Carbon Dioxide Emission Estimates from
Fossil-Fuel Burning, Hydraulic Cement Production, and Gas Flaring for 1995 on a
One Degree Grid Cell Basis
Prepared by
Antoinette L. Brenkert
Carbon Dioxide
Information Analysis Center
Oak Ridge National Laboratory
Oak Ridge, Tennessee 37831-6335
Date Published: February 1998 (Revised for the Web: 2003)
CONTENTS
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
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 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:
(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
CDIAC's Web site address: http://cdiac.ornl.gov/
For non-FTP data acquisitions (e.g., IBM- or MacIntosh-formatted floppy
diskettes), users may request data from
CDIAC using the following information:
Address: Carbon Dioxide Information Analysis Center
Oak Ridge National Laboratory
P.O. Box 2008
Oak Ridge, Tennessee37831-6335, U.S.A.
Telephone: (865) 574-3645 (Voice)
(865) 574-2232 (Fax)
Electronic mail: cdiac@ornl.gov
(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.