dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2012; Boden et al. , 2016; Oda et al. , 2018). Along with total emissions estimates, the spatial distribution of emissions is important for several reasons: (1) spatial gridded products enhance our basic understanding of CO 2 emissions; (2) spatial distributions are key inputs (as priors) for transport and data assimilation models, which influence the carbon budget  (Bao et al. , 2020) ; and (3) spatial distributions can be used for policy making toward emissions reductions and can provide useful information for the deployment of instruments in emissions monitoring for highemissions areas recognized by multiple inventories  (Han et al. , 2020). At the global level, gridded emissions datasets are often based on the disaggregation of country-scale emissions  (Janssens-Maenhout et al. , 2019; Wang et al.
, 2013). Thus, gridded emissions data are subjected to errors and uncertainties due to total emissions calculations and emissions' spatial disaggregation  (Andres et al. , 2016; Oda et al. , 2018; Oda and Maksyutov, 2011). For example, the Carbon Dioxide Information Analysis Center (CDIAC) distributes national energy statistics at a resolution of 1 • ×1 • using population density as a proxy  (Andres et al. , 2011 (Andres et al. , , 2016)). Further, to improve the spatial resolution of the emissions inventory, the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) distributes national emissions based on CDIAC and BP statistics with satellite nighttime lights and power plant emissions  (Oda et al. , 2018; Oda and Maksyutov, 2011). EDGAR derives emissions from the energy balance statistics of the International Energy Agency (IEA) and obtains country-specific activity datasets from BP plc, the United States Geological Survey (USGS), the World Steel Association, the Global Gas Flaring Reduction Partnership (GGFR)/US National Oceanic and Atmospheric Administration (NOAA), and the International Fertilizer Association (IFA).
Gridded emissions maps at a resolution of 0. 1 • × 0. 1 • are produced using spatial proxy data based on population density, traffic networks, nighttime lights, and point sources, as described in  Janssens-Maenhout et al. (2019). Based on subnational fuel, population, and other geographically resolved data, a high-resolution inventory of global CO 2 emissions was developed at Peking University  (Wang et al. , 2013). To accurately calculate emissions, a series of efforts have been made to quantitatively evaluate China's CO 2 emissions using national or provincial activity data, local EF, and detailed datasets of point sources  (Cai et al. , 2018; Li et al. , 2017; Wang et al. , 2013). The China High Resolution Emission Database (CHRED) was developed by  Cai et al. (2018)  and  Wang et al. (2014)  based on provincial statistics, traffic networks, point sources, and industrial and fuel-specific EF.
CHRED was featured based on its exclusive point source data from 1. 58 million industrial enterprises from the First China Pollution Source Census. The Multiresolution Emission Inventory for China (MEIC) was developed by  Zhang et al. (2007) ,  Lei et al. (2011), and Liu et al. (2015a)  at Tsinghua University through the integration of provincial statistics, unit-based power plant emissions, population density, traffic networks, and EF  (Li et al. , 2017; Zheng et al. , 2018a, b). The MEIC uses the China Power Emissions Database (CPED), in which the unit-based approach is used to calculate emissions for each coal-fired power plant in China with detailed unit-level information (e. g. , coal use, geographical coordinates). Regarding mobile emissions sources, a highresolution mapping approach is adopted to constrain vehicle emissions using a county-level activity database.
, 2017; Han et al. , 2020) , spatial scales  (Wang and Cai, 2017) , and point source data. Importantly, the authors note that the lack of a comprehensive understanding and comparison of the potential uncertainty in estimates of China's FFCO 2 , including spatial, temporal, proxy, and magnitude components, cause Chinese emissions data to be more uncertain, and thus it is important to present, analyze, and explain such differences among inventories. Here, we evaluated the uncertainty in China's FFCO 2 estimates by synthesizing global gridded emissions datasets (ODIAC, EDGAR, and PKU) and China-specific emission maps (CHRED, MEIC, and the Nanjing University CO 2 (NJU) emission inventory). Moreover, several other inventories were used in the evaluation analysis, such as the Global Carbon Budget from the Global Carbon Project and the National Communication on Climate Change of China (NCCC).
Emissions data An evaluation analysis was conducted from nine inventories including six gridded datasets (listed in Table  1 , ODIAC, EDGAR, PKU, CHRED, MEIC, and NJU) and three other datasets (GCP/CDIAC, CEADs, and NCCC) containing statistical data. We selected the year 2012 for spatial analysis because this is the most recent year available for all the gridded datasets and also because 2012 was a peak year of emissions due to the strong reductions following the impact of the 12th Five-Year Plan. Specifically, the global fossil-fuel CO 2 emissions datasets included the year 2017 version of ODIAC (ODIAC2017), version 4. 3. 2 of EDGAR (EDGARv4. 3. 2), and PKU-CO 2 , all of which use the Carbon Monitoring for Action (CARMA) as the point source. The China-specific emissions data used were from 2007 from CHRED, MEIC v1.
3, and NJU-CO 2 v2017, all of which use China Energy Statistical Yearbook (CESY) activity data. Moreover, three inventories were used as references, i. e. , GCP/CDIAC, CEADs, and NCCC, because GCP and ODIAC use CDIAC for the majority of the years, except the most recent 2 years, which were extrapolated using BP data. These three inventories were treated as an inventory in a time-series comparison. Data were collected from the official websites of ODIAC, EDGAR, PKU, and six tabular statistical datasets and were also acquired from the authors who developed CHRED, MEIC, and NJU. See the supporting information for more details on the data sources and the methodology used for each dataset.
Methodology for the evaluation of multiple datasets We evaluated the abovementioned datasets from three aspects: data sources, boundary (emission sectors), and methodology (Fig. 1 , Tables  1  and S1  and S2 in the Supplement). In regard to the data source, there are two levels: national data, such as UN or IEA statistics, and provinciallevel data, such as CESY. The emission sectors mainly include fossil-fuel production, industry production and processes, households, transportation, aviation/shipping, agriculture, natural biomass burning from wildfires, and the waste from these datasets; Table  S1  in the Supplement lists the sectors included in each inventory. In addition, for methodology, the analysis of the inventories includes the total estimates (activity data and EF) aspect and the spatial disaggregation of point, line, and area sources.
Total emissions and recent trends The interannual variations of China's CO 2 emissions from 2000 to 2016 were evaluated from six gridded emission maps and three national total inventories (Fig. 2 ). All the datasets show a significant increasing trend in the period of 2000 to 2013 from 3. 4 to 9. 9 Gt CO 2. The range of the nine estimates increased simultaneously from 0. 7 to 2. 1 Gt CO 2 (both are 21 % of the corresponding years' total emissions). In the second period (from 2013 to 2016), the temporal variations mostly levelled off or even decreased. Specifically, the emissions estimated from PKU and CEADs showed a slight downward trend, even though they used independent activity data from IEA (2014) and the National Bureau of Statistics (2016), and this downward trend was attributed to changes in the industrial structure, improved combustion efficiency, emissions control, and slowing economic growth  (Guan et al.
, 2018; Zheng et al. , 2018a). There is a large discrepancy among the current estimates, ranging from 8. 0 to 10. 7 Gt CO 2 in 2012. NJU had the highest emissions during the periods of 2005-2015, followed by EDGAR, MEIC, and CDIAC/GCP/ODIAC, while CEADs (National) and PKU were significantly lower (Fig. 2 ). These discrepancies are mainly because of three reasons: (1) the EF for raw coal was greater for EDGAR and ODIAC than the other databases. The EFs were different for different fossilfuel types and cement production (Table  S2  in the Supplement). Because coal consumption constituted 70 %-80 % of total emissions, the coal EF was more significant than the others. The EFs were different for the three major fossilfuel types (raw coal, oil, and natural gas) and cement production (Tables  1  and S2  in the Supplement).
In addition, the EFs were obtained from either the IPCC default values or local optimized values from different sources. The EFs do not change over time in these inventories, although they should, due to the unavailability of EFs over time; (2) differences in activity data; i. e. , NJU, MEIC, and CEADs (Provincial) use provincial data from National Bureau of Statistics (2016), while CEADs (National) and PKU use national data from CESY (2016) and IEA (2014), respectively (Tables  1  and S1  in the Supplement), such that the sum of provincial emissions is higher than the national total; and (3) differences in emission definitions (Tables  1  and S1  in the Supplement, emissions sectors). Although we tried to ensure that these datasets would be as comparable as possible, minor differences in emissions sources (sectors) remained.
For example, EDGAR contains abundant industry process-related emissions, whereas CEADs only considers cement production  (Janssens-Maenhout et al. , 2019). EDGAR and MEIC have similar trends, except for their magnitudes, and MEIC is usually greater than EDGAR. This is a combined effect of the above three reasons. Moreover, MEIC uses provincial energy data from CESY (2016), whereas EDGAR uses national-level data from IEA (2014). However, MEIC's EF is lower than that of EDGAR. These opposing effects bring the datasets closer in magnitude. Both the gridded products (ODIAC, EDGAR, MEIC, and NJU) and national inventory (GCP/CDIAC) show small differences in the magnitude of total emissions estimates and trends from 2000 to 2007, where the differences in magnitude increase gradually from 2008 onward.
Spatial distribution of FFCO 2 emissions The evaluation of spatially explicit FFCO 2 emissions is fundamentally limited by the lack of direct physical measurements at the grid scale (e. g. ,  Oda et al. , 2018). Thus, we attempted to characterize the spatial patterns of China's carbon emissions by presenting the available emissions estimates. We compared six gridded products, including ODIAC, EDGAR, PKU, CHRED, MEIC, and NJU, for the year 2012, which was the most recent year for which all six datasets were available. Spatially, the CO 2 emissions from the different datasets are concentrated in eastern China (Fig. 3 ). The high-emission areas were mostly distributed in city clusters (e. g. , Beijing-Tianjin-Hebei (Jing-Jin-Ji), the Yangtze River Delta, and the Pearl River Delta) and densely populated areas (e.
CO 2 emissions at the provincial level The provincial-level results showed more consistency than the grid-level results in terms of spatial distribution. All the products agreed that the eastern and southern provinces were high emitters (> 400 Mt CO 2 yr -1 , Figs. 4 and S3 in the Supplement), while the western provinces were low emitters (< 200 Mt CO 2 yr -1 , Figs. 4 and S3 in the Supplement). The five most emitting provinces were Shandong, Jiangsu, Hebei, Henan, and Inner Mongolia, with emissions values ranging from 577 ± 48 to 820 ± 102 Mt CO 2 in 2012 (Fig. 4 ). Meanwhile, the provinces located in the west-P. Han et al. : Evaluating China's fossil-fuel CO 2 emissions from a comprehensive dataset of nine inventories ern area with low economic activity and population density showed low carbon emissions (< 200 Mt CO 2 , Figs.
6g ). The frequency distribution of high-emissions grids revealed differences in the point source data. MEIC showed the largest number of high-emitting cells (500-500 000 t CO 2 km -2 , 5 % in com-parison with the others, which were at 2 %-3%, Fig. 6e ) by using a high-resolution emissions database (CPED) that included more power plant information  (Li et al. , 2017; Liu et al. , 2015a). Furthermore, ODIAC and EDGAR agreed well regarding the high emissions (> 100 000 t CO 2 km -2 ) because their point source emissions were both from the CARMA database (Table  1 ). Moreover, CARMA is the only global database that tracks CO 2 that gathers and presents the best available estimates of CO 2 emissions for 50 000 power plants around the world, of which approximately 15 000 have latitude and longitude information with emissions greater than 0.
The database includes approximately one-quarter of all greenhouse gas emissions. However, CARMA is no longer active (the last update was 28 November 2012), and the geolocations of power plants are not sufficiently accurate, especially in China  (Byers et al. , 2019; Liu et al. , 2013 Liu et al. , , 2015a;; Wang et al. , 2013). Therefore, users must perform corrections themselves  (Liu et al. , 2013 (Liu et al. , , 2015a;; Oda et al. , 2018; Wang et al. , 2013; Janssens-Maenhout et al. , 2019). As shown in the cumulative emissions plot (Fig. 6g ), PKU and NJU showed very similar cumulative curves, and the situation was similar for EDGAR and CHRED. Moreover, the total emissions for EDGAR and CHRED were largely determined by a small proportion of high-emitting grids that   showed a steep increase at the last stage of the cumulative curves (Fig.
Activity data differences in the datasets and their effects The activity data sources, data level, and sectors are the significant determinants of total emissions. As seen in Fig. 1 , activity data and EF determine the total emission estimates and affect the spatial distributions by using disaggregation proxies for point, line, and area sources. It has been welldiscussed that the sum of provincial data is greater than the national total  (Guan et al. , 2012; Hong et al. , 2017; Liu et al. , 2013; Z. Liu et al. , 2015; Shan et al. , 2018). CEADs (provincial) is 8 %-18 % greater than CEADs (national) after year 2008 (Fig. 2 ). Thus, the province-based estimates (e. g. , NJU and MEIC) are greater than CEADs (national). This difference could be attributed to the differences in national and provincial statistical systems and artificial factors, such as the fact that some provincial energy balance sheets were adjusted to achieve an exact match between supply and consumption  (Hong et al.
, 2017). For example, provincial statistics suffer from data inconsistency and double counting problems  (Zhang et al. , 2007; Guan et al. , 2012). One possible way to improve these statistics is to use the provincial consumption fractions to rescale the national total consumption when distributing emissions to grids. Hong et al. (2017)  found that the ratio of the maximum discrepancy to the mean value was 16 % due to the use of different versions of national and provincial data in CESY. Ranges of 32 %-47% of CO 2 emissions from the power sector (mainly coal use) were found among the inventories, while for the transport sector (mainly liquid fuels), the fractions ranged from 7 % to 9%. Apart from such differences, one peak of FFCO 2 emissions was identified by most datasets in 2013, which were largely found to be due to slowing economic growth (National Bureau of  Statistics, 1998 Statistics, -2017)) , changes in the industrial structure  (Mi et al.
, 2017; Guan et al. , 2018) , and a decline in the share of coal used for energy  (Qi et al. , 2016). Strategies for reducing emissions could be based on such uniformed trends, while making reduction policies for provinces requires the support of provincial energy-based datasets instead of national energy-based datasets. Estimates with more sectors are usually higher than those with fewer sectors. In regard to the incorporation of different emissions sectors, EDGAR includes international aviation and bunkers  (Janssens-Maenhout et al. , 2019)  and NJU incorporates waste  (Liu et al. , 2013)  (Table  S1  in the Supple-ment), and thus both were higher than the others. Moreover, for MEIC v. 1. 3 downloaded from the official website, biofuel combustion (which accounted for approximately 5. 7 % of the total) was included; however, the version used here was specially prepared to exclude biofuel to increase the comparability of the database.
In addition, CEADs industry processes only include cement production and were thus lower than those (e. g. , NJU and EDGAR) that include more processes (iron and steel, etc. )  (Janssens-Maenhout et al. , 2019; Shan et al. , 2018; Liu et al. , 2013). The PKU dataset used IEA energy statistics with more detailed energy subtypes. The emissions factors were based on more detailed energy subtypes with lower EFs, while other inventories used the averages of large groups (Table  1 ), such that the sum of more detailed subtypes might not equal the total of large groups due to the incompleteness of the statistics. These factors could explain the reasons for the lower emissions estimate  (Wang et al. , 2013). A further comparison with IEA, EIA, and BP estimates with only energy-related emissions also confirmed that estimates with more sectors would be greater than those with fewer (Fig.
(2015) , the carbon content of lignite ranged from 11 % to 51%, with a mean ± SD of 28 % ± 13 (n = 61). Furthermore, another study showed that the uncertainty from EFs (-16 % to 24 %) was significantly greater than that from activity data (-1 % to 9 %)  (Shan et al. , 2018). We recommended substituting the IPCC default coal EF with the CEADs EF. Regarding plant-level emissions from coal consumption, the collection of EFs measured at fields representing the quality and type of various coals is much needed to calibrate the large point source emissions, and we call for the inclusion of physical measurements for the calibration and validation of existing datasets  (Bai et al. , 2007; Dai et al. , 2012; Kittner et al. , 2018; Yao et al. , 2019). Different fuel types contribute differently to emissions factors; i.
Point sources in datasets and their effects on spatial distribution Point source emissions account for a large proportion of total emissions  (Hutchins et al. , 2017). Power plants consumed approximately half of the total coal production in the past decade (F. Liu et al. , 2015). Thus, the accuracy of point sources was extremely important for improving emission estimates. ODIAC, EDGAR, and PKU all distributed power plant emissions from the CARMA dataset. However, the geolocation errors in China are relatively large, and only 45 % of power plants are located in the same 0. 1 • × 0. 1 • grid in CARMA v2. 0 according to the real power plant locations that were identified by visual inspection in Google Maps  (Wang et al. , 2013). This discrepancy is because CARMA generally treats city-center latitudes and longitudes as the approximate coordinates of power plants  (Wheeler and Ummel, 2008; Ummel, 2012).
F. Liu et al. (2015)  found that CARMA neglected approximately 1300 small power plants in China. Thus, CARMA allocated similar emissions to a more limited number of plants than CPED (Table  S2  in the Supplement, 720, 1706, and 2320 point sources for ODIAC, EDGAR, and MEIC, respectively), and ODIAC had fewer point sources due to the elimination of incorrect geolocations. The high-emitting grids in CHRED were attributed to the 1. 58 million industrial enterprises from the First China Pollution Source Census (FCPSC) that were used as point sources  (Wang et al. , 2014). Following the CARMA example, we call on the open source of large point sources for datasets and reinforce the importance of Chinese scientists adjusting the locations of point sources from CARMA.
Effects of spatial disaggregation methods on line and area sources Downscaling methods are widely used because of their uniformity and simplicity due to the lack of detailed spatial data. The disaggregation methods used (e. g. , nighttime light, population) by inventories significantly affect the resulting spatial pattern. For example, ODIAC mainly uses nighttime light from satellite images to distribute emissions. Thus, the hotspots concentrate more strongly in high nighttime light regions. However, the use of remote sensing data tends to underestimate industrial and transportation emissions  (Ghosh et al. , 2010). For instance, coal-fired power plants do not emit strong lights and may be far from cities because transmission lines are used. Electricity generation and use usually occur in different locations, and stronger nighttime light does not always indicate higher CO 2 emissions  (Cai et al.
, 2010) , a problem that must be considered in future studies. Moreover, big cities have virtually eliminated the use of coal  (Guan et al. , 2018; Zheng et al. , 2018a) , while in rural areas, the use of coal has often increased  (Meng et al. , 2019). For example, a national survey showed that China's rural residential coal consumption fractions for heating increased from 19. 2 % to 27. 2 %  (Tao et al. , 2018). These transitions have impacts on the spatial distribution of both CO 2 and air pollutants. In addition, the high-resolution CO 2 emissions can serve as a potential proxy for fossil-fuel emissions  (Wang et al. , 2013) ; thus, further improvements to spatial disaggregation should consider these transitions and the survey data. Data availability. The datasets of ODIAC, EDGAR, PKU, and CEADs are freely available from http://db.
cger. nies. go. jp/dataset/ ODIAC/  (Oda, 2018) , http://edgar. jrc. ec. europa. eu/overview. php? v=432_GHG&SECURE=123 (Janssens-Maenhout, 2018), http: //inventory. pku. edu. cn/download/download. html (Wang and Tao, 2018) , and http://www. ceads. net/ (Shan and Guan, 2018) , respectively. CHRED, MEIC, and NJU are available from the data developers upon request. Supplement. Data and methodology descriptions of the nine datasets and supplementary figures on emission estimates. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-20-11371-2020-supplement. Author contributions. PFH and NZ conceived and designed the study. PFH and XHL collected and analyzed the datasets. PFH, XHL, NZ, and TO wrote the paper, with contributions from all the coauthors. Competing interests.
3. 2 Year published/ updated Data sources http://db. cger. http://edgar. jrc. ec. http: Data Data Data http://www. https://www. https://unfccc. nies. go. jp/ europa. eu/overview. //inventory. developer developer developer ceads. net/ globalcarbonproject. int/sites/default/ dataset/ODIAC/ php?v=432_GHG& pku. edu. cn/ (last access: org/carbonbudget/ files/resource/ (last access: SECURE=123 (last download/ 6 Novem- 19/data. htm (last China2BUR_ 6 Novem- access: 6 Novem- download. ber 2018)(reg- access: 6 Novem- English. pdf ber 2018) ber 2018) html (last istration ber 2019) (last access: access: required) 6 Novem- 6 Novem- ber 2019) ber 2018) References Oda (2018) Janssens-Maenhout Wang et Cai et Zheng Liu (2013) Shan et Friedlingstein et MEE (2018) (2019) al. (2013) al. (2018); (2018); Liu al.
Methods: We assembled a pantropical dataset of 2,548 forest plots (covering 1,191 ha) and quantified tree palm (i. e. , ≥10 cm diameter at breast height) abundance relative to co-occurring non-palm trees. We compared the relative abundance of tree palms across biogeographical realms and tested for associations with palaeoclimate stability, current climate, edaphic conditions and metrics of forest structure. Results: On average, the relative abundance of tree palms was more than five times larger between Neotropical locations and other biogeographical realms. Tree palms were absent in most locations outside the Neotropics but present in >80% of Neotropical locations. The relative abundance of tree palms was more strongly associated with local conditions (e. g. , higher mean annual precipitation, lower soil fertility, shallower water table and lower plot mean wood density) than metrics of long-term climate stability.