View source for Data listed 2015 and 2100

'''Variable for world'''<br/>This first sheet will include values for the world for all [https://pardee.du.edu/wiki/Variable_names variables ]that were chosen to be compared. This will allow the variables to be sorted and compared easily.

'''Example'''

------------------------

{| border="0" cellpadding="0" cellspacing="0" width="678"
|-
| height="19" width="122" | Variables
| width="64" | region
| width="182" | Unit
| width="182" | Ratio
| align="right" width="64" | 2015
| align="right" width="64" | 2100
|-
| height="19" | WATSAFE
| World
| UnImproved
| IFsNew-X%GDP/IFsbaseold_j
| align="right" | 1
| align="right" | 0.963
|-
| height="19" | GOVREV
| World
| Trillion $
| IFsNew-X%GDP/IFsbaseold_j
| align="right" | 1
| align="right" | 1.005
|-
| height="19" | LIFEXP
| World
| Total
| IFsNew-X%GDP/IFsbaseold_j
| align="right" | 1
| align="right" | 0.9994
|-
| height="19" | EDYRSAG15
| World
| Total
| IFsNew-X%GDP/IFsbaseold_j
| align="right" | 1
| align="right" | 0.9991
|-
| height="19" | EDYRSAG25
| World
| Total
| IFsNew-X%GDP/IFsbaseold_j
| align="right" | 1
| align="right" | 0.9991
|-
| height="19" | ENP
| World
| Total
| IFsNew-X%GDP/IFsbaseold_j
| align="right" | 1
| align="right" | 1.025
|-
| height="19" | INFRAELECACC
| World
| Total
| IFsNew-X%GDP/IFsbaseold_j
| align="right" | 1.006
| align="right" | 1
|}

----





'''Individual variable sheet'''<br/>The following sheets are&nbsp;for individual countries and respective [https://pardee.du.edu/wiki/Variable_names variables]. It includes country, raw data, and ratio for intial year and last year. This allows&nbsp;to compare highest and lowest values and their respective raw data.

'''Example 1 for GDP:'''

------------------------

{| border="0" cellpadding="0" cellspacing="0" width="565"
|-
| height="19" width="129" | 
| width="77" | New base
| width="77" | Old base
| width="77" | New base
| width="77" | Old base
| width="64" | 
| width="64" | 
|-
| height="19" | &nbsp;
| GDP[10]
| GDP[8]
| GDP[10]
| GDP[8]
| 
| 
|-
| height="19" | Country
| 2015 values
| 2015 values
| 2100 values
| 2100 values
| 2015 ratio
| 2100 ratio
|-
| height="19" | Afghanistan
| align="right" | 0.022
| align="right" | 0.022
| align="right" | 1.055
| align="right" | 0.882
| align="right" | 1
| align="right" | 1.196145
|-
| height="19" | Albania
| align="right" | 0.014
| align="right" | 0.014
| align="right" | 0.045
| align="right" | 0.04
| align="right" | 1
| align="right" | 1.125
|-
| height="19" | Algeria
| align="right" | 0.229
| align="right" | 0.229
| align="right" | 1.595
| align="right" | 1.495
| align="right" | 1
| align="right" | 1.06689
|-
| height="19" | Angola
| align="right" | 0.135
| align="right" | 0.135
| align="right" | 5.518
| align="right" | 6.271
| align="right" | 1
| align="right" | 0.879923
|-
| height="19" | Argentina
| align="right" | 0.538
| align="right" | 0.538
| align="right" | 2.472
| align="right" | 2.493
| align="right" | 1
| align="right" | 0.991576
|}

----



'''Example 2 for POP:'''

------------------------

{| border="0" cellpadding="0" cellspacing="0" width="648"
|-
| height="19" width="196" | POP
| width="81" | New base
| width="81" | Old base
| width="81" | New base
| width="81" | Old base
| width="64" | 
| width="64" | 
|-
| height="19" | Row Labels
| align="right" | 2015
| align="right" | 2015
| align="right" | 2100
| align="right" | 2100
| 2015 ratio
| 2100 ratio
|-
| height="19" | Afghanistan
| align="right" | 34.41
| align="right" | 34.41
| align="right" | 110.7
| align="right" | 90.51
| align="right" | 1
| align="right" | 1.223069
|-
| height="19" | Albania
| align="right" | 2.891
| align="right" | 2.891
| align="right" | 1.654
| align="right" | 1.657
| align="right" | 1
| align="right" | 0.998189
|-
| height="19" | Algeria
| align="right" | 39.73
| align="right" | 39.73
| align="right" | 64.63
| align="right" | 61.63
| align="right" | 1
| align="right" | 1.048678
|-
| height="19" | Angola
| align="right" | 27.88
| align="right" | 27.88
| align="right" | 168.6
| align="right" | 153.5
| align="right" | 1
| align="right" | 1.098371
|-
| height="19" | Argentina
| align="right" | 43.08
| align="right" | 43.08
| align="right" | 53.17
| align="right" | 52.91
| align="right" | 1
| align="right" | 1.004914
|-
| height="19" | Armenia
| align="right" | 2.926
| align="right" | 2.926
| align="right" | 2.132
| align="right" | 1.977
| align="right" | 1
| align="right" | 1.078402
|-
| height="19" | Australia
| align="right" | 23.93
| align="right" | 23.93
| align="right" | 39.61
| align="right" | 38.86
| align="right" | 1
| align="right" | 1.0193
|}

----



----

'''Final sheet'''<br/>This final sheet is created by appending all individual variable sheets. This sheet allows to sort values among all variables and countries.<br/>------------------------

{| border="0" cellpadding="0" cellspacing="0" width="670"
|-
| height="19" width="122" | Variable
| width="196" | Country
| width="64" | 
| width="119" | ratio
| width="64" | 2015 ratio
| width="105" | 2100 ratio
|-
| height="19" | INCOMELT310LN
| Slovak Rep
| Mil People
| IFsNew-X%GDP/IFsbaseold_j
| align="right" | 180.2
| align="right" | 3,672
|-
| height="19" | INCOMELT310LN
| Montenegro
| Mil People
| IFsNew-X%GDP/IFsbaseold_j
| align="right" | 149.5
| align="right" | 42,635
|-
| height="19" | INCOMELT310LN
| Poland
| Mil People
| IFsNew-X%GDP/IFsbaseold_j
| align="right" | 114.1
| align="right" | 22,586
|-
| height="19" | INCOMELT310LN
| Hungary
| Mil People
| IFsNew-X%GDP/IFsbaseold_j
| align="right" | 99.91
| align="right" | 2,502
|-
| height="19" | INCOMELT310LN
| Estonia
| Mil People
| IFsNew-X%GDP/IFsbaseold_j
| align="right" | 28.16
| align="right" | 161
|-
| height="19" | INCOMELT310LN
| Serbia
| Mil People
| IFsNew-X%GDP/IFsbaseold_j
| align="right" | 19.4
| align="right" | 51,771
|-
| height="19" | INCOMELT310LN
| Latvia
| Mil People
| IFsNew-X%GDP/IFsbaseold_j
| align="right" | 16.14
| align="right" | 87.06
|-
| height="19" | VADD
| Lesotho
| Materials
| IFsNew-X%GDP/IFsbaseold_j
| align="right" | 10.75
| align="right" | 7.464
|-
| height="19" | VADD
| Turkmenistan
| ICTech
| IFsNew-X%GDP/IFsbaseold_j
| align="right" | 4.554
| align="right" | 6.966
|-
| height="19" | INFRAELECACC
| Sudan South
| Total
| IFsNew-X%GDP/IFsbaseold_j
| align="right" | 4.186
| align="right" | 1.012
|}

----