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Carbon Dioxide Emission Estimates from Fossil-Fuel Burning, Hydraulic Cement Production, and Gas Flaring for 1995 on a One Degree Grid Cell Basis (NDP-058a)

Prepared by
Antoinette L. Brenkert
Carbon Dioxide Information Analysis Center
Oak Ridge National Laboratory
Oak Ridge, Tennessee 37831-6290

Date Published: February 1998 (Revised for the Web: 2003)

CONTENTS

Abstract

Carbon Dioxide Emission Estimates from Fossil-Fuel Burning, Hydraulic
Cement Production, and Gas Flaring for 1995 on a One Degree Grid Cell
Basis.
(March 1998)

Antoinette L. Brenkert

DOI: 10.3334/CDIAC/ffe.ndp058.2003

This data package presents the gridded (one degree latitude by one degree longitude) summed emissions from fossil-fuel burning, hydraulic cement production and gas flaring for 1995. Analogous to the data presented in NDP-058 (which includes estimates for 1950, 1960, 1970, 1980, and 1990), national emission estimates from the 1995 United Nations Energy Statistics Database (U.N., 1997), hydraulic cement production estimates from the U.S. Department of Interior's Bureau of Mines (USDO,1995), and supplemental data on gas flaring from the U.S. Department of Energy's Energy Information Administration were processed by Marland et al. (1997) following the methods of Marland and Rotty (1984). The only change in the methodology used to calculate the national CO2 emission estimates for 1995 was the implementation of separate carbon coefficients for soft and hard coal; the emissions estimates in NDP058 were calculated using a single carbon coefficient to characterize the carbon content of all coals. To distribute the national emission estimates from 1995 within each country, the population data base developed by Li (1996a) and documented by CDIAC (DB1016: Li, 1996) was used as proxy. Previously, Andres et al. (1996) had used a 1984 human population data set (Goddard Institute of Space Studies, Lerner et al., 1988) as proxy for gridding the 1950 through 1990 emission estimates within countries. The structure of the gridded 1995 emission data file differs, consequently, from the1950-1990 gridded emission files (CDIAC: NDP-058) in that individual grid cells may have been partitioned into more than one country analogous to Li's population data base. A country's representation in a grid cell is quantified by the percentage of that country's land area in a particular grid cell and identified by its United Nations identification code. The percentages and United Nations identification codes were used to allocate the national CO2 emissions estimates to the grid cells. Only those grid cells with a United Nations identification code, population estimate and carbon emission estimate are listed in the data file. Grid cells representing more than one country are repeated for each country represented. Note that to calculate national estimates from the data file, one has to sum by United Nations identification code. To calculate emissions for each grid cell or by latitude one has to sum by grid cell (latitude and longitude), or by latitude, respectively. A number of manipulations of Li's population data base were necessary (and documented) to properly distribute the national 1995 CO2 emission estimates over each country's grid cells.

Map: Carbon Dioxide Emission Estimates

(Map by Richard Olson and Holly Gibbs, ORNL/ESD)

Documentation file for Data Base NDP-058a (2-1998)

This data package presents the gridded (one degree latitude by one degree longitude) summed emissions from fossil-fuel burning, hydraulic cement production and gas flaring for 1995. Analogous to the data presented in NDP-058 (which includes estimates for 1950, 1960, 1970, 1980, and 1990), national emission estimates from the 1995 United Nations Energy Statistics Database (U.N., 1997), hydraulic cement production estimates from the U.S. Department of Interior's Bureau of Mines (USDO, 1995), and supplemental data on gas flaring from the U.S. Department of Energy's Energy Information Administration were processed by Marland et al. (1997) following the methods of Marland and Rotty (1984). The only change in the methodology used to calculate the national CO2 emission estimates for 1995 was the implementation of separate carbon coefficients for soft and hard coal; the emissions estimates in NDP058 were calculated using a single carbon coefficient to characterize the carbon content of all coals. To distribute the national emission estimates from 1995 within each country, the population data base developed by Li (1996a) and documented by CDIAC (DB1016: Li, 1996) was used as proxy. Previously, Andres et al. (1996) had used a 1984 human population dataset (Goddard Institute of Space Studies, Lerner et al., 1988) as proxy for gridding the 1950 through 1990 emission estimates within countries. The structure of the gridded 1995 emission data file differs, consequently, from the 1950-1990 gridded emission files (CDIAC: NDP-058) in that individual grid cells may have been partitioned into more than one country analogous to Li's population data base. A country's representation in a grid cell is quantified by the percentage of that country's land area in a particular grid cell and identified by its United Nations identification code. The percentages and United Nations identification codes were used to allocate the national CO2 emissions estimates to the grid cells. Only those grid cells with a United Nations identification code, population estimate and carbon emission estimate are listed in the data file. Grid cells representing more than one country are repeated for each country represented. Note that to calculate national estimates from the data file, one has to sum by United Nations identification code. To calculate emissions for each grid cell or by latitude one has to sum by grid cell (latitude and longitude), or by latitude, respectively. A number of manipulations of Li's population data base were necessary (and documented) to properly distribute the national 1995 CO2 emission estimates over each country's grid cells.

The data base contains:

  1. Documentation on the 1995 gridded carbon dioxide (CO2) emission data (in units of 1000 metric tons C per year per one degree latitude by one degree longitude grid cell).
  2. Detailed data file description.
  3. FORTRAN program to read the gridded CO2 emission data file.
  4. SAS code to read the gridded CO2 emission data file.
  5. Simple summary statistics.
  6. CDIAC's quality assurance checks.
  7. Instructions on how to obtain the data and documentation.
  8. References.
(A) Documentation

    Analogous to the data presented in NDP-058 (which includes estimates 
    for 1950, 1960, 1970, 1980, and 1990; Andres et al., 1996b),this document 
    presents the summed emissions from fossil-fuel burning, hydraulic cement 
    production and gas flaring for 1995.  National CO2 emission estimates 
    derived from the 1995 United Nations Energy  Statistics Database (U.N., 
    1997), hydraulic cement production estimates compiled by the U.S. 
    Department of Interior's Bureau of Mines (USDO,  1995), and supplemental 
    data on gas flaring obtained from the U.S. Department of Energy's Energy 
    Information Administration were processed by Marland et al. (1997) 
    following the methods of Marland and Rotty (1984). The only change in the 
    methodology used to calculate the  national CO2 emission estimates for 
    1995 was the implementation of  separate carbon coefficients for soft and 
    hard coal; the emissions estimates in NDP058 were calculated using a 
    single carbon coefficient to characterize the carbon content of all coals.

    To distribute the national emission estimates from 1995 within each 
    country, the population data base developed by Li (1996a) and documented 
    by CDIAC (DB1016: Li, 1996b) was used as proxy.  Previously, Andres et al. 
    (1996a) had used a 1984 human population data set (Goddard Institute of 
    Space Studies, Lerner et al., 1988) as proxy for gridding the 1950 through 
    1990 emission estimates within countries.  The structure of the gridded 
    1995 emission data file differs,  consequently, from the 1950-1990 gridded 
    emission files (CDIAC: NDP-058) in that individual grid cells may have 
    been partitioned  into more than one country analogous to Li's population 
    data base.  A country's representation in a grid cell is quantified by 
    the percentage of that country's land area in a particular grid cell and 
    identified by its United Nations identification code. The percentages and 
    United Nations identification codes were used to allocate the  national 
    CO2 emissions estimates to the grid cells.  Only those grid cells with a 
    United Nations identification code, population estimate  and carbon 
    emission estimate are listed in the data file. Grid cells  representing 
    more than one country are repeated for each country represented.  Note that 
    to calculate national estimates from the data file, one has to sum by United 
    Nations identification code. To calculate emissions for each grid cell or by 
    latitude one has to sum by grid cell (latitude and longitude), or by 
    latitude, respectively.

    A number of manipulations of Li's population data base were necessary to 
    properly distribute the national 1995 CO2 emission estimates.  When CO2 
    emission estimates were available for locations not represented in Li's 
    population data base (DB1016) we added those locations and calculated the 
    representation (percentage) of the added country as grid cell information. 
    The following seven sections summarize the changes:

(1) Li's alphanumeric identification codes were converted to numeric
    codes, and to United Nations codes where possible:
      C04 to 904 Canary-Islands
      B03 to 903 Jersey
      B02 to 902 Gaza-Strip
      B01 to 901 Guernsey
      B10 to 910 Israeli-occ-ter.
      C11 to 911 St.-Martin
      C16 to   0 Ocean
      C07 to 579 Jan-Mayen, to add to Norway
      B07 to 654 St.-Helena

(2) A number of Li's identification codes were changed to match the energy statistics UN codes: 250 to 251: France, to include Monaco 380 to 382: Italy, to include San Marino 578 to 579: Jan-Mayen, to be included with Norway 744 to 579: Svalbard, to be include in Norway 414 to 415: Kuwait, to include part of the Neutral Zone 682 to 684: Saudi Arabia, to include part of the Neutral Zone 886 to 887: Yemen (UN code 886) and 720 to 887: Democratic Yemen (UN code 720) merged on 22 May, 1990 to form a single state (UN code 887); 756 to 757: Liechtenstein, to join Switzerland (percentages had to be adjusted) 583 to 316: for Guam 584 to 582: for Carolina, Mariana, and Marshall Islands, but excluding Guam 585 to 582: for Carolina, Mariana, and Marshall Islands, but excluding Guam 580 to 582: for Carolina, Mariana, and Marshall Islands, but excluding Guam

(3) A number of locations and UN identification codes were added: 659 added: St-Kitts, 1 grid cell (lat=17.5 and long=-62.5; perc=100) 570 added: Niue, 2 grid cells (lat=-18.5 and long=-169.5, lat=-19.5 and long=-169.5; perc=50) 462 added: Maldives, 3 grid cells (lat=2.5 and long=73.5, lat=3.5 and long=73.5, lat=4.5 and long=73.5; perc=33.3) 184 added: Cook Island, 5 grid cells (lat=-19.5 and long=-159.5, lat=-19.5 and long=-158.5, lat=-19.5 and long=-157.5, lat=-18.5 and long=-162.5, lat=-18.5 and long=-159.5; perc=20) 016 added: American Samoa, 1 grid cell (lat=-14.5 and long=-171.5; perc=100) 666 added: St Pierre, 1 grid cell (lat=45.5 and long=-55.5; perc=100) 872 added: Wake island, 1 grid cell (lat=18.5 and long=166.5; perc=100) 520 added: Nauru, 1 grid cell (lat=-0.5 and long=167.5; perc=100)

(4) Czechoslovakia (formerly with UN-id 200) was split and percentages adjusted: 203: Czech Republic for cells west 17 of degrees E and north of 49 degrees N at 17.5 degrees E (perc=100.*perc/59.643). 703: Slovakia for cells east of 18 E and south of 49 degrees N at 17.5 degrees E (perc=100.*perc/40.355).

(5) The Socialist Federal Republic of Yugoslavia (formerly with UN-id 890) was split and percentages adjusted: 70: Bosnia (perc=perc*100/15.169) long=16.5 and lat=44.5 long=17.5 and lat le 44.5 and lat ge 43.5 long=18.5 and lat le 44.5 and lat ge 43.5 long=19.5 and lat=43.5 long=19.5 and lat=44.5 807: Macedonia (perc=perc*100/9.461) long ge 21.5 and lat le 41.5 long=22.5 and lat=42.5 705: Slovenia (perc=perc*100/8.088) long le 14.5 and lat ge 44.5 long=15.5 and lat=46.5 191: Croatia (perc=perc*100/17.023) long=14.5 and lat= 44.5 long=15.5 and lat le 45.5 long=16.5 and lat ge 45.5 long=16.5 and lat= 43.5 long=17.5 and lat=42.5 long=17.5 and lat ge 45.5 long=18.5 and lat= 45.5 long=18.5 and lat=45.5 891: Yugoslavia (perc=perc*100/50.262) long=18.5 and lat=42.5 long=19.5 and lat=41.5 long=19.5 and lat=42.5 long=19.5 and lat=45.5 long=19.5 and lat=46.5 long=20.5 long=21.5 and lat=42.5 long ge 21.5 and lat ge 43.5

(6) A number of population percentages were adjusted slightly in order to complete the distribution of all national CO2 estimates (i.e., so that the sum of all grid cells equals more closely the sum of the national totals) (note that only the first four adjustment caused significant improvement in the national fossil-fuel estimates): Country Adjustment factor Russian Federation (3307 cells): 100/99.8969 United States (1310 cells): 100/99.9478 China (1075 cells): 100/99.9673 Brazil (799 cells): 100/99.9885 Australia (790 cells): 100/99.9687 India (356 cells): 100/99.9841 Kazakhstan (404 cells): 100/99.9850 Mexico (243 cells): 100/99.9863 Iran (195 cells): 100/99.9868 Indonesia (316 cells): 100/99.9871 Canada (2086 cells): 100/99.9972 (7) Antarctic and ocean cells were deleted. (B) Detailed data file (ff95.dat) description

Parameter columns lat 1-6 long 8-13 ff95 16-23 popli 26-35 perc 38-46 unid 48-50 name 53-72 where: lat and long indicate the center point of a grid cell in decimal degrees. ff95 is the 1995 grid cell CO2 emission estimate from fossil-fuel burning, cement production, and gas flaring expressed in thousand metric tons carbon. popli is Li's (1996b) population estimate for a grid cell for a specific country. perc is the percentage of the national population of a country represented in a grid cell. unid is the United Nations identification code. name is the name of the country as identified by Li (1996b). Note: the data can easily be retrieved into spreadsheet software, given that the data are arranged in space delimited columns. (C) FORTRAN program to read the gridded CO2emission data file.

Program readff95 c double precision to avoid round-off errors real*8 lat,long,popli,ff95,percr,spop,sff95,sperc character*20 name open(10,file='ff95.dat',status='old') c prepare for summations: spop=0.d0 sff95=0.d0 sperc=0.d0 do 100 i=1,19969 read(10,10,end=911)lat,long,ff95,popli,perc,unid,name 10 format(f6.1,x,f6.1,2x,f8.2,2x,e10.0,2x,f8.4,2x,f3.1,2x,a20) write(12,10)lat,long,ff95,popli,perc,unid,name spop=spop+popli sperc=sperc+perc sff95=sff95+ff95 100 continue 911 continue i=i-1 c write summations to screen: write(*,*)'spop=',spop write(*,*) 'sperc=',sperc write(*,*) 'sff95=',sff95 write(*,*) 'lines read=',i stop end Result: spop= 5291059610.00000 sperc= 22099.6375999998 sff95= 6172868.54000011 lines read= 19969 (D) SAS code to read the gridded CO2 emission data file.

data fin; infile 'ff95.dat'; input @1 lat 6.1 @8long 6.1 @16 ff95 8.2 @26 popli 10. @38 perc 8.4 @48 unid 3.@53 name $char20.; proc sort; by unid; proc means noprint; by unid; var perc ff95 popli; id name; output out=sums sum=spercsff95 spop; data sums; set sums; file 'out'; put @10 unid 3. @15 name$char20. @37 _freq_ 4. @43 sff95 8.1 @53 sperc 8.4 @64 spop 10.; The file 'out' is printed as section (E)

(E) Summary statistics concerning the data file: --------------------------------------------------------------------- UN-id name # of National total total cells emission summed population estimates percentage estimates for 1995 (Li, 1996b) --------------------------------------------------------------------- 4 Afghanistan 92 338.7 99.9977 16556000 8 Albania 8 503.8 99.9970 3250001 12 Algeria 251 24907.5 99.9952 24960006 16 American-Samoa 1 74.5 100.0000 0 20 Andorra 1 0.0 100.0000 55300 24 Angola 131 1255.5 99.9985 9194019 660 Anguilla 2 0.0 100.0000 6900 28 Antigua-and-Barbuda 1 87.9 100.0000 65000 32 Argentina 345 35330.6 99.9900 32321997 51 Armenia 15 996.3 100.0000 3373239 533 Aruba 3 491.5 100.0000 61000 36 Australia 790 79096.7 99.9943 17065026 40 Austria 22 16180.2 100.0078 7712003 31 Azerbaijan 21 11619.7 99.9970 7138480 44 Bahamas 13 466.1 100.0038 255000 48 Bahrain 2 4047.8 100.0010 503000 50 Bangladesh 27 5713.1 99.9960 113684002 52 Barbados 2 224.7 100.0000 257000 112 Belarus 46 16184.4 99.9961 10197931 56 Belgium 12 28333.9 100.0000 9934999 84 Belize 7 113.0 100.0040 189000 204 Benin 18 173.1 100.0004 4621998 60 Bermuda 1 123.9 100.0000 61000 64 Bhutan 13 65.1 99.9920 1539000 68 Bolivia 122 2858.9 99.9974 7171003 70 Bosnia-Herzegovina 7 503.2 100.0000 3608983 72 Botswana 64 611.9 99.9946 1238001 76 Brazil 799 68012.4 99.9942 149041985 96 Brunei 2 2246.8 100.0000 257000 100 Bulgaria 22 15475.5 100.0080 8991000 854 Burkina 38 261.2 99.9972 8993000 108 Burundi 6 58.1 100.0040 5492001 116 Cambodia 27 136.4 100.0006 8336002 120 Cameroon 61 1130.5 99.9955 11523998 124 Canada 2086 118927.3 99.9972 26646873 904 Canary-Islands 4 0.0 0.0000 0 132 Cape-Verde 2 31.0 100.0000 363000 136 Cayman-Islands 3 83.7 100.0000 25500 140 Central-African-Rep. 70 64.5 99.9946 3007998 148 Chad 131 25.9 100.0009 5552996 152 Chile 135 12036.7 99.9953 13173003 156 China 1075 871310.9 99.9965 1130311683 170 Colombia 130 18428.6 99.9952 32299987 174 Comoros 1 18.4 100.0000 543000 178 Congo 47 346.3 99.9982 2228998 184 Cook-Isl. 5 5.9 100.0000 0 188 Costa-Rica 11 1428.2 100.0000 3034999 191 Croatia 10 4643.7 100.0012 4049911 192 Cuba 27 7933.5 100.0043 10608001 196 Cyprus 5 1413.4 100.0010 702000 203 Czech-Republic 16 30581.3 100.0016 9341567 208 Denmark 16 14976.1 100.0052 5139999 262 Djibouti 5 101.3 100.0000 440001 212 Dominica 1 21.8 100.0000 72000 214 Dominican-Republic 8 3212.1 100.0110 7170000 218 Ecuador 34 6176.9 100.0014 10547002 818 Egypt 117 25020.6 99.9912 52425996 222 El-Salvador 6 1415.9 99.9970 5172000 226 Equatorial-Guinea 7 36.0 99.9990 351999 233 Estonia 18 4487.8 99.9964 1580040 230 Ethiopia 130 962.1 99.9930 49830992 238 Falkland-Islands 7 11.2 100.0000 2000 234 Faroe-Islands 1 169.1 100.0000 48000 242 Fiji 7 200.9 100.0060 731000 246 Finland 88 13922.1 99.9929 4986000 251 France 90 92813.4 99.9952 56735007 254 French-Guiana 14 237.8 99.9994 98001 258 French-Polynesia 2 153.2 100.0000 198000 266 Gabon 36 967.2 100.0023 1159001 270 Gambia 4 58.6 99.9980 861000 902 Gaza-Strip 1 0.0 100.0000 624000 268 Georgia 20 2113.6 100.0002 5463671 276 Germany 64 227924.4 100.0020 79364991 288 Ghana 34 1104.4 100.0005 15020001 292 Gibraltar 1 62.0 100.0000 31047 300 Greece 39 20821.4 100.0061 10089000 304 Greenland 770 137.4 99.9999 55559 308 Grenada 1 46.1 100.0000 91000 312 Guadeloupe 1 416.4 100.0000 390000 320 Guatemala 18 1961.9 99.9978 9197001 901 Guernsey 1 0.0 100.0000 57000 324 Guinea 35 294.7 99.9981 5755003 624 Guinea-Bissau 8 62.8 100.0030 964000 328 Guyana 30 254.5 99.9990 796002 332 Haiti 7 174.3 100.0050 6486000 340 Honduras 18 1052.1 100.0092 5137998 344 Hong-Kong 2 8459.0 100.0000 5705000 348 Hungary 20 15249.6 99.9973 10361000 910 Israeli-occ-terr. 1 0.0 100.0000 1584700 352 Iceland 40 492.0 99.9973 254994 356 India 356 248016.9 99.9965 846190994 360 Indonesia 316 80821.2 99.9974 184282991 364 Iran 195 71987.3 99.9982 58266982 368 Iraq 61 27018.3 99.9955 18079998 536 Iraq-Saudi-Arabia-N. 5 0.0 0.0000 0 372 Ireland 20 8797.5 99.9910 3503001 376 Israel 9 12640.8 99.9900 4644999 382 Italy 70 111890.8 99.9963 57661000 384 Ivory-Coast 40 2827.5 99.9955 11980002 388 Jamaica 5 2470.5 100.0080 2402999 392 Japan 81 307523.8 100.0011 123536998 903 Jersey 1 0.0 100.0000 84000 400 Jordan 17 3632.0 100.0050 3282000 398 Kazakhstan 404 60447.5 99.9975 16744198 404 Kenya 67 1824.2 99.9980 23584999 296 Kiribati 2 5.9 100.0000 71000 415 Kuwait 6 13297.5 100.0020 2143000 417 Kyrgyzstan 42 1490.8 100.0035 4412880 418 Laos 38 84.4 100.0046 4202002 428 Latvia 21 2542.9 99.9957 2682306 422 Lebanon 4 3641.3 99.9980 2740000 426 Lesotho 8 0.0 100.0000 1747001 430 Liberia 17 87.1 100.0004 2574999 434 Libya 173 10753.4 99.9944 4545007 440 Lithuania 21 4043.0 99.9997 3727035 442 Luxembourg 3 2527.9 100.0000 414000 446 Macau 1 335.7 100.0000 463000 807 Macedonia 4 2934.3 100.0000 2250871 450 Madagascar 72 306.5 100.0030 12009996 454 Malawi 21 197.8 100.0080 9581999 458 Malaysia 52 29095.7 100.0033 17891003 462 Maldives 3 50.2 99.9000 0 466 Mali 138 126.7 99.9943 9214001 470 Malta 1 470.9 100.0000 354000 474 Martinique 2 556.4 100.0000 361000 478 Mauritania 114 837.3 100.0000 2024001 480 Mauritius 1 407.0 100.0000 1075000 175 Mayotte 1 0.0 100.0000 94300 484 Mexico 243 97662.1 99.9977 84486010 316 Micronesia 3 1128.6 100.0000 111000 498 Moldova 14 2951.9 100.0015 4366792 492 Monaco 1 0.0 100.0000 29800 496 Mongolia 234 2308.0 99.9951 2189973 500 Montserrat 1 11.7 100.0000 12400 504 Morocco 60 7994.7 99.9951 25061003 508 Mozambique 101 270.8 99.9999 14200010 104 Myanmar 91 1919.5 99.9956 41825003 516 Namibia 94 0.0 99.9968 1438997 520 Nauru 1 37.7 100.0000 0 524 Nepal 28 417.5 99.9890 19570999 528 Netherlands 14 37089.8 99.9920 14944000 530 Netherlands-Antilles 2 1761.6 100.0000 175000 540 New-Caledonia 9 468.0 100.0030 168001 554 New-Zealand 60 7489.0 99.9998 3329998 558 Nicaragua 20 737.5 100.0033 3675999 562 Niger 130 305.1 99.9930 7730998 566 Nigeria 100 24757.4 99.9937 108541998 570 Niue 2 0.8 100.0000 0 408 North-Korea 27 70138.3 99.9997 21771002 579 Norway 153 19774.1 99.9991 4242011 512 Oman 45 3116.5 99.9980 1524000 586 Pakistan 115 23294.5 99.9943 118121999 582 Palau-Islands 6 65.3 100.0000 15100 591 Panama 15 1882.4 100.0006 2418001 598 Papua-New-Guinea 70 677.5 99.9945 3875001 600 Paraguay 53 1036.2 100.0020 4277001 604 Peru 145 8340.4 99.9946 21549997 608 Philippines 69 16690.5 99.9940 62437000 612 Pitcairn 1 0.0 100.0000 61 616 Poland 57 92258.2 99.9974 38118994 620 Portugal 16 14172.0 99.9990 9868001 630 Puerto-Rico 4 4239.9 100.0080 3530000 634 Qatar 5 7920.6 100.0030 427001 638 Reunion 2 424.5 100.0000 604000 642 Romania 44 33050.0 100.0014 23207000 643 Russian-Federation 3307 496181.9 99.9922 148546837 646 Rwanda 5 133.6 100.0000 7027001 674 San-Marino 1 0.0 0.0000 0 678 Sao-Tome 2 20.9 100.0000 119000 684 Saudi-Arabia 211 69389.0 99.9960 14870010 686 Senegal 28 836.4 100.0078 7326997 690 Seychelles 1 44.4 100.0000 71000 694 Sierra-Leone 12 120.6 100.0000 4150998 702 Singapore 1 17377.1 100.0000 2710000 703 Slovakia 15 10381.1 99.9971 6319435 705 Slovenia 7 3196.1 99.9865 1924022 90 Solomon-Islands 16 43.5 100.0062 319999 706 Somalia 80 3.1 100.0006 8677003 710 South-Africa 153 83455.7 99.9918 37958996 410 South-Korea 19 101957.7 99.9951 43377000 724 Spain 82 63211.7 100.0019 38958995 144 Sri-Lanka 12 1606.5 99.9965 17217000 659 St-Kitts-(81-) 1 26.0 100.0000 0 666 St-Pierre 1 19.3 100.0000 0 654 St.-Helena 1 1.7 100.0000 7100 662 St.-Lucia 3 51.9 100.0000 133000 911 St.-Martin 1 0.0 0.0000 0 670 St.-Vincent 1 34.3 100.0000 107000 736 Sudan 246 955.0 99.9918 25202978 740 Suriname 17 587.0 99.9950 422001 748 Swaziland 6 123.8 100.0000 751000 752 Sweden 106 12168.8 99.9930 8565999 757 Switzerland 13 10603.7 99.9982 6740452 760 Syria 33 12561.5 100.0028 12355001 762 Tadzhikistan 37 1021.0 100.0028 5359952 158 Taiwan 8 47538.4 99.9983 20352966 834 Tanzania 97 665.8 99.9954 25993015 764 Thailand 74 47773.0 99.9990 54676995 768 Togo 13 203.3 99.9950 3530998 776 Tonga 1 28.5 100.0000 96000 780 Trinidad 2 4669.5 99.9980 1236000 788 Tunisia 28 4178.2 99.9972 8057002 792 Turkey 109 45281.6 99.9974 55991002 795 Turkmenistan 76 7733.0 99.9953 3714270 796 Turks-Caicos-Isl. 2 0.0 100.0000 12350 800 Uganda 33 284.8 99.9991 17560001 804 Ukraine 107 119594.5 99.9959 51938119 784 United-Arab-Emirates 17 18643.1 100.0040 1588998 826 United-Kingdom 58 147956.0 99.9948 57620998 840 United-States 1310 1407257 99.9946 248769679 858 Uruguay 26 1467.9 99.9936 3093999 860 Uzbekistan 80 26985.7 99.9988 20702528 548 Vanuatu 6 16.7 99.9960 149999 862 Venezuela 106 49190.0 99.9931 19320998 704 Vietnam 57 8654.2 99.9990 66687999 92 Virgin-Islands-(UK) 1 14.2 100.0000 16600 850 Virgin-Islands-(USA) 1 3121.5 100.0000 107000 872 Wake-Isl. 1 16.8 100.0000 0 732 Western-Sahara 35 57.0 99.9960 158000 882 Western-Samoa 2 36.0 100.0000 160000 887 Yemen 55 3932.8 99.9972 11684005 891 Yugoslovia 18 9015.8 99.9975 11957216 180 Zaire 232 572.6 99.9922 37390987 894 Zambia 90 655.9 99.9912 8137997 716 Zimbabwe 47 2656.6 99.9940 9947006 Total # lines in data file: 19969 Total 1995 CO2 emissions: 6172868.6(1000 metric tons C) Total population: 5291059610 Total percentage: 22099.62 (use only to verify data transport) (F) CDIAC's quality assurance checks. 1) The population divisions of Czechlovakia and the former Socialist Federal Republic of Yugoslavia were checked against the 1995 U.N. population statistics provided by the U.N. Statistical Office. 2) The national populations were checked against values published in DB1016 (Li, 1996b) 3) The gridded national fossil-fuel emission summations were checked against the values available in NDP-030 (Boden, 1998; Marland et al, 1997)) (the largest difference is 8 units for the United Kingdom). 4) The summed national emissions published in NDP030R8 amounted to 6172918 units of 1000metric tons C. The summed gridded national estimates presented here amount to 6172869 units of 1000 metric tons C. The total difference of 49 units (due to roundoff) is less than 0.001%. 5) Latitudinal summations of the gridded emissions were compared with previously published gridded data (NDP-058) and a graph produced (lat. gif). (G) Instructions on how to obtain the data and documentation. This data base (NDP-058A)and the related NDP-058 and NDP-030/R8 are available free of charge from CDIAC. The files are available from CDIAC's anonymous FTP (file transfer protocol) area via the Internet. Obtaining the data from CDIAC's anonymous FTP area requires a computer with FTP software and access to the Internet. Commands used to obtain the data base are shown below. For additional information, contact CDIAC. >ftp cdiac.ornl.gov or >ftp 128.219.24.36 Login: anonymous Password: YOU@your internetaddress Guest login ok, access restrictions apply. ftp> cd pub/ndp058a/ ftp> dir ftp> mget files ftp> quit (H) References: Andres, R.J., G. Marland, I. Fung, and E. Matthews. 1996a. A one degree by one degree distribution of carbon dioxide emissions from fossil-fuel consumption and cement manufacture, 1950-1990. Global Biogeochemical Cycles10:3:419-429. Andres, R.J., G. Marland, I. Fung, E. Matthews, and A.L. Brenkert. 1996b. Geographic patterns of carbon dioxide emissions from fossil-fuel burning, hydraulic cement production, and gas flaring on a one degree by one degree grid cell basis: 1950 to 1990. ORNL/CDIAC-97, NDP-058. Carbon Dioxide Analysis Center, Oak Ridge, Tennessee. http://cdiac.ornl.gov/epubs/ndp/ndp058/ndp058.html. U.S. Department of Interior, 1995. Annual Review, U.S. Geological Survey, Gordon P. Eaton, Director. Reston, VA 20192. February, 1997. Boden, T. A., G. Marland, and R. J. Andres, 1996. Estimates of global, regional, and national annual CO2 emissions from fossil-fuel burning, hydraulic cement production, and gas flaring: 1950-1992, Rep. ORNL/CDIAC-90, NDP-030/R6, 600 pp., Oak Ridge Nat. Lab., Oak Ridge, Tenn. http://cdiac.ornl.gov/ndps/ndp030.html. Lerner, J., E. Matthews and I. Fung, 1988. Methane emissions from animals: A global high-resolution database. Global Biochemical Cycles, 2:139-156. Li., Y.-F., A. McMillan, and M. T. Scholtz. 1996a. Global HCH usage with 1 degrees x 1 degrees longitude/latitude resolution. Environmental Science & Technology 30:3525-33. Li., Y.-F. 1996b. Global Population Distribution (1990), Terrestrial Area and Country Name Information on a One by One Degree Grid Cell Basis. ORNL/CDIAC-96, DB1016, Carbon Dioxide Analysis Center, Oak Ridge, Tenn. http://cdiac.ornl.gov/ndps/db1016.html. Marland, G., and R. M. Rotty, 1984. Carbon dioxide emissions from fossil-fuels: A procedure for estimation and results for 1950-1982. Tellus 36(B):232-261. Marland, G., T. A. Boden, A. L. Brenkert, R.J. Andres, and J.G.J. Olivier.1997. CO2 from fossil fuel burning: Updates on the magnitude, distribution, and uncertainty of emission estimates. p. 4 In '97CO2 Extended Abstracts, Fifth International Carbon Dioxide Conference, Cairns, Queensland, Australia. United Nations, 1997. Statistical Yearbook. United Nations Statistics Division, United Nations, New York. N.Y. 10017.