Data Portal
Confidence shows the level of coni dence that the research team attributes to the indicator, based on a multi-point assessment. Judgements are made in relation to the set of indicators that make up the Monitor assessment only; so, for example, the research team has more coni dence in indicators labelled "Robust" than in indicators labelled "Speculative". Some experts may however consider the robust indicators to still possess inadequate coni dence, or speculative indicators to exceed simple speculation. A 3-point scale is used to evaluate whether each criterion reviewed contributes or detracts from the overall level of coni dence. Confidence shows the level of coni dence that the research team attributes to the indicator, based on a multi-point assessment. Judgements are made in relation to the set of indicators that make up the Monitor assessment only; so, for example, the research team has more coni dence in indicators labelled "Robust" than in indicators labelled "Speculative". Some experts may however consider the robust indicators to still possess inadequate coni dence, or speculative indicators to exceed simple speculation. A 3-point scale is used to evaluate whether each criterion reviewed contributes or detracts from the overall level of coni dence. Severity shows the scale of the overall or absolute global impact of a given indicator and the breadth of effects internationally. "Major" impacts might involve, for example, tens of billions of dollars of economic damage or over 100,000 deaths on average per year. Other indicators estimate 
much lower levels of damage or even positive net impacts, in which case the severity may be assessed as "Minimal"

Affected Groups indicates the specific population segments or communities particularly affected or susceptible to the impacts of a given indicator. The groups may be socially, economically, geographically or otherwise dei ned depending on the impacts under examination. Injustice shows how unjust or not a given effect is on the global scale. It denotes the level of injustice of a specific effect or set of effects as they are assessed by one of the Monitor's indicators. Injustice is highest when the affected countries have least responsibility for climate change and at its lowest when impacts are shared the most among countries with high responsibility. The four-point score is dei ned by statistical quartiles, so the level of injustice is also relative only to the Monitor's Climate section indicators themselves. Responsibility for climate change is based on total country GHG emissions from 1990-2005 (Mueller et al, 2007). Priority shows the amount of support a specific effect area has received through international climate funding. It denotes the level of priority that the effect or set of effects assessed by one Monitor indicator has, as rel ected in international climate i nance expenditures for adaptation."High priority" denotes higher levels of funding from developed countries, targeting the issue in affected developing countries."Low" or"no priority" is given to concerns for which i nancial support has been marginal or virtually absent. The OECD Creditor Reporting System sub-sector l ows for 2010 have been used as the basis for the analysis (OECD CRS, 2012). High priority Low priority Not a priority The Millennium Development Goals (MDGs) represent the international community's eight primary objectives for poverty reduction to be achieved by 2015. The MDG Effect indicates an impact for specific MDGs. One of the eight goals relates to an international partnership for development and is not relevant to the Monitor's impact analysis. Any of the other seven goals are highlighted whenever an indicator assesses a Climate or Carbon effect that is understood to specifically undermine one or another of these goals. (For more information on the MDGs visit: www.un.org/millenniumgoals) Millenium Development Goal (MDG): End Poverty and Hunger Millenium Development Goal (MDG): Achieve Universal Primary Education Millenium Development Goal (MDG): Promote Gender Equality Millenium Development Goal (MDG): Reduce Child Mortality Millenium Development Goal (MDG): Improve Maternal Health Millenium Development Goal (MDG): Combat HIV/AIDS & Infectious Diseases Millenium Development Goal (MDG): Ensure Environmental Sustainability
Severity shows the scale of the overall or absolute global impact of a given indicator and the breadth of effects internationally. "Major" impacts might involve, for example, tens of billions of dollars of economic damage or over 100,000 deaths on average per year. Other indicators estimate 
much lower levels of damage or even positive net impacts, in which case the severity may be assessed as "Minimal"

Affected Groups indicates the specific population segments or communities particularly affected or susceptible to the impacts of a given indicator. The groups may be socially, economically, geographically or otherwise dei ned depending on the impacts under examination. The Millennium Development Goals (MDGs) represent the international community's eight primary objectives for poverty reduction to be achieved by 2015. The MDG Effect indicates an impact for specific MDGs. One of the eight goals relates to an international partnership for development and is not relevant to the Monitor's impact analysis. Any of the other seven goals are highlighted whenever an indicator assesses a Climate or Carbon effect that is understood to specifically undermine one or another of these goals. (For more information on the MDGs visit: www.un.org/millenniumgoals)
ADDITIONAL MORTALITY ADDITIONAL AFFECTED POPULATION (1000s)
 COUNTRY     2010 2030 2010 2030
Afghanistan 75- 5015000
Albania 5-3- 102500
Algeria 77 205000
Angola 75- 309500
Antigua and Barbuda 75- 0
Argentina 3-0+ 25060001
Armenia 88
Australia 0+0+ 5001,25026
Austria 0+0+ 10025001
Azerbaijan 65- 103000
Bahamas 5-4+ 1100
Bahrain 66 100
Bangladesh 66 8520000
Barbados 65- 110
Belarus 3-0+ 7015000
Belgium 2+0+ 10020001
Belize 5-4+ 1100
Benin 66 102500
Bhutan 2+0+ 52000
Bolivia 5-4+ 257000
Bosnia and Herzegovina 3-0+ 306000
Botswana 66 1500
Brazil 5-4+ 6001,50012
Brunei 66 10
Bulgaria 0+0+ 9515000
Burkina Faso 66 103500
Burundi 65- 102500
Cambodia 5-4+ 308000
Cameroon 65- 307500
Canada 2+0+ 30070013
Cape Verde 88
Central African Republic 65- 51500
Chad 77 103500
Chile 4+1- 9520000
China 75- 2,0004,25037
Colombia 5-4+ 10025000
Comoros 88
Congo 65- 51500
Costa Rica 4+2+ 205000
Cote d'Ivoire 66 204500
Croatia 0+0+ 7015000
Cuba 2+0+ 10020000
Cyprus 5-4+ 5500
Czech Republic 0+0+ 15025000
Denmark 0+0+ 8015000
Djibouti 5-3- 51000
Dominica 75-
Dominican Republic 77 51500
DR Congo 75- 10035000
Ecuador 5-4+ 4010000
Egypt 66 4510000
El Salvador 3-0+ 4010000
Equatorial Guinea 66 1100
Eritrea 65- 103000
Estonia 0+0+ 203500
Ethiopia 5-3- 30085001
Fiji 3-0+ 51500
Finland 1-0+ 6015000
France 1-0+ 7501,50037
Gabon 65- 1500
Gambia 77 1100
Georgia 3-0+ 305000
Germany 1-0+ 8501,75048
Ghana 66 256000
Greece 2+0+ 10020001
Grenada 66
Guatemala 5-4+ 4010000
Guinea 66 52000
Guinea-Bissau 65- 1500
Guyana 75- 1100
Haiti 77 110
Honduras 5-3- 257000
Hungary 0+0+ 15025000
Iceland 1-0+ 51000
India 66 40090001
Indonesia 65- 40090001
Iran 75- 15035000
Iraq 77 206000
Ireland 0+0+ 5515000
Israel 1-0+ 8520001
Italy 1-0+ 6501,25035
Jamaica 65- 51000
Japan 5-4+ 40075013
Jordan 65- 103000
Kazakhstan 5-3- 5010000
Kenya 75- 5015000
Kiribati 99
Kuwait 77 1100
Kyrgyzstan 75- 51500
Laos 5-4+ 153500
Latvia 0+0+ 356500
Lebanon 1-0+ 5010000
Lesotho 66 1100
Liberia 66 11000
Libya 66 11000
Lithuania 2+0+ 306500
Luxembourg 3-0+ 51000
Macedonia 0+0+ 357000
Madagascar 66 103500
Malawi 65- 205500
Malaysia 75- 409500
Maldives 75- 1100
Mali 77 52000
Malta 3-0+ 1500
Marshall Islands 5-4+ 1
Mauritania 66 51000
Mauritius 66 1100
Mexico 5-3- 40095001
Micronesia 5-4+ 10
Moldova 2+0+ 357000
Mongolia 65- 51000
Morocco 77 154000
Mozambique 77 256000
Myanmar 75- 7515000
Namibia 75- 51000
Nepal 77 153500
Netherlands 0+0+ 25050012
New Zealand 0+0+ 10025001
Nicaragua 65- 102000
Niger 77 104000
Nigeria 75- 20055000
North Korea 75- 459000
Norway 0+0+ 10020001
Oman 65- 51500
Pakistan 77 9025000
Palau 5-4+
Panama 65- 51000
Papua New Guinea 1-0+ 7520000
Paraguay 5-4+ 154500
Peru 5-4+ 7520000
Philippines 65- 20045000
Poland 0+0+ 5001,00001
Portugal 1-0+ 10025001
Qatar 88
Romania 2+0+ 20040000
Russia 4+1- 8501,50013
Rwanda 75- 154500
Saint Lucia 75- 1
Saint Vincent 5-3- 110
Samoa 99
Sao Tome and Principe 5-4+ 110
Saudi Arabia 66 154500
Senegal 66 52000
Seychelles 66
Sierra Leone 66 51000
Singapore 5-4+ 102500
Slovakia 1-0+ 5510000
Slovenia 0+0+ 357000
Solomon Islands 65- 1500
Somalia 4+2+ 4015000
South Africa 3-0+ 35065001
South Korea 5-4+ 10025001
Spain 2+0+ 40075023
Sri Lanka 66 204500
Sudan-South Sudan 66 4510000
Suriname 66 10
Swaziland 66 1100
Sweden 0+0+ 15035001
Switzerland 0+0+ 10020001
Syria 66 154000
Tajikistan 66 51000
Tanzania 66 154000
Thailand 5-4+ 15035000
Timor-Leste 66 1100
Togo 65- 102500
Tonga 5-3- 10
Trinidad and Tobago 75- 1500
Tunisia 75- 153500
Turkey 75- 10025000
Turkmenistan 75- 51500
Tuvalu 5-3-
Uganda 75- 4515000
Ukraine 3-0+ 30060001
United Arab Emirates 88
United Kingdom 0+0+ 8001,75038
United States 1-0+ 3,5008,0001540
Uruguay 3-0+ 256000
Uzbekistan 66 256500
Vanuatu 88
Venezuela 5-3- 10025000
Vietnam 5-4+ 25060000
Yemen 66 155500
Zambia 65- 205500
Zimbabwe 75- 205500
TOTAL20,35345,12363,499144,380