Saccharomyces yeasts is known to be associated with such segregation errors, but estimates of the importance of nondisjunction to postzygotic reproductive isolation have been hampered by difficulties in accurately measuring nondisjunction frequencies. Here, we use spore-autonomous fluorescent protein expression to quantify nondisjunction in both interspecific and intraspecific yeast hybrids. We show that segregation is near random in interspecific hybrids. The observed rates of nondisjunction can explain most of the sterility observed in interspecific hybrids through the failure of gametes to inherit at least one copy of each chromosome. Partially impairing the anti-recombination system by preventing expression of the RecQ helicase SGS1 during meiosis cuts nondisjunction frequencies in half. We further show that chromosome loss through nondisjunction can explain nearly all of the sterility observed in hybrids formed between two populations of a single species. The rate of meiotic nondisjunction of each homologous pair was negatively correlated with chromosome size in these intraspecific hybrids. Our results demonstrate that sequence divergence is not only associated with the sterility of hybrids formed between distantly related species but may also be a direct cause of reproductive isolation in incipient species.Genome-wide sequence divergence between populations can cause hybrid sterility through the action of the anti-recombination system, which rejects crossover repair of double strand breaks between nonidentical sequences. Because crossovers are necessary to ensure proper segregation of homologous chromosomes during meiosis, the reduced recombination rate in hybrids can result in high levels of nondisjunction and therefore low gamete viability. Hybrid sterility in interspecific crosses of  Saccharomyces yeasts, quantification of the frequency of these errors has been elusive until now. We use the recently developed technique of spore-autonomous fluorescent protein expression to quantify chromosome segregation errors in gametes produced by both interspecific and intraspecific yeast hybrids. By doing so, we show that segregation is near random in interspecific hybrids, accounting for nearly all of the observed gamete inviability. We also demonstrate that sequence divergence is associated with sterility of hybrids both between distantly related species and between different strains of the same species, providing a general mechanism for reproductive isolation in yeast.Different species are kept genetically separated from each other by reproductive isolation, which can result in the production of sterile hybrids. Despite the central role of reproductive isolation in evolutionary biology, the mechanisms underlying hybrid sterility remain controversial. We focus on one potential cause: genome-wide sequence divergence. Sequence divergence can lead to hybrid sterility by interfering with the proper segregation of chromosomes during gamete production, leading to most gametes lacking at least one essential chromosome. While hybrid sterility is known to be associated with mis-segregation in  Saccharomyces cerevisiae have established genome-wide sequence divergence as a cause of meiotic errors underlying hybrid sterility  between the estimated genetic divergence of the parental pairs and the resulting offspring viability […] indicating that general DNA sequence differences were not sufficient to explain the observed reproductive isolation’. In our reanalysis, we found a negative, albeit not statistically significant, correlation between hybrid spore viability and parental genetic divergence . However, two groups (indicated by red and yellow points) represent hybrids formed between parents with different chromosomal arrangements. The red points are hybrids formed between S288C and strains carrying a Chr VIII to XVI reciprocal translocation with an ECM34-SSUI breakpoint [S1-S3]. The region on Chr VIII involved in this translocation is near the telomere and contains no essential genes, and consequently this rearrangement should cause only 25% of hybrid spores to be inviable. The yellow points represent hybrids between S288C and strains containing a translocation between two large chromosomal regions  that each contains at least one essential gene [S1]. These two rearrangements are expected to cause a 50% reduction in spore viability in these hybrids. (B) Following the practice of Liti and colleagues [S4], we have corrected the observed spore viabilities for the effects of known chromosomal rearrangements in the groups represented by red and yellow points. The corrected spore viabilities are highly correlated with parental genetic divergence . Thus, after correcting for the effects of chromosomal rearrangements, parental genetic divergence was a clear predictor of hybrid spore viability even among closely related parents (slope = −1.25% viability per 0.1% genetic divergence). Hybrids represented by blue points contain no known rearrangements and were therefore not adjusted. Omitting these blue points from the analysis has no effect on our conclusion , nor does restricting the analysis to only the high-fertility collinear pairs represented by grey and blue points . (C) Spore viabilities of intraspecific S. cerevisiae hybrids were independently measured by [S2] as part of the ‘100 genomes’ project; each strain was crossed with S288C to generate intraspecific hybrids. Since not all major chromosomal rearrangements have been identified for these strains, we have restricted our analysis to hybrids formed between parents identified as having collinear genomes (hybrid spore viability >75%) in [S2]. Full genomes for strains that Strope and colleagues [S2] provided hybrid fitness measures for but no genomic sequences were obtained from Genbank . The genome sequence of strain YJM1628 was used for the isogenic strain YJM1615. Genetic distances were determined by aligning whole genomes using REALPHY [S5] and calculating % identity using Geneious 10.2.3 based on 8,813,278 aligned sites. Once again, hybrid spore viability was significantly correlated with parental genetic divergence . The negative relationship was very similar to that in (B) with a slope = −1.33% viability per 0.1% genetic divergence (grey line). The slope was only slightly changed by the omission of the homozygous S288C parent (−1.35% viability per 0.1% genetic divergence). Underlying data can be found in 16. The dotted red line represents the relationship between spore viability and genetic divergence reported by Liti and colleagues [S4]. Our method underestimates spore viabilities for intraspecific hybrids because Chr I has a higher than average rate of nondisjunction in these strains Click here for additional data file.S3 FigYKL050c and YKR005c. From genetic maps in S. cerevisiae strain S288C , we estimated the map distance of these two loci at approximately 35 cM, the approximate limit at which linkage can be calculated by tetrad analysis without empirically derived correction [S6]. We marked YKL050c with PYKL050c-GFP_URA3 and YKR005c with PDIT1-RFP_LEU2 in both S. cerevisiae strain Y55 and S. paradoxus strain N17 and scored tetrads produced by parental diploids and hybrid diploids as the PD (2 red spores and 2 green spores), the NPD (2 red/green [represented here as yellow] spores and 2 nonfluorescent spores), or as the T . Tetrads not matching any of these three categories were omitted from the analysis . Since nondisjunction is extremely rare in parental types, we calculated genetic distances in parents according to the standard equation [S6]: 100(T + 6NPD)/2(PD + NPD + T). In hybrids, the low number of Ts observed indicates that single crossover events are rare; the frequency of double crossover events must therefore be negligible, and all non-parental ditypes can be ascribed to nondisjunction events [S7]. We therefore estimated the genetic distance between these loci in hybrids as 100T/2(PD + T). Chr, chromosome; NPD, non-parental ditype; PD, parental ditype; T, tetratype.We investigated recombination between two loci located approximately 100 kb apart on Chr XI: (PDF)Click here for additional data file.S4 FigLEU2 and URA3 were knocked out using antibiotic resistance cassettes (in most cases either HYGMX or KANMX) in haploid S. cerevisiae strains Y55 and S288C and/or S. paradoxus strains  to allow selection for the integration of spore-autonomous fluorescent protein expression cassettes . Fluorescent constructs, excluding the promoters, were amplified from plasmids pSK691 (RFP_LEU2) and pSK726 (GFP_URA3) and placed under the control of the endogenous YKL050c or DIT1 promoter by replacing the appropriate ORF, generating strains with ykl050c::RFP_LEU2, ykl050c::GFP_URA3, and dit1::RFP_LEU2, dit1::GFP_URA3 genomic regions. The integrated fluorescent constructs plus the appropriate endogenous strain-specific promoters  were then amplified from genomic DNA and integrated at the desired site on each chromosome. GFP-marked haploids were mated to strains of the opposite mating type with RFP at the allelic position to generate parental or hybrid diploids, which were then sporulated to examine meiotic segregation. See (PDF)Click here for additional data file.S1 DatapCLB2_SGS1) parents and hybrids marked at each chromosome with the DIT1-promoted (‘DIT1 promoter’ sheet) or YKL050c-promoted (‘YKL050c promoter’ sheet) spore-autonomous fluorescent reporter. Tetrads with ‘other’ patterns of segregation were included in the total counts but not in the nondisjunction counts. The ‘100 genomes hybrids’ sheet includes hybrid spore viability data from [S2] and genetic distances from S288C calculated using REALPHY [S5].Data include numbers of tetrads scored as having correct segregation (different cells) or nondisjunction (same cells) for wild-type and anti-recombination–impaired ((XLSX)Click here for additional data file.S1 TextIncludes strain tables, primer tables, and supporting references.(PDF)Click here for additional data file.


Lactobacillus plantarum and Lactobacillus fermentum) in two fermented environments, which showed that most of the mutations occurred in fermented vegetables and that fermenting environment might be the major factor for these mutations. This research provides us with new insights into beneficial microbial resources in regard to microbial diversity and genetic polymorphisms and lays a foundation for the subsequent development and utilization of beneficial microorganisms.Fermented fish, fermented shrimp and fermented crab are traditionally prepared seafoods that are commonly consumed in the Hainan area in China. We studied the microbial diversity and metabolic pathways in traditional fermented seafoods using high-throughput sequencing technology, and based on our previous research, we also compared the differences between fermented seafood and fermented vegetables. The alpha diversity of fermented seafood was higher than that of fermented vegetables and attained the highest level in fermented shrimp. The dominant genera in fermented seafood were different from those of fermented vegetables. Furthermore, we analyzed the 16S rDNA gene polymorphisms (SNPs) of the same dominant species ( Fermentation is the major storage method of food in many countries  and is cBifidobacterium and Lactobacillus are the most commonly used probiotics . Fermented seafood was washed and crushed, and then 6–7% of salts were added to obtain the natural fermentation. Lactic acid was the main produced acid in the process of microbial fermentation. These sauces are semifluid, have many small solid particles and are often used as seasoning in cooking. The most commonly eaten home dish in Hainan Province is fried sweet potato leaves in shrimp sauce.Probiotics were defined as “Live microorganisms which when administered in adequate amounts confer a health benefit on the host” . Bifidobobiotics . Many feobiotics , with Lay varied  and consy varied  and chroy varied , hyperliy varied  and cardy varied , as welly varied . We colly varied . Shrimp In recent years, the expansion of genomic sequencing has promoted tremendous advances in metagenomics , which hIn this study, 23 fermented seafood samples were collected. Nine fermented fish samples were collected from the cities of Lingshui, Baoting and Qiongzhong; 5 fermented crab samples were collected from Wanning City; and 9 fermented shrimp samples were collected from the cities of Baoting, Ledong, Lingshui and Wanning in Hainan Province in China. The specific sampling areas were in the Ten grams of fermented seafood sample was mixed uniformly with 90 mL of a sterile NaCl solution . On the one hand, the solution was used to dilute the mixture, which was then coated on MRS solid agar medium; On the other hand, the diluent of mixture was used to extract DNA directly for 16S rDNA sequencing.A single colony was selected, and DNA was extracted after the second-generation of the culture. After being oscillated evenly, the supernatant was used for DNA extraction with a QIAGEN DNA Mini-Kit  and a bead-beating method . The quaThe DNA of fermented seafood was extracted with a QIAGEN DNA Mini-Kit. The V3-V4 region of the 16S ribosomal RNA (rRNA) genes was amplified , and a sWe performed microbial community analyses in the QIIME platform (v1.7) using high-quality sequences . PyNAST Lactobacillus plantarum and 158 Lactobacillus fermentum strains were chosen for a comparison of SNPs , fermented bamboo shoots (FBS) and fermented watermelons (FW)] was analyzed and various bacterial strains were isolated . A varie of SNPs .P < 0.001), and no significant differences were observed among FBS, FCC, FW, FC and FF. The Shannon (p < 0.001). The alpha diversity of FS was highest in fermented seafood, but in the fermented seafood samples, no significant differences existed among between FS, FC and FF. This result meant that a different alpha diversity existed among the different raw materials of the fermented species.We measured the pH value in the samples . Interes Shannon , Simpson Shannon , Chao1 ( Shannon  and ACE  Shannon  indices P < 0.001) in PC1 was observed based on both the weighted  of the weighted UniFrac distance  and the weighted  and unweweighted  WilcoxonLactobacillus, Pediococcus, Weissella, Bacillus, Lactococcus, Pseudomonas, Acinetobacter, Rummeliibacillus, Enterobacter, Enterococcus and Sphingomonas. The relative abundances of Lactobacillus, Pediococcus, Weissella, Bacillus, Lactococcus, Pseudomonas, Acinetobacter and Rummeliibacillus were more than 1%. In fermented seafood, the average abundance of Lactobacillus was only 19%, and in fermented vegetables, the relative average content of Lactobacillus was 67%  among all samples  in two fermented environments, which showed that most of the mutations occurred in fermented vegetables. The raw materials had a greater impact on the microbiota in the fermented products. This research provides new insight into the beneficial microbial resources in regard to microbial diversity and genetic polymorphisms, providing basic research data for the subsequent development and utilization of beneficial microorganisms.In this study, the microbial diversity of fermented seafood in the Hainan area was systematically studied. This research showed that fermented seafood had high alpha diversity and that the microbial structure was different. Based on our previous research, we also compared the differences between fermented seafood and fermented vegetables; those differences were mainly reflected in the alpha and beta diversities and the dominant microbial genera. Fermented shrimp had the highest alpha diversity, and the dominant bacteria were also different. Furthermore, we analyzed the 16S gene polymorphisms of the same dominant species (The sequence data reported in this paper have been submitted to the NCBI database (Accession No. PRJNA507916).JZ and WL developed the experimental design. SJ and CM conducted the research. DH provided essential reagents. CM and QP analyzed the data. SJ and QP wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The dataset builds upon data from CDP , the Bonn Center for Local Climate Action and Reporting , and data collected by Peking University (83 cities in China). The CDP data being self-reported by cities, we applied quality control procedures, documented the type of emissions and reporting method used, and made a correction to separate CO2 emissions from those of other greenhouse gases. Further, a set of ancillary data that have a direct or potentially indirect impact on CO2 emissions were collected from other datasets (e.g. socio-economic and traffic indices) or calculated , then combined with the emission data. We applied several quality controls and validation comparisons with independent datasets. The dataset presented here is not intended to be comprehensive or a representative sample of cities in general, as the choice of cities is based on self-reporting not a designed sampling procedure.We present a global dataset of anthropogenic carbon dioxide (CO Cities concentrate population, energy use2 and economic output, hence are important focal points for investigating emission drivers and mitigation options, prompting across-city comparative analyses of greenhouse gases (GHG) emissions4 and energy use2.Cities are hotspots of the global carbon cycle, with considerable fossil fuel and cement CO2 emissions estimates are needed from local inventories, but also underlying ancillary socio-economic data. Scope-1 emissions cover GHGs emitted in the city territory, including emissions from grid-supplied energy produced within cities5. Scope-2 emissions include grid-supplied energy used by cities and produced by power plants outside the city boundary. The distinction about power plants emissions included in Scope-1 is not always precise in city-reported data. Scope-1 GHG emissions of cities5 include transport, industrial, waste and local power plants emissions, and allow for a more direct comparison between cities, as uncertain additional assumptions are needed for Scope-2 emissions about the GHG mix of electricity consumed by each city.To understand city emission drivers in different global regions, not only city-scale CO5. Among the different types of GHG emissions in cities, fossil fuel CO2 emissions are more robustly comparable between cities as they constitute the largest share of total GHG emissions and are estimated by more cities than non-CO2 gas emissions. Additionally, Scope-1 CO2 emissions from inventories could ultimately be compared to observation-based estimates using atmospheric CO2 measurements and tracer transport models7, provided emissions from power plants within a city territory are included in reported Scope-1 emissions.City-level GHG emissions are mostly self-reported using different inventory methods based on energy and fuel statistics and follow different protocols2 emission patterns between cities. Key drivers are population and Gross Domestic Product (GDP) per capita. For instance, analysis of the relationships between per-capita consumption and per-capita GDP was applied to energy consumption in cities by Creutzig et al.1, who derived a typology of urban energy use and mitigation potentials and highlighted city compactness and high gasoline prices as explanatory variables for lower energy use in affluent cities.Ancillary emission drivers data related to processes impacting the use of carbon fuels allow us to better understand differences in CO2 emissions with data related to emission drivers is still missing, although there are several regional datasets8–13 as well as recent efforts to estimate city carbon footprints using regional14 and national15 consumption-based models. Sorting out the best available information on data sources and a better understanding of the methods used to produce them help address inconsistencies across the set of cities studied; however, full traceability to the original data used in each city emission inventory is often not possible, and city-level CO2 emissions and driver-related data are not always consistent across space and time. Further, the scope of gases and sectors covered by the datasets are often uneven and activity data could have been scaled down from national data when local activity data are unavailable.A comprehensive global city dataset with Scope-1 CO2 emissions based on CDP16, the carbonn Climate Registry of the Bonn Center for Local Climate Action and Reporting (http://carbonn.org), and a set of Chinese cities compiled by Beijing University (private communication). CO2 emission data are complemented with key ancillary data associated with emissions. The dataset includes 343 cities in nine geographic regions. The majority (88%) of these cities reported emissions between 2010–2015. When the original data reported total emissions from multiple GHGs, we developed a simple correction procedure to remove the contribution of methane (CH4). Scope-2 emission data are provided as additional information when available. Wherever possible, independent consistency checks of the data are performed. The final dataset of Scope-1 CO2 emissions and ancillary data is organized as a tab-separated values (TSV) file of dimensions 343 rows (cities) × 179 columns.Here, we present a global dataset of Scope-1 city CO2 emissions from this study with independent datasets for US and Japanese cities.In the following, we describe the methodology used to produce the final dataset (Methods) and details about each individual dataset (Data Records). The Technical Validation section includes analyses to support the technical quality of the dataset. That is, the Quality Assurance/Quality Control (QA/QC) applied to data records of the same variable when different datasets include this variable, and the comparison of Scope-1 CO2 emissions and ancillary data. http://jupyter.org/, open-source web application for code, visualizations, etc.) using pandas DataFrames (http://pandas.pydata.org/), a data structure that allows data sorting, counting or conditional replacement to be executed on tabular data in lines of code. 16 (DCDP2016) provides annual mean GHG emissions in CO2 equivalents from 187 cities in eight geographic regions. The CDP data are collected through an Online Response System that allows cities to report climate hazards, climate actions, targets and emission inventories. The emissions disclosed to CDP are provided directly by city governments. Cities first identify the inventory boundary and their emissions sources. Most cities use the scopes framework to report Scope-1 and Scope-2 emissions.The 2016 edition of the CDP emissions dataset5. Scope-2 are the emissions from purchased energy generated upstream from the city, mainly electricity5. Total Emissions are the sum of the two scopes. Emission data in DCDP2016 were examined manually to extract only Scope-1 emissions, after correcting for inconsistencies by either consulting with the city data providers or by comparing with a more recent version17 of the CDP data called DCDP2017. Based on quality control procedures comparing Scope-1, Scope-2 and total emissions , quality flags were assigned to the GHG Scope-1 emissions of each city. The quality-controlled data are denoted DCDP. CDP, summarized in Scope-1 are the direct territorial emissions from residential and industrial heating, transport, industrial sectors and power plants within the territory of citiesTo calculate their emissions, cities collect data for a variety of sources, including city departments, their country’s national inventory, local utilities, statistics agencies, and universities. Methods such as scaling can be used for national data to estimate the cities inventory. Once cities have the appropriate data, they multiply the activity data by an emission factor associated with the activity being measured. Cities then compile all this information and provide CDP their inventory and a summary of their inventory data.2 sources from other GHG contributions in DCDP. In total, 44 cities (25%) reported only emissions from fossil fuel and cement CO2, 6 cities did not specify the GHGs covered, 4 reported CO2 and CH4, and 133 included all GHGs, that is, CO2, CH4, N2O, HFCs, PFCs, SF6, and/or NF3  = f the Scope-1 CO2-equivalent emissions (f) as a function of the set of GHGs considered. Then:Since emissions of different greenhouse gases (GHGs) are included under Scope-1 emissions, we separated the fraction of emissions from fossil fuel and cement CO NF3 see . We sepaf(gi) is the emission of each GHG, and αi its warming potential. The emission of CO2 denoted by Where 2 GHGs compared to those of CH4, denoted by Neglecting emissions of other non-CO2 emissions were 4 emissions from waste in each city, approximated by national per capita methane waste emissions multiplied by city population, using country emissions data from the EDGAR inventory (Data Citation 1), country population from The World Factbook , and a GWP of 28 for CH4, time horizon 100 years18. We highlight that this is a simplification; however, deriving city-specific estimates from country-specific emission factors seems reasonable and agrees with the approach taken by UNFCCC for the official national reporting. Regarding whether per capita waste generation (solid waste and waste water) might differ between rural and urban areas, we assume that for OECD countries, there is only a minor difference since consumption and income levels are closely correlated. For emerging countries, the difference in per capita waste of rural and urban population could be larger or CH4 emissions from waste might not even be covered in all rural areas as managed, and this accounted for in the used CH4 emission dataset.An upper bound of Scope-1 CO2 emissions was obtained using the same approach, considering this time CH4 emissions from waste and from natural gas.A lower bound of β) to account for import and export. If the volume of domestic natural gas production, import and export are represented by vproduction, vimport and vexport, respectively, then:Note that figures for national methane emissions from natural gas production should be scaled by the remaining fraction  Scope-1 GHG emissions reported by 48 large cities from the carbonn Climate Registry  with 6 cities in Africa, 5 in East Asia, 10 in Latin America & Caribbean, 3 in North Africa, Middle East, West Asia, 19 in South Asia, and 18 in Southeast Asia, and 25 other cities for which we had ancillary data from Creutzig et al.1; 2) Scope-1 CO2 emissions from the new data compilation of 83 large cities in China from Peking University (DPKU). The emissions were grouped with DCDP into a single dataset denoted Demissions covering 343 cities, for which ancillary data was then gathered in the following steps. Note that DCDP, Dcarbonn and DPKU contained some ancillary data that we preserved (see Data Records section).Most of the cities that disclosed their emissions to CDP are from USA, Canada, Australia, New Zealand (43%) and Europe (31%). Therefore, the D2 emissions. Spatial units of these ancillary data were checked for consistency with emissions data. The consistency of spatial and temporal units between ancillary data and emissions, and within ancillary data themselves, were checked and assigned a Quality Flag . Ancillary data related to emissions were collected to match Scope-1 COet al.1 , UITP (https://trid.trb.org/view/708144) , and WB20 data . These datasets contain energy-use, other socio-economic and climate-related attributes. We integrated the GEA, UITP and WB datasets that comprise 273 cities into DGEA+, but this dataset only matched emissions from 57 cities from DCDP, 25 from Dcarbonn and 35 from DPKU. In DGEA+, duplicate identical values of the same attribute in DGEA, DUITP and DWB were merged into one common column tagged with label ‘GEA+’. If the values were different, they were investigated further to resolve discrepancies (e.g. data entry errors) or left as is if the values were obtained from different sources; any such discrepancies are noted in the Data Records.First, we used three datasets previously reported in Creutzig al.1 see : GEA19 , an urbanization index (http://www.un.org/en/development/desa/population/publications/pdf/urbanization/WUP2011_Report.pdf), and climate indices of heating and cooling degree days (HDD and CDD). HDD and CDD are commonly used to estimate the climate-dependent demand for energy needed to heat or cool a building, respectively, and are defined as the yearly sum of the difference between a base temperature Tb and daily temperature, Tdaily, whenever the daily temperature is lower (HDD) or greater (CDD) than Tb:Altogether, the DGEA, DUITP and DWB, HDD and CDD were given for a 5-year average over 2007–2011 for 13 base temperatures in increments of 0.5 °C (Tb = 12.5 °C–18.5 °C for HDD and Tb = 17 °C–23 °C for CDD). Those values were computed by the online software tool Degree Days.net (www.degreedays.net) created by BizEE Software (http://www.bizeesoftware.com/) using daily temperature data obtained from Weather Underground local weather stations in different cities (www.wunderground.com). The software returns monthly averages across the year range specified.In DGEA+ with Demissions. Missing ancillary data in this merged dataset were then completed from external sources and merged again into Demissions in the order listed below .We merged DOTHERS to fill gaps in DGEA+ and to add new attributes related to emissions. Note that DOTHERS data were not used to replace any data – the names of these independent, updated or new attributes were tagged with an ‘others’ label and added alongside existing attributes (themselves tagged with their dataset of origin). City population and area from multiple sources were obtained for all cities in Demissions. Diesel and gasoline prices provided in DGEA+ for year 200921 were updated to 2014 values22 for all the cities in Demissions. Average household size was added for 119 out of 139 cities in Demissions that did not have this attribute from DGEA+, and 48 DGEA+ values were updated. The mean travel time to work was obtained from external sources for 152 of the 187 CDP cities in Demissions. City area, population, average household size, and mean travel time were checked for spatial consistency before being included in DOTHERS. Gross Domestic Product (GDP) estimates (at Purchasing Power Parity (PPP) and nominal) along with population values for corresponding years, where available, were obtained manually from various external data sources (see Data Records). Altogether, GDP-PPP  estimates were found for 153 (211) cities. The original GDP estimates given in DPKU for all 83 Chinese cities were not checked against independent sources.We obtained external ancillary data denoted as D23 (DINRIX) covering in total 1064 cities of which 118 matched Demissions, and from TomTom , covering 390 cities of which 123 matched Demissionss.Congestion-related data was obtained from INRIX24 based on 2015 data (DIESE) for 181 cities, of which 85 cities matched Demissions.Socio-economic indicators related to transportation, environment, economy, technology, governance and human capital was obtained from the IESE Cities in Motion datasetUEX, from high-resolution satellite land cover imagery in 282 urban area clusters that correspond to 343 cities in Demissions. The DUEX attributes relate to, for each year, high and low built-up areas , the BUA fraction (%) out of the total BUA for high and low BUAs, and low/high BUA population density per km2. DUEX was derived from Landsat imagery collections, pre-processed by the Global Human Settlement (http://data.europa.eu/89h/jrc-ghsl-ghs_smod_pop_globe_r2016a) and built-up (http://data.europa.eu/89h/jrc-ghsl-ghs_built_ldsmt_globe_r2015b) grids, and population data from the Center for International Earth Science Information Network (CIESIN) Gridded Population of the World (GPWv4) (http://data.europa.eu/89h/jrc-ghsl-ghs_pop_gpw4_globe_r2015a). Please see the Data Records section for more details.Urban area expansion in units of percentage of total built-up area (BUA) for low and high BUAs reflects the increase in the built environment due to activities related to urban life, such as housing construction. Here, we calculated 18 urban area expansion attributes pertaining to three different years , denoted DGEA+ had CDD and HDD indices for some cities, these indices were recomputed (Dclim) from a global harmonized gridded climate dataset25 sampled for the grid-cell containing each city. Dclim contains HDD for a base temperature (Tb) of 15.5 °C and CDD for a base temperature (Tb) of 23 °C.Although Demissions merged with all ancillary datasets  is denoted Dfinal and has dimensions of 343 rows (cites) × 179 columns . The data is publicly available on PANGAEA (Data Citation 2).The dataset containing Dfinal, To summarize the coverage of the different data contained in Dfinal covers emissions and ancillary data in a wide range of countries and 83 cities in China, allowing data to be analyzed for differences between cities worldwide . In the interest of “data discoverability”, we developed an interactive, web-based data visualization, available at the temporary URL https://katirg.github.io/GlobalCarbonCities/. Permanent visualization of Dfinal will be available in the Global Carbon Atlas at www.globalcarbonatlas.org in March, 2019.The new dataset DGEA+, DINRIX, DTomTom, DIESE, Dclim, DUEX, and DOTHERS). The notebooks import the following modules: pandas (version 0.22.0), numpy (version 1.14.0), matplotlib (version 2.1.2), and csv (version 1.0).The Jupyter notebooks (IPython version 5.1.0) used in this section are publicly archived with the final dataset on PANGAEA (Data Citation 2), along with the necessary input files  , good quality Scope-1 GHG emissions  were found for 144 of these cities. Carbonn: Scope-1 GHG values were obtained for 73 cities. PKU: Scope-1 GHG values were obtained for 83 cities.Common column to store Scope-1 GHG emissions values in Dfinal, i.e., ‘CDP’, ‘carbonn’, or ‘PKU’.Common column to store the name of the dataset corresponding to the Scope-1 emissions in Dfinal. CDP: Units ‘tCO2’ are used for cities that reported only CO2 (refer to the ‘Gases included (CDP)’ attribute), and units ‘tCO2-eq’ are used for cities that specified gases other than CO2 or did not specify whether non-CO2 gases were included. Carbonn: units ‘tCO2-eq’ are used since a gas species breakdown was not available. PKU: units ‘tCO2’ are used for all cities since emissions related to CO2 only.Common column to store the units of the Scope-1 GHG emissions values in Dfinal. CDP: emission years ranged from 1990–2016, with 88% of the data reported for the period 2010–2015. Carbonn: emission years ranged from 1994–2017, with 78% of the data reported for the period 2010–2015. PKU: all emissions were estimated for the year 2010.Common column to store the year of the Scope-1 GHG emissions values in Dfinal.Common column to store the emissions protocol used to obtain the Scope-1 GHG emissions values in DCDP: The protocols used to estimate emissions in DCDP are described in the CDP submission guidelines . The majority of cities report their inventory to CDP using the Global Protocol for Community-Scale Greenhouse Gas Emission Inventories (GPC)5 launched in 2014 by C40, WRI and ICLEI, after a pilot in 2011 with 35 cities. The GPC aims to enable a consistent, transparent and internationally recognized approach for cities to measure and report emissions, allowing for credible comparison and aggregation across timescales and geographies. Cities that commit to the Compact of Mayors, now the Global Covenant of Mayors for Climate and Energy, must complete an emissions inventory using the GPC methodology.Within GPC, cities can choose to either report BASIC (Scope 1 + Scope 2 + some waste Scope 3 emissions) or BASIC+ emissions . The breakdown was as follows:1. Global Protocol for Community-Scale Greenhouse Gas Emissions Inventories (GPC), , 64 cities (34%). Comprises emissions from stationary energy, transportation, waste, Industrial Processes and Product Use (IPPU), Agriculture, Forestry and Other Land Use (AFOLU), Total Scope 1 , and Total BASIC emissions.2. International Emissions Analysis Protocol (ICLEI), 18 cities (10%). Comprises emissions from stationary energy, transport, fugitive emissions, industrial processes, agriculture, land use, land use change and forestry, solid waste disposal, wastewater treatment and discharge, and other sources.3. 2006 IPCC Guidelines for National Greenhouse Gas Inventories, 29 cities (16%). Comprises emissions from energy, IPPU, AFOLU, waste and other sectors.4. U.S. Community Protocol for Accounting and Reporting of Greenhouse Gas Emissions (ICLEI), 26 cities (15%). Comprises emissions from built environment, transportation and other mobile sources, solid waste; wastewater and water, agricultural livestock, and upstream impacts of community-wide activities.5. “Other”, 50 cities (27%). This category includes combinations or subsets of methodologies, or propitiatory methodologies specific to a region/city. Comprises emissions from buildings, water, waste, transport/residential/commercial/industrial/ institutional use, stationary combustion, mobile combustion, industrial processes, waste, and any other classification.Carbonn: cities reported emissions based on the GPC protocol (http://e-lib.iclei.org/wp-content/uploads/2015/12/cCR2015_5Year_Report.pdf).PKU: Scope-1 CO2 emissions were calculated from direct energy consumption statistics26 with CO2 emission factors of diesel oil, coal gas, natural gas, and liquefied petroleum gas calculated according to the guidelines of IPCC 200627.The three common geo-descriptive columns are:final. Ambiguous city names were corrected .Consistent city name assigned to each city in Dfinal.Consistent country name used in Dfinal using the regions defined in Dcarbonn.A geographic region assigned to each city in D2 emissions separated as described in Methods from total GHG emissions reported by cities in DCDP were estimated between 1990–2016, with 88% of the data being in the period 2010–2015. Some cities also reported change in their emissions between the most recent and previous reporting periods, explanations for this change, methodology details, and gases included, as well as ancillary data such as population, boundary definition, land area, mean annual temperature, mean altitude, and Gross Domestic Product (GDP). The following records were obtained from DCDP.Scope-1 COName provided by each city.Boundary of the city provided by each city. Boundary types are diverse and correspond to metropolitan area for 25 cities, administrative boundary of a local government: 147; combination of administrative divisions: 2; geopolitical Boundary - physical areas over which local government has jurisdictional control: 3; municipal boundary: 1; Other boundary types: 4 cities.The year when a city reported data to CDP, that is, 2016 for 162 cities and 2017 for 25 cities.CDP match the recent report from . Also, according to the Instituto Nacional de Estatística Portugal, the population of Moita near Lisbon is 66,029 (https://en.wikipedia.org/wiki/Moita).Latitude and longitude coordinates provided by each city. The following discrepancies were noted in these coordinates. Wellington referred to Wellington, Florida instead of Wellington, New Zealand. Moita in Portugal did not localize to the city. We set the Moita city near Lisbon, since the population  and emissions  in DReported city population.Year corresponding to reported city population data.CDP.City land area available for 184 out of 187 cities in D2, 6 cities did not specify, 4 cities included CO2 and CH4, and the remaining 133 cities included CO2, CH4 plus a combination of one or more of N2O, HFCs, PFCs, SF6, and/or NF3  of cities reduced their emissions with respect to the previous reporting year. Emissions increased for 36 (19%) cities, and stayed the same for 12 (6%) cities. Of the remaining cities, 24 (13%) cities were reporting their emissions for the first time in 2016 (thus had no previous comparison year), 7 (4%) cities reported “other” to indicate that their emission data was not directly comparable to previous estimates due to protocol differences, and 20 cities (11%) did not specify.CDP2016 dataset to explore the reasons provided for changes in emissions. Most reasons tended to be due to a methodological change, population growth, change in policy, or increase in renewable energy and electricity.The interested reader may refer to the original DCDP in units of metric ton CO2-eq. Note that some cities reported only CO2, effectively making this unit tCO2 (refer to the ‘Gases included (CDP)’ attribute). Scope-2 emissions exist for 141 out of 187 cities which were of good quality .Scope-2 GHG emissions from D2 emissions reported in 2016 in units of metric ton CO2-eq. Note that some cities reported only CO2, effectively making this unit tCO2 (refer to the ‘Gases included (CDP)’ attribute). Depending on the methodology selected, Scope-1 and Scope-2 emissions are included in total emissions.The city total COCDP. Values provided by source for DCDP cities.Average altitude of the city in meters above sea level, available for 168 out of the 187 cities in DCDP.Average annual temperature in degrees C, available for 177 out of the 187 cities in DGDP of the city in the currency reported by the city (given in next column).Currency of the GDP value.The year for which the GDP value was obtained.Source of reported GDP data.For cities whose CDP2016 data were replaced by CDP2017 values; reasons are indicated in this column. Please see the Technical Validation section.Quality flags (A–E) assigned to emissions as described in section ‘Technical Validation’.Lower and upper bound estimates of Scope-1 emissions resulting from the methane corrections described in Methods Step 2.Lower and upper bound estimates of Scope-1 emissions resulting from the methane corrections described in Methods Step 2.Lower and upper bound estimates of total emissions resulting from the methane corrections described in Methods Step 2.Lower and upper bound estimates of Total emissions resulting from the methane corrections described in Methods Step 2.Defined as (Scope 1 + Scope 2)/Total emissions. See ‘Quality Flags’ section and carbonn (see Methods) were estimated for years in the period 1994–2017, with 78% of the data in 2010–2015. Emissions correspond to all activities occurring throughout the local government’s entire geographic area (http://e-lib.iclei.org/wp-content/uploads/2015/12/cCR2015_5Year_Report.pdf). Cities also reported ancillary data such as city type, population, GDP and nominal GDP. The following records were obtained from Dcarbonn.Scope-1 GHG emissions from 73 cities selected in DCity name.City type: Municipality: 58; Independent municipality: 7; Special municipality/Federal district: 3; State/Region: 2; Independent intercommunality: 1; Intercommunality:1; Sovereign city-state: 1.City population.Year corresponding to reported city population.GDP of the city in the units reported by the city (see next column).Currency unit of GDP.Year corresponding to reported GDP.Nominal GDP in units specified in ‘GDP unit (carbonn)’ above.carbonn.Average altitude of the city, in meters above sea level, available for 2 out of the 73 cities in DPKU correspond to the year 2010. Cities also reported ancillary data such as urban population, built-up area, GDP and income per capita. The following records were obtained from DPKU.All of the emissions reported by the 83 cities in DCity name.Urban population.PKU .Built-up area was defined by interpreting Landsat images for each city in DGDP of the city in renminbi currency units.Per capita income of the city in renminbi currency units.2 emissions provided by source.Residential COGEA+ is the fusion of DGEA, DUITP and DWB containing the following attributes:As explained in Methods, DGEA.City name as given in DUITP.City name as given in DWB.City name as given in DWB cities: City: 12; Greater metropolitan area: 3; Government Administered Area (Province): 3; City and County: 1; County: 1.City type for DWB cities (1998–2006).Study year for emissions for DGEA, available for 41 of the 187 cities in DCDP.City population from DUITP, available for 42 of the 187 cities in DCDP.City population from DWB, available for 15 of the 187 cities in DCDP.City population from DWB, available for 15 of the 187 cities in DCDP.Year corresponding to city population in Dyear = 1950, 1990 and 2010 from DWB, available for 15 of the 187 cities in DCDP.Population of urban agglomerations in year1 = 1950 and 1990 and year2 = 2010 from DWB, available for 15 of the 187 cities in DCDP.Population growth rate between GEA, available for 30 of the 187 cities in DCDP.City area from DWB, available for 15 of the 187 cities in DCDP. Used Wikipedia values corresponding to definition of the city.City area from DGEA, available for 41 of the 187 cities in DCDP.Population per area from DGEA, available for 30 of the 187 cities in DCDP.Population per sqrt(area) from DUITP, available for 42 of the 187 cities in DCDP.Population per area from DWB, available for 15 of the 187 cities in DCDP.Population per area from DWB, available for 15 of the 187 cities in DCDP.Population per sqrt(area) from DCDP. One city in DGEA+ (Brasília) was missing a weather station ID.The ID of the weather station used to calculate HDD and CDD, available for 56 of the 187 cities in DGEA+. HDD values across DGEA, DUITP and DWB were the same for all cities except Sao Paulo in DUITP, which disagreed with the values in the other two datasets. The DUITP values were considered anomalous and were removed. No HDD data were available for one city (Brasília) in DGEA+.HDD values for a base temperature of 15.5 °C obtained from the weather station specified in the ‘weather_station_id’ attribute of DGEA+. CDD values across DGEA, DUITP and DWB were the same for all cities except Sao Paulo in DUITP, which disagreed with the values in the other two datasets. The DUITP values were considered anomalous and were removed. No CDD data were available for one city (Brasília) in DGEA+.CDD values for a base temperature of 23 °C obtained from the weather station specified in the ‘weather_station_id’ attribute of D1 that these temperature thresholds were the most predictive of energy use, given the data available. .These base temperature values for HDD and CDD were selected since it was shown in a 2015 studyTotal final energy consumption in GJ/capita/yr. Provided by source.2-eq/capita/yr, excluding aviation and marine sources. Provided by source.Per capita emissions for energy use in tCO21. In the final consolidated dataset, these values were not used; updated 2014 values22 were used instead (see Methods Step 4).November 2008 diesel price 21. In the final consolidated dataset, these values were not used; updated 2014 values22 were used instead (see Methods Step 4).November 2008 gasoline price UITP were anomalous compared with the corresponding DGEA and DWB values; cross-checking with the reference cited in the datasets revealed that these DUITP values were incorrect and they were therefore removed.Fuel prices for two cities (Rio de Janeiro and Sao Paulo) in DCDP. The following discrepancies/uncertainties were noted for household size in DGEA+:Average number of persons per household, obtained from multiple sources, available for 48 of the 187 cities in DSydney: The 2011 household size reported in DGEA+ corresponds to the population of the Greater Sydney Area, whereas the corresponding city population in Demissions corresponds to the ‘City of Sydney’.Stockholm: It could not be confirmed that household size in DGEA+ represents the corresponding city population in Demissions. Household size in DOTHERS was estimated by a manual calculation based on values from Statistics Sweden .Lisbon: It appears that the 2011 population for household size in DGEA+ does not match the corresponding city population in Demissions.Melbourne: The household size reference link in DGEA+ is actually for Sydney.Seoul. The household size reference link in DGEA+ is invalid.GEA+ that matched cities in DCDP.It was also found that the household size source reference no longer exists for 14 of the 57 cities in DYear corresponding to household size values.Reference corresponding to household size values obtained from multiple sources.Comments regarding household size computations.http://www.un.org/en/development/desa/population/publications/pdf/urbanization/WUP2011_Report.pdf), available for 57 of the 187 cities in DCDP.Percentage of Population Residing in Urban Areas in 2005 (https://www.scribd.com/document/17016734/MasterCard-Worldwide-Centers-of-Commerce-Index-2008), obtained by considering 43 indicators and 74 sub-indicators across seven dimensions , available for 33 of the 187 cities in DCDP.Center of commerce index . Source: derived visually from Google maps, available for 51 of the 187 cities in DCDP.Indicates type of bounding geography . Despite the title of this column, strings for the geographical features are used as values, not 0 or 1. Source: derived visually from Google maps, available for 18 of the 187 cities in DGEA.GDP per capita per year at Purchasing Paring Parity (PPP) provided by DUITP.GDP per capita per year (PPP) provided by DWB.GDP per capita per year (PPP) provided by Demissions to indicate whether the city has corresponding ancillary data in DGEA+  or if no matching city exists in DGEA+ .Computed for each city in DINRIX. The attributes relate to congestion rate, defined as the ratio of the total drive time in congested vs. free flow traffic, in percent.The following congestion-based attributes were obtained from DThe average peak period congestion rate is applied to travel times to derive daily time spent in peak period congestion. The average number of hours spent in congestion during peak hours is then estimated assuming 240 working days a year.Average of seven congestion rates: peak periods on highways in and out of the city; peak periods within a city; day time travel on highways in and out of a city; day time travel within a city; late night on highways in and out of a city; late night within a city; weekend travel on all roads.Determined by the number of peak hours that drivers spent in congestion in 2016.The seven congestion rates (see above) weighted by relative volumes to reflect typical driving patterns, which is then weighted by the Median Travel Time, effectively adjusting the congestion rate by the city’s size and associated average journey times.TomTom. The attributes relate to congestion level, defined as the percent increase in overall travel times when compared to uncongested traffic.The following congestion-based attributes were obtained from DIncrease in overall travel times when compared to uncongested traffic. Travel times were calculated using speed measurements on individual road segments and entire networks in TomTom’s historical traffic database, weighted by the number of measurements.Percent change in congestion level compared to the previous year.Percent increase in travel time compared with uncongested traffic during morning peak times .Percent increase in travel time compared with uncongested traffic during evening peak times .Ranking based on Congestion Level; applied only to cities with population greater than 800,000.IESE: Economy, Environment, Governance, Human capital, International impact, Mobility and transportation, Public management, Social cohesion, Technology, Urban planning, the Cities in Motion index (CIMI), CIMI ranking, and CIMI performance. These indicators reflect the sustainability and standard of living of a city.Thirteen socio-economic indicators were obtained from DThe IESE indicators include both city and national level variables from several sources with objective measures  as well as subjective measure . The calculation of the synthetic indicators produces values that were standardized in order to note the differences amongst the various cities between positions more intuitively. In order to modify the scale of each city, the city with the highest score (first in the ranking) was assigned the number 100. Cities with a high performance (H) are considered to be those with an index greater than 90; relatively high (RH), between 60 and 90; average (A), between 45 and 60; and low (L), below 45.The IESE economy index includes aspects that promote economic development. This index takes into account four positively-contributing indicators – labour productivity calculated as GDP/working population [thousands], number of headquarters of publicly traded companies, percentage of 18 to 64-year-old population who are new entrepreneurs or owners/managers of a new business [per capita], and gross domestic product [millions USD at 2014 prices], and two negatively-contributing indicators – number of calendar days needed so a business can operate legally, and ease of starting a business [rank].2 emissions (fossil fuel burning and cement manufacture [kt]), CO2 emission index, methane emissions arising from human activities , PM2.5 , PM10 , and pollution index.The IESE environment index aims to reflect environmental sustainability/sustainable development. This index takes into account two positively-contributing indicators – percentage of the population with access to the water supply, and environmental performance index (1 = poor to 100 = good), and six negatively-contributing indicators – COThe IESE governance index aims to reflect the effectiveness, quality and sound guidance of state intervention. This index takes into account five positively-contributing indicators: the strength of legal rights for borrowers and lenders, corruption perceptions index (0 = very corrupt to 100 = very transparent), number of functions of the city’s innovation department, range of government Web services, and whether a city has an open data platform.The IESE human capital index aims to reflect a city’s talent and ability to create/support/retain it. The index takes into account seven indicators, considered as positive contributors to the index: the proportion of the population with secondary education, the number of business schools, the international movement of higher-level students, the number of universities, the number of museums per city, the number of art galleries per city, and the expenditure on leisure and recreation [millions USD at 2014 prices].The IESE international impact index aims to reflect the international renown of a city . This index takes into account four positively-contributing indicators: the number of international tourists, the number of passengers who travel with airlines, number of hotels per capita, and the number of international conferences and meetings hosted in a city, and one negatively-contributing indicator – ranking of cities according to the number of photos taken in the city and uploaded to Panoramio .2 consumption and other traffic system inefficiencies), traffic inefficiency index , number of road accidents per 100,000 inhabitants, and commute index (considering travel time to work).The IESE mobility and transportation index aims to reflect how well a city facilitates movement and access to public transportation services. This index takes into account three positively-contributing indicators – the number of metro stations per city, number of arrival and departure flights in a city, means of transportation (increases with increasing public transportation options), and four negatively-contributing indicators – traffic index , varies from 0 to 100, with 0 being a situation of perfectly equitable income distribution and 100 that of perfect inequality), price of property as percentage of income) – and one negatively-contributing indicator, the ratio of women workers in the public administration.The IESE social cohesion index aims to reflect the degree of social interaction and sense of belonging to a common situation or project. This index takes into account five positively-contributing indicators – the ratio of death per 100,000 inhabitants, crime rate, health index, unemployment rate (number of unemployed / labor force), the Gini index , innovation index , number of smartphones per capita, and the number of wireless access points.The IESE urban planning index aims to reflect the habitability of a city in terms of e.g. quality of health infrastructure, housing policies, design of public spaces. This index takes into account four positively-contributing indicators – percentage of the population with access to sanitation facilities, number of bicycle shops per capita, number of architecture firms per capita, and the number of cycling enthusiasts per capita, and one negatively-contributing indicator – number of people per household.The IESE Cities in Motion Index (CIMI) is a weighted aggregation of the 10 indices described above: economy: 1; human capital: 0.4814; international outreach: 0.6212; urban planning: 0.841; environment: 0.6215; technology: 0.3763; governance: 0.4047; social cohesion: 0.5941; mobility and transportation: 0.4707; and public management: 0.571.The IESE CIMI ranking is a 1–181 ranking (since there are 181 cities) of the CIMI, where 1 = highest, 181 = lowest.The IESE CIMI performance classifies a city’s performance based on its CIMI: high ; relatively high ; average ; low .emissions that are not part of DGEA+ do not have climate indices, therefore we calculated average HDD and CDD over the same period (2007–2011) as in DGEA+ for all 343 cities in Demissions for the optimal temperature thresholds of 15.5 °C for HDD and 23 °C for CDD, as explained above. The temperature data were based on the three-hourly ERA-interim reanalysis available from 1979 and updated in real time29, sampled at a spatial resolution of ≈ 80 km. Calculating climate indices from gridded data that is treated homogeneously allows comparison of different locations without possible biases such as weather station locations within a city. However, at the relatively coarse resolution of the grid, coastal and orographic effects on surface temperature may have been missed25. The city locations were obtained from the latitude and longitude coordinates in DOTHERS.The cities in Dclim at grid points closest to the lat/lon coordinates above, averaged over the same five-year period (2007–2011) as for the climate indices in DGEA+. HDD 15.5 C is defined as the sum over one year of the difference between 15.5 °C (base temperature) and the daily temperature, whenever the daily temperature is lower than the base temperature.HDD calculated for a base temperature of 15.5 °C from DGEA+ for 25 cities out of 56 cities with climate indices in DGEA+. Twelve cities are colder than predicted by the global model, and 5 of these cities are at high altitudes (>1000 m). The relatively course resolution of the ERA-interm dataset may not account for coastal and orographic effects25; this could lead to the prediction of fewer colder days than for the same coordinates but at higher altitude. The remaining 7 cities are at lower altitudes (13–935 m) and thus altitude effects likely do not explain why they are colder than the model predictions.We noted that our computed HDD 15.5 C had poor correspondence (30–90%) with observations-based HDD 15.5 C in Dclim at grid points closest to the lat/lon coordinates, averaged over the same five-year period (2007–2011) as for the climate indices in DGEA+. CDD 23 C is defined as the sum over one year of the difference between 23 °C (base temperature) and the daily temperature, whenever the daily temperature is higher than the base temperature.CDD calculated for a baseline temperature of 23 °C from DGEA+ for 53 cities out of 56 cities with climate indices in DGEA+. Forty-eight cities were warmer than predicted by the global model except for four cities , perhaps attributable to the urban heat island effect.We noted that our computed CDD 23 C had poor correspondence (30–90%) with observations-based CDD 23 C in DUEX contains three types of urban area expansion attributes for two built-up areas (BUA) values (low and high) pertaining to three different years: 1990, 2000, and 2014. This results in 3 types×2 BUAs×3 years = 18 DUEX attributes. The three types of attributes are BUAs [km2], BUA fraction [%] out of the total city area, and BUA population density [people/km2], as described below. The methodology for the preparation of DUEX, completed beforehand using ArcGIS (www.arcgis.com) and R using the city coordinates in Dclim, is as follows:Dclim. UACs are approximations, reflecting estimates of urban catchment areas and do not depict administrative borders. UACs are selected based on the global human settlement (GHS) dataset (http://data.europa.eu/89h/jrc-ghsl-ghs_smod_pop_globe_r2016a). GHS is classified into high- and low-density and rural clusters, defined as:The UAC is the study area for each city. The selection is based on city coordinates described above for DHigh-density clusters (HDCs) comprise “contiguous cells  with a density of at least 1,500 inhabitants/km2 or a built-up density greater than 50%, and a minimum of 50,000 inhabitants per cluster”30.Low-density clusters (LDCs) comprise “contiguous cells  with a density of at least 300 inhabitant/km2 and minimum of 5,000 inhabitants per cluster”30.Rural clusters include all cells outside high- and low-density clusters30. From these areas, only the areas that intersect with one of the leftover city points are selected.CDP  are classified as rural and therefore were not considered in this analysis.In the first round, contiguous areas from the HDC that intersect with one of the city points were identified and extracted. In the second round, contiguous LDC areas were identified and extracted for the city points that did not intersect with HDC in the first round. Note: two cities in D2) within the following ranges: 8,000,000–100,000,000; 100,000,001–300,000,000; 300,000,001–2,000,000,000; 2,000,000,001–4,000,000,000; 4,000,000,001–6,015,000,000 were assigned buffer distances (km) of 3, 5, 7, 10, and 13, respectively.A buffer was applied, whereby the buffer distance was chosen based on the size of the urban cluster . Classes . Datasets were classified into ‘low’ and ‘high’ for values between 0–25% and >25% of the built-up area (BUA), respectively, for the years 1990, 2000 and 2014. Classified raster data were added up for the change analysis and the areas calculated for high and low BUA.Built-up intensity changes for each UAC were calculated based on built-up area raster grids (GHG BUILT-UP_GRID) (http://data.europa.eu/89h/jrc-ghsl-ghs_pop_gpw4_globe_r2015a) depicts the 2015 population, whereas the latest built-up area layer is from 2014. The 18 attributes obtained from DUEX for 282 urban clusters corresponding to the 343 cities in Dfinal are described below:Population densities are extracted  within the classified built-up areas, high and low, for the 1990 baseline. Changes are captured from 1990–2000 and from 2000–2015. Note that the population raster dataset (GPWv4)  for year = 1990, 2000, and 2014; three attributes in total.High BUA for year = 1990, 2000, and 2014, where total BUA is the sum of low and high BUA; three attributes in total.Percentage of low BUA of the total BUA for year = 1990, 2000, and 2014, where total BUA is the sum of low and high BUA; three attributes in total.Percentage of high BUA of the total BUA for year = 1990, 2000, and 2014; three attributes in total.The population density in the low BUA for year = 1990, 2000, and 2014; three attributes in total.The population density in the high BUA for The attributes obtained from other external sources are as follows:City latitude. Used for HDD/CDD calculations.City longitude . Used for HDD/CDD calculations.https://www.mediawiki.org/wiki/GeoHack), obtained from the city’s Wikipedia page.Source of city coordinates: GeoHack (emissions where possible. We noted the largest discrepancies (30–90%) in population values in DOTHERS vs. DCDP in three cities:Population values obtained from external sources, consistent with the population and city definition in DBrasília: The population value in DCDP was 1,409,671 (2015 estimate) vs. 2,977,216 (2016 estimate) in DOTHERS (https://en.wikipedia.org/wiki/Bras%C3%ADlia#cite_note-pop-1). The IBGE average household size value from the 2010 census31 also corresponds to a population of 2,977,216.Caracas: The population value in DCDP was 3,518,590 (2015 estimate) vs. 5,290,000 (2016 estimate) in DOTHERS .Santiago de Guayaquil: The population value in DCDP was 2,350,915 (2010 estimate) vs. 3,500,000 (2014 estimate) in DOTHERS (https://en.wikipedia.org/wiki/Guayaquil).Year corresponding to the external population values.Source for external population values.emissions.City area obtained from external sources, consistent with population, area and city definition in DYear corresponding to the area value.Source for area value.22.2014 diesel price, from national values22.2014 gasoline price, from national valuesemissions. Note that the following assumptions were made to estimate household size in the cities listed below:Average number of people per household obtained from multiple sources. All updated household size values we collected were obtained for populations that matched the city population in DAddis Ababa: The 1984 value for average household size32 was used since a more recent value could not be found.Aspen and Pitkin county: Average household size for “Aspen and Pitkin county”  was assumed to be close enough to household size of “Aspen” , since population values are close (https://www.census.gov/quickfacts/fact/table/aspencitycolorado/PST045216).Bogor: The average household size of West Java in 2012 survey report33 was assumed to be representative of the average household size of Bogor, a city in West Java, since a value for Bogor itself could not be found.  which is consistent with the value obtained for a 2014 Jakarta survey34 (4.5).)Bornova: The average household size of Izmir province found in the 2011 census35 was used for Bornova.Caracas: The population of Caracas in DCDP  corresponds to the population of the Metropolitan Region of Caracas (https://en.wikipedia.org/wiki/Metropolitan_Region_of_Caracas) which is made up of the Metropolitan District of Caracas  plus 11 municipalities over Miranda and Vargas states. Therefore, to estimate household size, the household sizes of Distrito Capital, Miranda and Vargas states were averaged: 1/3×(3.41 + 3.52 + 3.58) = 3.50.Durban: The population of Durban in DCDP is 3,555,868, but actually this corresponds to the population of eThekwini Metropolitan Municipality . Therefore, the average household size for eThekwini  and not Durban  was used.Incheon: Average household size in the 2015 census report36 is stated as 2.65 for “Incheon Province”; this value was assumed to be valid for Incheon city.Jakarta: Average household size for Jakarta was based on a survey34 of 297 households in 2014.Pretoria Tshwane: The most recent household size statistic that could be found (3.40)37 corresponds to year 2007 and has a similar value as in the original DGEA+.Quito and Santiago de Guayaquil: The 1990 value for average household size38 was used since a more recent value could not be found. Note also that this value is listed as “Average number of persons per living quarters”.Suwon: Only the average household size for Gyeonggi province (2.68) could be found36. Since Suwon is the largest metropolis of Gyeonggi province, this value was assumed to be representative of the average household size in Suwon.Santiago de Cali: The average household size for Valle de Cauca in the 2005 census39 was used. Santiago de Cali is the capital of the Valle del Cauca department, and the most populous city in southwest Colombia.Year for which the mean household size above is given.Source reference for updated household sizes.GEA+ for city ‘Birmingham’ was “Calculation: pop/household (1036900/410700)”. The updated household size for Birmingham, however, was obtained directly from EUROSTAT (http://ec.europa.eu/eurostat/) without the need for calculation; therefore, this comment was removed.Updated comments pertaining to household size. For example, the original comment in Demissions. The following assumptions that were made in estimating mean commute time for some cities.Mean one-way travel time to travel to work for city citizens. All values we collected were obtained for populations that matched the city population in DAjax, ON: The mean commute time for Durham region was used since a value could not be found for the city of Ajax, Ontario.Birmingham: The mean commute time was taken from Telegraph article (http://www.telegraph.co.uk/motoring/news/3122428/Birmingham-worst-place-for-commuting-survey.html and https://www.findaphd.com/search/projectdetails.aspx?PJID=61628), but the original reference could not be found.Leicester, Manchester: The mean commute time was taken from Telegraph article (http://www.telegraph.co.uk/motoring/news/3122428/Birmingham-worst-place-for-commuting-survey.html and https://www.findaphd.com/search/projectdetails.aspx?PJID=61628), but the original reference could not be found.Seoul: Two sources for mean commute time were found: one value (41.2 min) from the 2010 Statistics Korea census referenced by a 2011 KBS World Radio article (http://world.kbs.co.kr/english/news/news_Ec_detail.htm?No=84738), and another value (48.0 min) from a “big data analysis conducted by the nation’s No. 2 mobile carrier KT” referenced in the a 2017 Korea Herald article . The latter value was used since it is most recent of the two values.Suwon: The reference above for Seoul also reports an average mean commute time (43.5 min) for Gyeonggi province. Since Suwon is the largest metropolis of Gyeonggi province, it was assumed that this was representative of Suwon.Rotterdam: The average commute time for MRDH (Metropoolregio Rotterdam Den Haag) across education levels was used40. However, MRDH comprises 23 out of 60 municipalities in Zuid-Holland province, and its population and area are much larger than Rotterdam itself .Mexico City: The mean commute time (75 min) was obtained from a 2017 Mexico News Daily article (http://mexiconewsdaily.com/news/45-days-a-year-spent-commuting-in-cdmx/), but the original El Universal report cited could not be found. However, this value is consistent with the 61–90 minutes estimation in a 2008 report from the Universidad Nacional Autónoma de México41.Caracas, Lima: mean travel time was based on 16 contributors only (www.numbeo.com).Ljubljana: mean travel time was based on 30 contributors only (www.numbeo.com).Vlinius: mean travel time was based on 46 contributors only (www.numbeo.com).Reykijavik: mean travel time was based on 22 contributors only (www.numbeo.com).Athens: mean travel time was based on 117 contributors only (www.numbeo.com).Buenos Aires: mean travel time was based on 42 contributors only (www.numbeo.com).Adelaide: mean travel time was based on 37 contributors only (www.numbeo.com).Canberra: mean travel time was based on 31 contributors only (www.numbeo.com).Melbourne: mean travel time was based on 81 contributors only (www.numbeo.com).Santiago: mean travel time was based on 36 contributors only (www.numbeo.com).Hong Kong: mean travel time was based on 53 contributors only (www.numbeo.com).Copenhagen: mean travel time was based on 49 contributors only (www.numbeo.com).Torino: mean travel time was based on 23 contributors only (www.numbeo.com).Amman: mean travel time was based on 25 contributors only (www.numbeo.com).Warsaw: mean travel time was based on 83 contributors only (www.numbeo.com).Cape Town: mean travel time was based on 74 contributors only (www.numbeo.com).Durban: mean travel time was based on 17 contributors only (www.numbeo.com).Johannesburg: mean travel time was based on 81 contributors only (www.numbeo.com).Pretoria Tshwane: mean travel time was based on 21 contributors only (www.numbeo.com).Taipei City: mean travel time was based on 26 contributors only (www.numbeo.com)Quito: calculated based on the following estimation based on four time windows weighted by the fraction of time spent for each38:0.2×(15 min/2) + 0.39×(15 min + 7.5 min) + 0.24×(30 min + 15 min) + 0.08×(60 min + 30 min)Year for which mean travel time to work is given.Source for mean travel time values.final includes GDP values at Purchasing Paring Parity (GDP-PPP) and nominal GDP (nGDP) obtained from multiple GDP assessments, as specified in the ‘source’ columns. Altogether, these references gap-filled the GDP-PPP attribute for 153 cities , and gap-filled the nGDP attribute for 211 cities .DGDP-PPP values in billions USD.GDP-PPP normalized by population values referenced in the same source.The following sources were used to obtain GDP-PPP in the order listed below:https://www.brookings.edu/research/global-metro-monitor); 140 cities matched Dfinal. The original units [$Million] were converted to [$Billion] for consistency with other GDP datasets. The population values were obtained from the same data source for the same reference year (2014).Brookings Institution for 2014 ; 5 cities matched Dfinal. The population values were obtained from the same data source for the same reference year (2015).Brookings Institution for 2015 (https://web.archive.org/web/20110504031739/https://www.ukmediacentre.pwc.com/imagelibrary/downloadMedia.ashx?MediaDetailsID=1562), obtained for 8 cities in Dfinal for which a value from Brookings Institution for 2014 was not available.PricewaterhouseCoopers (PwC) for the year 2008  Report  in billions USD.Nominal GDP normalized by population values referenced in the same source.The following sources were used to obtain nGDP in the order listed below:http://foreignpolicy.com/2012/08/07/the-most-dynamic-cities-of-2025/, or displayed as an interactive map http://www.mckinsey.com/tools/Wrappers/Redesign/InteractiveWrapper.aspx?sc_itemid={C84CB74F-A3B1-47B1-8265-6252F6D85B68}), of which 168 cities matched Dfinal.McKinsey for the year 2010 (https://stats.oecd.org/). The OECD GDP values were found for 27 cities in Dfinal that did not have a value from McKinsey 2010.The Organisation for Economic Co-Operation and Development (OECD) (available at https://en.wikipedia.org/wiki/List_of_cities_by_GDP#cite_ref-42), found for 14 cities that did not have a value from McKinsey 2010 or from OECD.Various external sources, principally from Wikipedia .https://www.cia.gov/library/publications/the-world-factbook/rankorder/rankorderguide.html).National per capita methane emissions from waste and natural gas production multiplied by city population using country emissions data from the EDGAR inventory for year 2010 (Data Citation 1) and population data from the World Factbook .Year of national natural gas export data published in the CIA World Fact Book 2014  and household size (HQF), respectively.City area  differs between datasets, which led us to define an area quality flag (AQF). Differences of the year for which population household size and Scope-1 COCDP to DGEA (attributes ‘City area (CDP) [km2]’ and ‘City area (GEA) [km2]’), AQF (CDP/GEA) = 1 is set for ratios within range  and AQF (CDP/GEA) = 0 otherwise. Inconsistent areas between DCDP and DGEA with AQF(CDP/GEA) = 0 were found for 10 out of 30 common cities. Four inconsistencies were attributable to different city boundary definitions:Determined from the ratio of city areas of DStockholm: DCDP area is for ‘City of Stockholm’, while DGEA area is for ‘Stockholm County’.Toronto: DCDP area is for ‘City of Toronto’, while DGEA area is for ‘Greater Toronto Area’.Cardiff: DCDP area is for ‘City of Cardiff’, while DGEA area is for ‘Cardiff and Vale of Glamorgan’.Birmingham: DCDP area is for ‘Birhmingham City Council’, but city boundary is not specified in DGEA.CDP and DWB, DCDP and DOTHERS, DPKU and DGEA, DPKU and DGEA, and DPKU and DOTHERS.Similarly to AQF (CDP/GEA), other AQF (X/Y) flags were computed for cities areas across pairs of datasets (X/Y), namely DCDP to DWB, AQF (CDP/WB) = 0 were found for 3 out of 15 cities. Two inconsistencies are attributable to different city boundary definitions and a data entry error:From the ratio of areas in DLos Angeles: DCDP is for ‘City of Los Angeles’ while DWB for ‘Los Angeles County’.Toronto: DCDP is for ‘City of Toronto’ whereas DWB is for ‘Greater Toronto Area’.Seattle: the DCDP area value was a typo .CDP vs. DOTHERS areas, AQF (CDP/OTHERS) = 0, were found in 19 out of 184 cities. Areas were not reported for four DCDP cities . Three data entry errors in DCDP areas were found and corrected for Faro, Porto Alegre, Salvador, and manually corrected in Dfinal. The DCDP area of Kadiovacik is likely not 5,947 km2 as reported and was estimated manually to be 0.23 km2 by drawing a square around its center using https://www.daftlogic.com/projects-google-maps-area-calculator-tool.htm). It is unclear if remaining area inconsistencies were due to different city boundary definitions or e.g. data entry errors.From the ratio of areas in DPKU vs. DGEA, AQF (PKU/GEA) = 0 were found for all 19 common cities. The built-up area reported in DPKU was consistently much smaller than the area reported in DGEA, likely due to city boundary definitions.From the ratio of areas in DPKU vs. DWB area, AQF (PKU/WB) = 0 were found for all 3 common cities. The built-up area reported in DPKU is consistently much smaller than in DWB, likely due to city boundary definitions.From the ratio of areas in DPKU vs. DOTHERS area, AQF (PKU/others) = 0, were found for 73 out of 77 cities. The built-up area reported in DPKU is consistently much smaller than the area reported in DOTHERS, likely due to city boundary definitions.From the ratio of areas in D2 emissions were estimated, which defines temporal quality flags for population (PQF) and household size (HQF).Temporal consistency means similarity of the year for which population household size and Scope-1 COemissions  and the year of DCDP population. TQF value = 1 for differences less than 5 years, and 0 otherwise. Inconsistent values were found for 14 out of 187 cities. Most of these inconsistencies were on or near the cutoff 5-year point . The remaining two cities  have recent population years  but not very recent emission years .Determined from the absolute difference between the year of Scope-1 emissions in DInconsistent values were found for 11 out of 68 cities. Most of these inconsistencies were on or near the cutoff 5-year point . The remaining city (Graz) has a recent population year (2014) but a quite out-dated emission year (1994).Inconsistent values were found for 17 out of 20 cities. In these cases, the difference in population and emission years ranged from 6–15 years.WB (attribute ‘Population 2010 (WB)’ in Dfinal). Inconsistent values were found for 2 out of 20 cities. In these cases, the difference in population and emission years was on the cutoff point of 5 years.Note that this PQF was determined from the 2010 population value in DOTHERS (difference range 5–22 years).Inconsistent values were found for 59 out of 269 cities in Demissions and the year of DGEA+ household size, where these values exist. HQF value = 1 for differences less than 15 years (given the slower changes in this variable), and 0 otherwise. No inconsistent HQF (GEA+) values were found for all 103 cities for which household size values exist in DGEA+.Determined from the absolute difference between the year of Scope-1 emissions in DOTHERS. Inconsistent values were found for two out of 176 cities for which external household size values were obtained (Graz and Piacenza). These inconsistencies are due to quite out-dated emission years [1994 for Graz (Dcarbonn) and 1990 for Piacenza (DCDP)].Defined as in HQF (GEA+) above but for household sizes in DCDP  and scope-specific values  as shown in CDP2016 values were replaced by DCDP2017 values if the latter had a better quality flag.Emission Quality Flags (EQF) were computed for DCDP2016 by those from DCDP2017, and to have EQF = D for 36 cities missing both scopes. Special case for Rotterdam: DCDP2016 data was EQF = A, but included emissions from several facilities in the port and were replaced by DCDP2017 values .EQF = A, B, C, D, or E were assigned based on the steps shown in CDP emission data, systematic errors were also introduced when removing non-CO2 gases emissions. Further, whether Scope-1 data from DCDP, and Dcarbonn and DPKU included all power plants in the territory of each city was not verified systematically against independent sources. Many of these city power plants can be small and may not be reported by each city.The uncertainty of reported emissions from cities is particularly difficult to estimate since no formal uncertainty analysis is applied by cities on the bottom-up activity and emission factor data that they collect for inventories. In our processing of DAccurate estimates for city-scale emissions uncertainties depends on a clear understanding of system boundaries, i.e. emitting activities included in the accounting for Scope-1. A large source of errors between city emission estimates is the use of distinct systems boundaries . Once these inconsistencies are corrected, the between-estimates uncertainty could be reduced.A detailed evaluation against independent estimates is arguably the best practical way to assess uncertainties when looking at a multiple-cities dataset. This is the approach we followed in this section.et al.42 provides U.S. fossil fuel CO2 emissions for the year 2002 on a 10 × 10 km grid at the level of fuel type, economic sub-sector, and county/state identification over the entire US landscape. VULCAN includes individual factories, power plants, roadways and neighborhoods. To compare with our Scope-1 values for US cities, we considered two scenarios: 1) summed VULCAN emissions from all the sectors of aircraft, cement, commercial, industrial, non-road, on-road, residential, and electricity production and 2) we removed from the sum of all-sectors the emissions from electricity production. Emissions at the location of 64 US cities in Dfinal (63 from DCDP and 1 from Dcarbonn) were collocated with the gridded VULCAN data, assuming that each city is approximately a circle of area corresponding to the reported administrative area in our dataset. Scope-1 emissions and Scope-1 emission density (emissions/area) in Dfinal were regressed against the two corresponding VULCAN scenarios including and excluding emissions from electricity production. The correlation coefficient (R2) when electricity production emissions were included was 0.69 (respectively 0.70 when excluded) for Scope-1 emissions and 0.56 (0.59) for Scope-1 emission density. The lower correlation coefficient in emission density for both cases is due to the coarse approximation of the city shape (using circles for cities areas instead of detailed GIS administrative area shape files overlaid with VULCAN gridded data) and the low resolution of VULCAN, and/or the different years they represent.The VULCAN dataset from Gurney final Scope-1 emissions. MAD = 99% and 82%, including and excluding power plant electricity generation, respectively. Most of this error is of a systematic nature due to the following reasons: emissions are for year 2002 in VULCAN and for more recent years in Dfinal, the VULCAN gridded data were not sampled for the precise administrative area of Dfinal in each US city, we lack precise knowledge on whether all local power plant emissions were included in Scope-1 data in Dfinal as compared to the comprehensive set of city power plants included in VULCAN. Scope-1 emissions of at least 6 out of 14 US cities in Dfinal include power plants; but 20 cities in Dfinal reported zero within-city power plant emissions in their Scope-1 emissions. Other cities likely reported power plants but we could not verify it from their declarations.We also calculated the median absolute deviation (MAD) between VULCAN and D2 emissions in Dfinal for US cities is described in Markolf et al.43. The authors compiled publicly available national datasets for estimating emissions in the 100 most populated metropolitan areas in the US in 2014. Twenty-eight cities in Markolf et al. overlapped with Dfinal. Markolf et al. documented separately CO2 emissions from on-road transportation, electricity generation, industrial processes, residential buildings, commercial buildings and waste. Then, the emissions were divided by population because the metropolitan areas in Markolf et al. generally encompass a wider territory than in Dfinal. The ratio between per capita Scope-1 emissions of Markolf et al. to Dfinal was calculated, both including and excluding electricity generation in Markolf et al. When electricity generation was excluded, there were three outliers with a ratio ≥2 ; the mean ratio excluding these outliers was 1.2 ± 0.4. When electricity generation was included, there were 11 outliers with ratios ≥2; the mean ratio excluding these outliers was 1.3 ± 0.4.The second dataset against which we compared Scope-1 COfinal per-capita Scope-1 emissions for the 28 common US cities is 393% and 175% . These ratios were reduced when the outliers (see above) were removed, giving MAD of 197% and 161% . We note that some of the inventory data used by Markolf et al. may be common with DCDP and Dcarbonn, thus Markolf et al. cannot be proven to be strictly independent from our data.The MAD statistics between Markolf and Dfinal Scope-1 CO2 emissions was developed for all municipalities in Japan and described in Nakamichi et al.44 The authors computed direct CO2 emissions using a bottom-up (built-up) approach based on emission factors and other statistics attributed to four civilian categories: residential, commercial, industrial (including electricity production) and transportation, as well as the total integrated direct emissions. The year of analysis was 2005 for all categories. Six cities in Nakamichi et al. overlapped with Dfinal. The ratio between Scope-1 emissions of Nakamichi et al. to Dfinal was calculated, taking into account the ratios between reported population and city areas, which are in agreement . The mean Scope-1 emission ratio was 0.8 ± 0.4 (range 0.4–1.3). Excluding Tokyo, corresponding to the worst ratio (0.4) despite consistent population and area ratios, the mean Scope-1 emission ratio was 0.9 ± 0.3 (range 0.5–1.3).The third dataset against which we compared DHow to cite this article: Nangini, C. et al. A global dataset of CO2 emissions and ancillary data related to emissions for 343 cities. Sci. Data. 6:180280 doi: 10.1038/sdata.2018.280 (2019).Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.