specific power plant data, and ODIAC does not address that issue particularly differently from other global emissions inventories subset over China. For completeness, however, evaluation of global inventories like ODIAC and a suite of increasingly available China-specific inventories (e.g., MEIC) would provide value as part of future model–observation comparison efforts.

Based on multi-year means (2005 to 2009) and 95 % confidence intervals derived from two-sample t tests, we find that within the L_0.90 evaluation region EDGAR and CDIAC report emissions that are significantly lower than ZHAO by typically 20 % (-24 %, -16 %) and 36 % (-37 %, -34 %), respectively. Across China's administrative regions, the highest discrepancy between the global and regional inventories is in northern China (ZHAO is approximately 30 % higher than both EDGAR and CDIAC). In addition, northern China represents one of the administrative regions with the highest CO2 emissions density (2300 to 3300 MgCO2km-2, compared to the average of 700 MgCO2km-2 averaged across China) and is therefore a particularly rich spatial subset for emissions inventory evaluation. A detailed breakdown of emissions by region of China is provided ins Table S1 in the Supplement. Spatial differences are displayed in Fig. S7.

Previous work has found that temporal variations in CO2 sources can be significant and surface CO2 can be perturbed by between 1.5 and 8 ppm within source regions based on the time of day and/or day of week, resulting from a combination of changes in activity patterns as well as synoptic-scale transport effects (Nassar et al., 2013). However, appropriate data for establishing reasonable temporal scaling factors for data-sparse regions such as China are difficult to obtain, and as in the case of Nassar et al. (2013) China's activity factors are based on US activity factors weighted according to China's EDGARv4.2 emissions patterns. We applied the weekly and diurnal Nassar et al. (2013) scaling factors to our emissions, but these did not generate statistically significant differences from the unscaled versions. These statistically insignificant results suggest that a more rigorous set of temporal scaling factors need to be developed for China. CDIAC does provide monthly gridded inventories with seasonality embedded. However, predictions based on that seasonality deviated even further from the observations than predictions based on constant annual emissions. In the CDIAC global dataset, the seasonality in emissions is based upon generalized global activity factors that are not necessarily appropriate for estimating seasonality of human activity in China. Therefore, in this study we do not explicitly consider diel and seasonal variation in anthropogenic CO2 fluxes.

We prescribe biotic contributions to the CO2 signal by adapting the VPRM model output for the study domain to generate 0.25∘×0.25∘ gridded estimates of hourly CO2 net ecosystem exchange (NEE) from 2005 to 2009. Details of the VPRM model and output for China are presented in Dayalu et al. (2018a). The VPRM is driven by 8 d 500 m MODIS surface reflectance values and 10 min averages of WRF downward shortwave radiation and surface temperature fields. The VPRM parameters are calibrated using eddy flux measurements in the study domain representing each ecosystem type classified according to the International Geosphere-Biosphere Programme (IGBP) scheme. Calibration and evaluation eddy-flux data are obtained from FluxNet and ChinaFlux collaborators. The L_0.90 region is dominated by croplands (Fig. S8), in particular the winter wheat and corn dual cropping that characterizes the North China Plain (Dayalu et al., 2018a). We use one biosphere model in this study to simplify our assessment of variations across the different emissions inventories. Our selection of the VPRM in particular is based on results from Dayalu et al. (2018a), where the VPRM was shown to have significantly lower regional bias than an ensemble of global 3-hourly flux products subset over China.

Appropriate quantification of background CO2 concentrations (i.e., the CO2 concentration at the lateral edges of the model domain and/or prior to interaction with domain surface processes) enables realistic assessment of the study domain's contribution to atmospheric CO2 at varying timescales. CT2015 estimates of CO2 concentrations are provided on a 3∘×2∘ grid at upwind background locations. Background values are selected and corrected for large-scale biases using methodology similar to Karion et al. (2016) where a particle must originate from the outermost domain edge and/or 3000 ma.s.l.; further details are provided in Sect. S4. The predicted background CO2 is shown in Fig. 3a together with observed CO2 at Miyun for the 11:00–16:00 LT period over the 5-year observational record. For most of the year the measured CO2 shows large enhancements above background and only in midsummer is there a small depletion relative to background values.

We define hourly ΔCO2 as a regional change (enhancement or depletion) imparted to concentrations of CO2 advected from the boundary (CO2,CT2015) such that, for each observation hour ΔCO2,obs, 1ΔCO2,obs=CO2,obs-CO2,CT2015. For each modeled hour ΔCO2,mod, i and j represent the surface grid cell locations and h represents the hour of the 7 d back trajectory: 2ΔCO2,mod=∑0h-168h∑ijfootij×(ANTHij+VPRMij). Note that for the modeled enhancement or depletion, only the VPRM fluxes change hourly; as stated previously, the annual anthropogenic fluxes are atemporal.

Without a sufficiently dense network of high-temporal-resolution observations, a full-scale inverse modeling approach to inventory scaling is inappropriate. At annual timescales, where anthropogenic sources dominate the CO2 signal, we compare annual observed and modeled ΔCO2 to define a mean bias and derive a scale factor to quantify the model–observation mismatch based on the slope of the comparison. Isotopic analysis of atmospheric CO2 from a site in Beijing in 2014 suggests that annually the fossil fuel burning does dominate the region, contributing 75 % ± 15 % to the annual signal (Niu et al., 2016). Annually, the biospheric impact in the region is not zero; rather, the anthropogenic signal dominates. The biospheric quantity of relevance annually is the net carbon flux as a balance of GPP and respiration and is highly uncertain in both sign and magnitude in this region (Piao et al., 2009). In the Piao et al. (2009) study, regional inversions are based on the very limited dataset of nine sites across all of Asia. Our assumption of dominant anthropogenic influence in northern China is in keeping with the priors and process-based models from the relevant regions in Piao et al. (2009) that assume zero and are not significantly corrected by relatively poorly constrained inversions. At seasonal timescales, we use the difference between observed and modeled ΔCO2 normalized by L_0.90 area to obtain a mass flux offset that combines vegetation and anthropogenic inventories. With the available data it is not possible to independently evaluate both the anthropogenic and biogenic CO2 fluxes. For further details of the scaling technique, please refer to Sect. S5.

As shown in Fig. 2, we find strong seasonality in the footprint percentile contours, in agreement with previous analysis of Miyun observations by Wang et al. (2010). At annual timescales, the L_0.90 region is comparable to the WRF d02 extent. Northern China, including Inner Mongolia, dominates the L_0.90 region both seasonally and annually. Due to the heavy biosphere influence in the regional growing season, previous work by Wang et al. (2010) used Miyun non-growing-season measurements of CO2 and carbon monoxide (CO) as an anthropogenic tracer to estimate combustion efficiency for China. When compared to bottom-up estimates of national combustion efficiency, observations suggested 25 % higher combustion efficiency than bottom-up estimates of national combustion efficiency; however, Wang et al. (2010) note that the regional (northern China) and seasonal (winter) subsets could contribute to such a discrepancy. The seasonality exhibited in Fig. 2 indeed suggests that combustion efficiency estimates derived from non-growing-season measurements alone do not represent anthropogenic processes in provinces south of Miyun that are visible in the observations primarily during the growing season. Low-emitting regions northwest of Miyun such as Inner Mongolia influence the site more in the fall and winter relative to other seasons. In the spring and summer, higher-emitting regions in provinces south of Miyun are more influential. However, non-growing-season CO2 is influenced by often inefficient district heating in the northwest. And, while growing-season CO2 is influenced by intense urban activities from Beijing and other cities to the south, vegetation draws down both background and locally observed CO2 significantly (Fig. 3a).

We evaluate unscaled model performance relative to observations at hourly, seasonal, and annual timescales. While inventory scaling is performed at the policy-relevant scales of seasons and years, examination of the models at shorter timescales provides insight into model bias and error aggregation at longer timescales. Table 1 summarizes hourly model bias across all years and pooled by season.

All modeled hourly quantities include the same biological component from VPRM, background concentrations, and transport models such that the only source of variation among models is the anthropogenic inventory. With a few exceptions that are discussed in the following sections, CO2,EDGAR+VPRM, CO2,CDIAC+VPRM, ΔCO2,EDGAR+VPRM, and ΔCO2,CDIAC+VPRM systematically underestimate observations, as indicated by larger deviation below the 1:1 line in the comparison of modeled to measured ΔCO2 (Table 1, Fig. 3b–d.)

We quantify model–observation mismatch by estimating the additive flux corrections at seasonal timescales and multiplicative corrections at annual timescales. We emphasize that these “corrections”, or scalings, are not optimizations; rather, they simply reflect the extent to which the individual anthropogenic + VPRM flux models deviate from the observations. Complete seasonal and annual scaling results are provided in Sect. S5 and Tables S2–S3.

The observational record informing the scaling integrates the biological and anthropogenic signals. At the seasonal scale, where biological processes are significant contributors to the signal, we scale the sum of the anthropogenic and biological fluxes (Fig. 7). Scaled non-growing-season flux estimates are higher than unscaled values, partially accounting for the VPRM generally underestimating ecosystem respiration by an additive offset throughout the year (Dayalu et al., 2018a). The multi-year seasonal results in Table 1 suggest that this offset can aggregate to a 1–2 ppm difference; the result would be a shift in baseline rather than overall pattern for each of the three simulations. As the vegetation and all other components are controlled across models, the inter-model variance reflects the relative performance of the anthropogenic estimates. We find that in the non-growing months the original ZHAO + VPRM inventory typically remains within the 95 % confidence bounds of the scaled inventory. However, both EDGAR + VPRM and CDIAC + VPRM are consistently significantly lower than their scaled counterparts. At least in the winter, where biogenic processes are at a minimum, this suggests that both EDGAR and CDIAC underestimate anthropogenic emissions, and that ZHAO estimates are closer to actual emissions. Improved representation of temporal anthropogenic activity factors and biosphere processes are needed to extend the conclusions of anthropogenic inventory performance to all seasons. In the absence of such data, it is not possible to conclusively state whether model–data mismatch is rooted in anthropogenic emissions biases or biogenic biases. During the growing seasons, however, the afternoon vegetation signal is significant, and the picture is more complex. In the spring, the CO2 signal at Miyun is significantly affected by the North China Plain winter wheat growing season. The effect of scaling in the spring from 2005 to 2007 is to increase CO2 emissions with a net positive seasonal flux; however, in 2008 and 2009 we find the net seasonal flux becomes negative such that uptake dominates emissions. The prior models in all cases predict positive flux. During the summer months, ZHAO + VPRM predicts more emissions and/or less uptake relative to EDGAR + VPRM and CDIAC + VPRM. Scaling of summertime fluxes serves to significantly increase ZHAO + VPRM uptake estimates; the EDGAR + VPRM and CDIAC + VPRM prior estimates are within the 95 % confidence bounds of the scaling for reasons discussed previously.

We report annual scaled anthropogenic inventories in the L_0.90 region in Fig. 8 and Table 2 as MtCO2 yr-1. As discussed previously, the annual scalings are applied only to the anthropogenic inventory, as the signal at the annual timescale is effectively dominated by anthropogenic emissions; net ecosystem fluxes are expected to be relatively minor in the L_0.90 region in comparison. For all years, the emissions estimated by the original ZHAO inventory lie within the 95 % confidence bounds of the scaled ZHAO inventory. However, for EDGAR and CDIAC, the original inventories consistently underestimate observations. Averaged over the 5-year study period, EDGAR and CDIAC lead to modeled estimates of CO2 mixing ratios that are typically lower than observations by 30 % and 70 % respectively (Fig. 6). Averaged across the 5 years, this translates to EDGAR and CDIAC being scaled relative to their unscaled values in the L_0.90 region by 1.3 and 1.7, respectively (Fig. 8; Table 2). In the case of EDGAR, we note a general increase in observational agreement from 2005 to 2009.

We examine the statistical significance of the inter-annual observed concentration and enhancement differences using a two-sample t test (Table 3). The observed concentrations including advected global background (Fig. 6a–c) display an overall increasing trend of 1.87 (1.8, 1.9) ppm CO2 yr-1 between 2005 and 2009, in agreement with flask samples obtained from nearby WMO sites between 2007 and 2010 (Liu et al., 2015). The inter-annual increases are statistically significant (Table 3). However, when we remove the modeled background to more closely examine regional patterns that would otherwise be drowned out by the global signal, we find that the regional ΔCO2 trend (Fig. 6d–f; Table 3) does not parallel the increasing global CO2 trend (Fig. 6a–c; Table 3). Regionally, the observed enhancements increase from 2005 to 2006 and plateau in 2007 before decreasing in 2008. Regional ΔCO2 increases again in 2009. Earlier work by Wang et al. (2010) extended the Miyun observations of CO2 growth rate to all of China and estimates a lower CO2 growth rate than previously suggested. However, Fig. S6 suggests local reductions in regions influencing Miyun, possibly in preparation for the Beijing Olympics, are partially offset by increases elsewhere. A larger network of sites would be needed to quantify this further in order to evaluate the CO2 growth rate for other regions in China and for China as a whole.

In Fig. 9a we estimate gross regional product (GRP) for 8 of China's 34 provincial-level administrative units, specifically those encompassed significantly by the L_0.90 region: Beijing, Tianjin, Henan, Shanxi, Shandong, Hebei, Inner Mongolia, and Liaoning. Using data from the International Monetary Fund (IMF; https://www.imf.org/en/Data, last access: 9 February 2020) and World Bank (World Bank, 2017) we retrieved the GDP for each of the above provinces and summed them to estimate the GRP. GDP calculations are inherently uncertain and were available as single values for each province per year. A more extensive economic analysis to estimate the uncertainty of these values is beyond the scope of this study. Key economic events occurred during the study time period and are likely contributors to the observed interannual variation in regional CO2 emissions (Fig. 6d–e) and a doubling of GRP from 2005 to 2009 (Fig. 9a). In particular, the time period from 2005 to 2009 saw industrial energy efficiency improvements which began in 2007 under the 11th FYP, preparations for and staging of the 2008 Beijing Summer Olympics, the global financial crisis in late 2008, and a large Chinese fiscal stimulus in 2009. We further note that the global financial crisis of 2008 correlates with a plateauing of the percentage contribution of northern China GRP to national GDP (Fig. 9a).

As policy targets are often measured as relative changes over multiple years, an important component of emissions inventories is their ability to accurately capture multi-year changes. Observations indicate enhancements above background CO2 increased by 28 % (22 %, 34 %) between 2005 and 2009. ZHAO + VPRM estimates a 20 % increase over the same time period while EDGAR + VPRM and CDIAC + VPRM estimate 61 % and 56 % increases respectively.

China has pledged a 60 %–65 % reduction in carbon intensity by 2030 and has additionally set a benchmark of 40 %–45 % reduction in carbon intensity by 2020, where both targets are relative to the baseline year 2005 (NDRC, 2015; Guan et al., 2014). However, Guan et al. (2014) found that provincial trends in carbon intensity can vary significantly from national trends. Using the GRP values shown in Fig. 9a, we calculate a northern China regional carbon intensity incorporating the eight provinces encompassed significantly by the L_0.90 region (Fig. 9c). We also estimate an L_0.90 regional carbon intensity based on the official national energy-related CO2 emissions in NDRC (2015); we scale the national total by 39 % (35 %, 42 %), which is the mean (range) contribution of the L_0.90 region to the national emissions in 2005, averaged across the three unscaled gridded emissions inventories. We emphasize that carbon intensity values are inherently uncertain due to complexities in GRP and GDP calculations such as double-counting due to inter-provincial trade or spatial mismatch between emissions and economic data. Nevertheless, the analysis provides valuable insight into trends rather than precise values.

Over the study time period, the GRP of the L_0.90 region more than doubled (Fig. 9a), exhibiting a moderate, positive correlation with the increasing trend in emissions (Fig. 9b). Coinciding with the 2008 Beijing Summer Olympics, the region's contribution to China's GDP grew from approximately 13.5 % in 2007 to nearly 16 % in 2008, representing a 20 % increase, before plateauing into 2009 (Fig. 9a). As noted in Guan et al. (2014), reductions in carbon emissions intensity can come about via two main pathways: the first, within industries, through increased energy efficiency combined with expanded production capacity; the second, across the economy, through structural shifts from energy-intensive industrial sectors to service sectors. The doubling of GRP with the apparent reduction in regional carbon intensity suggests a combination of enlarged production capacity (including production of higher valued goods) and a shift toward a service-oriented economy. In the former instance, a larger production capacity tends to reduce the overall energy (and, therefore, carbon) consumption of a single production unit. In the latter instance, the energy consumption by the service sector is considerably lower than that required by industrial and manufacturing processes. In the northern China region, however, industry continues to dominate the economy, suggesting that carbon intensity reductions are more due to enlarged production capacity. From 2005 to 2009, carbon intensity for the L_0.90 region decreased by 47 % (28 %, 65 %), based on a one-sample t test of pooled emissions intensity changes across scaled inventories. Analysis presented by organizations such as the World Bank (World Bank, 2017) suggests China's carbon intensity at the national level decreased by 20 % in 2009 relative to 2005. However, we note that the carbon emissions data source for the World Bank carbon intensity calculations is CDIAC. We have shown that at least for the L_0.90 region, CDIAC emissions lead to significant underestimates of observations. Our work here suggests that carbon accounting organizations such as the World Bank would benefit from basing their national estimates for China on a variety of inventories, incorporating increasingly available China-specific approaches (including but not limited to MEIC and PKU), EDGAR, and newer global inventories such as ODIAC. However, we emphasize a crucial point with respect to the value of carbon intensity targets, in agreement with Guan et al. (2014): carbon intensity targets are especially misleading in developing countries where absolute emissions continue to significantly grow in concert with economic development goals. We see that despite the decreasing carbon intensity of the region, pooled emissions estimates from the three scaled inventories suggest an 18 % increase in absolute emissions from 2005 to 2009 (Table 2, Fig. 9b). In terms of the climate impact, it is the absolute carbon emissions rather than the carbon intensity that ultimately matters.