AR5 Scenario Database

Introduction

This scenarios database documents the long-term scenarios reviewed in the Fifth Assessment Report (AR5) of Working Group III of the Intergovernmental Panel on Climate Change (IPCC). It comprises 31 models (sometimes in different versions) and in total 1,184 scenarios. In an attempt to be as inclusive as possible, an open call for scenarios was made through the Integrated Assessment Modeling Consortium (IAMC) with approval from the IPCC WGIII Technical Support Unit. To be included in the database, four criteria had to be met. First, only scenarios published in the peer-reviewed literature could be considered, per IPCC protocol. Second, the scenario must contain a minimum set of required variables and some basic model and scenario documentation (meta data) must be provided. Third, only models with at least full energy system representation were considered given that specific sectoral studies were assessed in Chapters 8-11 of the report. Lastly, the scenario must provide data out to at least 2030. Scenarios were submitted by entering the data into a standardized data template that was subsequently uploaded to this database system administered by the International Institute of Applied System Analysis (IIASA). The AR5 scenario database is documented in Annex II, Section A.II.10, of the 5th Assessment Report of Working Group III. For the purpose of the assessment, scenarios have been grouped in various categories relating to, among other things, radiative forcing outcome in 2100, overshoot, technology availability and policy assumptions (see Annex A.II.10.3 for a detailed description). The classification of individual scenarios can be downloaded here.

Model comparison projects

A majority of scenarios was provided by model comparison projects which have published their scenario data in project specific databases. These project scenario databases can hold more information than is provided here. Scenario data from this site may be freely used for non-commercial and educational purposes with proper acknowledgment of the data sources. The data source is specified in the scenario name (i.e. the project name in most cases). Users are requested to ensure proper acknowledgment of the data sources of the scenarios they use as detailed below. Table A.II.15 from Annex II below provides information about the model comparison projects that are represented in the AR5 scenario database. In particular, it lists the number of models and scenarios included in the database, the areas in which models were harmonized, the leading institutions and references to the overview publications.

Table A.II.15. Model inter-comparison exercises generating transformation pathway scenarios included in AR5 database.

Model Intercomparison Exercise	Year Completed	Number of Models in AR5 scenario database	Number of Scenarios in AR5 scenario database	Areas of Harmonization	Lead Institution	Overview Publication
ADAM (Adaptation and Mitigation Strategies—Supporting European Climate Policy)	2009	1	15	Technology availability, Mitigation policy	Potsdam Institute for Climate Impact Research (PIK)	Edenhofer et al., 2010
AME (Asian Modeling Exercise)	2012	16	83	Mitigation policy	Pacific Northwest National Laboratories (PNNL)	Calvin et al., 2012
AMPERE (Assessment of Climate Change Mitigation Pathways and Evaluation of the Robustness of Mitigation Cost Estimates)	2013	11	378	Technology availability; mitigation policy; GDP; population	Potsdam Institute for Climate Impact Research (PIK)	AMPERE2: Riahi et al., 2014 AMPERE3: Kriegler et al.,2014a
EMF 22 (Energy Modeling Forum 22)	2009	7	70	Technology availability, mitigation policy	Stanford University	Clarke et al., 2009
EMF 27 (Energy Modeling Forum 27)	2013	16	362	Technology availability, mitigation policy	Stanford University	Blanford et al., 2014; Krey et al., 2014; Kriegler et al., 2014c
LIMITS (Low Climate Impact Scenarios and the Implications of required tight emissions control strategies)	2014	7	84	Mitigation policies	Fondazione Eni Enrico Mattei (FEEM)	Kriegler et al., 2014b; Tavoni et al., 2014
POeM (Policy Options to engage Emerging Asian economies in a post-Kyoto regime)	2012	1	4	Mitigation policies	Chalmers University of Technology	Lucas et al., 2013
RECIPE (Report on Energy and Climate Policy in Europe)	2009	2	18	Mitigation policies	Potsdam Institute for Climate Impact Research (PIK)	Luderer et al., 2012a
RoSE (Roadmaps towards Sustainable Energy futures)	2013	3	105	Mitigation policy; GDP growth; population growth, fossil fuel availability	Potsdam Institute for Climate Impact Research (PIK)	Bauer et al., 2013; De Cian et al., 2013b; Calvin et al., 2014; Chen et al., 2014;Luderer et al., 2014

More detailed information on models, scenarios and references...

Model characteristics and references to scenarios

Table A.II.14 from Annex II provides more detailed information about the models and their key features represented in the database and the publications that document individual scenarios in the database.

Table A.II.14. Contributing models to the AR5 Scenario Database.

Model (versions)	Economic coverage and feedback	Myopic/ Foresight	Regional and emissions detail	Representation of climate and land use	Cost measures	Scenario Publications	Number of Scenarios included in AR5 data base
AIM-Enduse (12.1; backcast 1.0)	Partial equilibrium	Myopic	32 regions; 5 substances (v. 12.1)/8 substances (v. backcast 1.0)	None	Energy system cost mark-up (v 12.1; backcast 1.0)/area under marginal abatement cost curve (backcast 1.0)	Akashi et al., 2014;Kriegler et al., 2014b; Tavoni et al., 2014	41
BET (1.5)	General equilibrium	Foresight	32 regions; CO₂only	Climate damages; no land use	Consumption loss, GDP loss, energy system cost mark-up	Yamamoto et al., 2014	23
DNE21+ (v.11, v.12)	Partial equilibrium	Foresight	54 regions; 6 substances (v.11)/13 substances (v.12)	Temperature change; no land use	Energy system cost mark-up	Akimoto et al., 2012; Wada et al., 2012; Kriegler et al., 2014a; Riahi et al., 2014;Sano et al., 2014	43
EC-IAM 2012	General equilibrium	Foresight	11 regions; 6 substances	Climate damages; no land use	Consumption loss, GDP loss, energy system cost mark-up, welfare loss	Kriegler et al., 2014c	21
Ecofys Energy Model	Partial equilibrium	Myopic	1 region; 3 substances	No climate; land use for bioenergy	Energy system cost mark-up	Deng et al., 2012	1
ENV-Linkages (WEO2012)	General equilibrium	Myopic	15 regions; 6 substances	No climate; land use for food consumption	Consumption loss, GDP loss, equivalent variation, welfare loss	Kriegler et al., 2014c	17
FARM (3.0)	General equilibrium	Myopic	15 regions; CO₂ only	No climate; land use by land type for bioenergy and food consumption	Consumption loss, GDP loss, equivalent variation, welfare loss	Sands et al., 2014	12
GCAM (2.0, 3.0, 3.1, MiniCAM)	Partial equilibrium	Myopic	14 regions; 13 substances	Temperature change; Land use by land type for bioenergy and food consumption	Area under marginal abatement cost curve	Calvin, Edmonds, et al., 2009;Calvin et al., 2012, 2013, 2014; Iyer et al., 2013; Kriegler, Tavoni, et al., 2014 ; Tavoni et al., 2014	139
GEM-E3-ICCS	General equilibrium	Myopic	37 regions; 11 substances	No climate; land use for food consumption	Consumption loss, GDP loss, equivalent variation	Kriegler et al., 2014a	11
GRAPE (ver1998, ver2011)	General equilibrium	Foresight	15 regions; 5 substances	Temperature change; land use by land type for food consumption	Consumption loss, GDP loss	Calvin et al., 2012; Kriegler et al., 2014c	14
GTEM REF32	General equilibrium	Myopic	13 regions; 5 substances	No climate; land use for food consumption and crop prices	Consumption loss, GDP loss, welfare loss	Mi et al., 2012	4
IEEJ (ver.2011)	Econometric	Foresight	43 regions; CO₂ only	Temperature change; no land use	Energy system cost mark-up	Matsuo et al.	2
IGSM	General equilibrium	Myopic	16 regions; 12 substances	Climate damages; land use by land type for bioenergy, food consumption and crop prices	Consumption loss, GDP loss, equivalent variation, welfare loss; area under marginal abatement cost curve; energy system cost mark-up	Prinn et al., 2011	5
IMACLIM (v1.1)	General equilibrium	Myopic	12 regions; CO₂ only	Temperature change; no land use	Welfare loss, GDP loss, consumption loss, equivalent variation	Bibas and Méjean, 2013; Kriegler et al., 2014a; Riahi et al., 2014	53
IMAGE (2.4)	Partial equilibrium	Myopic	26 regions; 13 substances	Temperature change; land use by land type for bioenergy and food consumption	Area under marginal abatement cost curve	van Vliet et al., 2009, 2014; van Ruijven et al., 2012; Lucas et al., 2013; Kriegler et al., 2014a; b;Riahi et al., 2014; Tavoni et al., 2014	79
iPETS (1.2.0)	General equilibrium	Foresight	9 regions; CO₂ only	Land use for food consumption	Consumption loss, GDP loss, welfare loss	O’Neill et al., 2012	4
KEI-Linkages	General equilibrium	Myopic	13 regions; CO₂ only	No climate; land use for food consumption and crop prices	Consumption loss, equivalent variation	Lim and Kim, 2012	4
MARIA23_org	General equilibrium	Foresight	23 regions; 6 substances	Temperature change and climate damage; land use by land type for bioenergy and food consumption	Welfare loss, GDP loss, consumption loss, GDP loss, energy system cost mark-up	Mori, 2012	5
MERGE (AME, EMF22, EMF27)	General equilibrium	Foresight	9 (AME)/8 (EMF22) regions; 7 (AME,EMF22) /12 (EMF27) substances	Climate damages; no land use	Consumption loss, GDP loss, welfare loss	Blanford et al., 2009, 2013; Calvin et al., 2012	44
MERGE-ETL (2011)	General equilibrium	Foresight	9 regions; 5 substances	Temperature change; no land use	Consumption loss, GDP loss, welfare loss	Marcucci and Turton, 2013; Kriegler et al., 2014a;Riahi et al., 2014	48
MESSAGE (V.1, V.2, V.3, V.4)	General equilibrium	Foresight	11 regions; 10 (V.1)/13 (V.2, V.3, V.4) substances	Temperature change; land use by land type for bioenergy (all versions)	GDP loss, energy system cost mark-up (all versions); area under marginal abatement cost curve (V.1, V.3, V.4); consumption loss (V.3, V.4)	Krey and Riahi, 2009; Riahi et al., 2011, 2012, 2014; van Vliet et al., 2012; Kriegler et al., 2014a; b; McCollum et al., 2014; Tavoni et al., 2014	140
Phoenix (2012.4)	General equilibrium	Myopic	24 regions; CO₂ only	Radiative forcing; land as factor of production in agriculture and forestry (including feedstocks for biofuels)	Welfare loss, GDP loss, consumption loss, equivalent variation	Fisher-Vanden et al., 2012;Kriegler et al., 2014c	31
POLES (AMPERE, EMF27, AME)	Partial equilibrium/ econometric	Myopic	57 regions (AMPERE, EMF27)/47 regions (AME); 6 substances	No climate; land use by land type for bioenergy (AMPERE, AME)	Area under marginal abatement cost curve	Dowling and Russ, 2012;Griffin et al., 2014; Kriegler et al., 2014a; Riahi et al., 2014	79
REMIND (1.1, 1.2, 1.3, 1.4, 1.5)	General equilibrium	Foresight	11 regions; CO₂ only (1.1, 1.2)/4 substances (1.3)/ 6 substances (1.4)/6–9 substances (1.5)	Temperature change; land use emissions via MAC (1.2, 1.3, 1.4) and from a land use model (MAgPIE; 1.5)	Consumption loss, GDP loss, welfare loss	Leimbach et al., 2010; Luderer et al., 2012a; b; Arroyo-Currás et al., 2013;Bauer et al., 2013;Aboumahboub et al., 2014; Tavoni et al., 2014;Klein et al., 2014; Kriegler et al., 2014a; b;Riahi et al., 2014	158
SGM	General equilibrium	Myopic	8 regions; CO₂ only	None	Consumption loss, GDP loss, equivalent variation, area under marginal abatement cost curve	Calvin et al., 2009b	7
TIAM-ECN	Partial equilibrium	Foresight	15 regions; 3 Substances	Radiative forcing; no land use	Energy cost increase; energy system cost mark-up	Kober et al., 2014;Kriegler et al., 2014b; Tavoni et al., 2014	12
TIAM-World (2007, 2012.02, Mar2012)	Partial equilibrium	Foresight	16 regions; 3 Substances	Temperature change; land use for bioenergy	Area under marginal abatement cost curve (all versions); welfare loss (2012.02); energy system cost mark-ups (2007, Mar2012)	Loulou et al., 2009;Labriet et al., 2012;Kanudia et al., 2013	41
TIMES-VTT	Partial equilibrium	Foresight	17 regions; 6 Substances	Temperature change; no land use	Consumption loss, energy system cost mark-ups	Koljonen and Lehtilä, 2012	6
WITCH (AME, AMPERE, EMF22, EMF27, LIMITS, RECIPE, ROSE)	General equilibrium	Foresight	13 regions/ 12 regions (RECIPE); 6 Substances	Temperature change (AME, AMPERE); climate damages (EMF22, EMF27; no land use	Consumption loss, GDP loss, welfare loss, energy system cost mark-ups	Bosetti et al., 2009; de Cian et al., 2012; Massetti and Tavoni, 2012; De Cian et al., 2013a; Kriegler et al., 2014a; b; Marangoni and Tavoni, 2013; Riahi et al., 2013; Tavoni et al., 2013	132
WorldScan2	General equilibrium	Myopic	5 regions; 8 Substances	No climate; land use for food consumption	Welfare loss, GDP loss, equivalent variation	Kriegler et al., 2014a	8

Database Tutorial

A short tutorial on the use of the web database can be found below. If you experience technical problems with this database, please contact the AR5 Database Administrator.

The Navigation tabs

At the upper end of the browser window five navigation tabs can be found that provide different functionality of the web database. These six tabs are described in more detail in the following section.

About tab

The About tab provides information about the database and gives instructions on how to use the database. The Regions, Sectors and Series tabs all allow to view the scenarios in the database. The difference between these three tabs for viewing is the way how scenario data can be combined for viewing.

Regions tab

The Regions tab allows selecting a single variable from a single scenario (e.g. total GDP in Market Exchange Rate for a baseline scenario from a specific model) in order to compare this selection across different regions. For variables that can be added in a meaningful way (e.g. GDP, total primary energy consumption) the graph that appears on the right hand side will be a stacked are graph while for variables that are not additive (e.g. price information) a line graph will be displayed.

Sectors tab

The Sectors tab allows selecting multiple variables from a single scenario and region. This view is most useful for displaying a set of variables from one sector, for example, all fuel types of industrial final energy consumption. Again, if the variables can be added in a meaningful way (e.g. different fuel types of one sector) a stacked area graph is shown; if this is not possible (e.g. for different fuel prices) a line graph is displayed. In case variables with different units are selected a warning is issued on the y-axis label of the graph in red. Please note that it is necessary to mark a variable name (so that it appears highlighted in blue) in addition to selecting variables for the graph on the right hand side to be updated (see also under (3.) Variables below).

Series tab

The Series tab allows selecting a single variable from multiple scenarios and regions. The preview graph on the right is always a line graph and is most useful to compare trends across different scenarios (and models) in one or multiple regions.

Common Features of the Regions, Sectors and Series tabs

In all three view tabs the following selections can be made in the navigation bars on the upper left-hand side of the browser window:

(1.) Regions: In the upper left area of the screen is a field named Regions. Depending on the tab (see above) you may select one or multiple regions for which the data is shown on the screen. The regions are organized in the two folders, the World and RC5 Regions.

(2.) Scenarios: This field includes the list of scenarios from which one or more scenarios can be selected. In addition to scenarios, for a number of variables historical and base year data can be shown to compare with scenario results.

(3.) Variables: In this field the variables can be selected for which the data is shown on the screen. Note that in the Sectors tab it is necessary to not only required to tick one or multiple variables for selection, but also to mark a variable name (so that is appears highlighted in blue) for the graph on the right hand side to be updated. It is not important which variable or variable category is marked to initiate the graph update. The definition of variables can be found in the standardized data template.

The Chart Preview on the upper right-hand side of the browser window shows the graph of the selected data (variable + scenarios + regions). In addition, the horizontally oriented Query Results area in the middle of the screen shows the data in tabular format.

It is possible to export the data either into Excel or two different graphical formats (PNG = portable network graphics, SVG = scalable vector graphics). In order to do so, select one of the options in the Output Options window at the bottom of the browser window. The field titled Notes shows additional information or explanatory text for the selected variables. The availability of notes is still under development and ultimate the contents will depend on input from modeling teams.

Download tab

The Download tab offers the possibility to download the entire content of the AR5 Scenario Database in one file, a zip archive that contains the data and comments provided by modeling teams about specific models, scenarios, variablesin or regions in csv format. To download the entire database it is necessary to enter your name and e-mail address (see privacy policy). In case of updates (e.g., error corrections in the underlying database) we will inform you by sending an e-mail to the address that you supply above.

Region definitions

The consolitated results in the database are shown at regional aggregations of the World and five regions (based on the definitions from the Representative Concentration Pathways, RCPs. These regions are defined as follows:

Aggregation on the five region level (see Annex II, Section A.II.2). It should be noted that these regions were also used in the so-called Representative Concentration Pathways (RCPs).

OECD1990 = This region includes the OECD countries in 1990.
Aland Islands, Andorra, Australia, Austria, Belgium, Canada, Channel Islands, Denmark, Faroe Islands, Finland, France, Germany, Gibraltar, Greece, Greenland, Guam, Guernsey, Holy See (Vatican City State), Iceland, Ireland, Isle of Man, Italy, Japan, Jersey, Liechtenstein, Luxembourg, Monaco, Netherlands, New Zealand, Norway, Portugal, Saint Pierre and Miquelon, San Marino, Spain, Svalbard and Jan Mayen, Sweden, Switzerland, Turkey, United Kingdom, United States

EIT = Economies in Transition. This region is sometimes also referred to as Reforming Ecomonies of Eastern Europe and the Former Soviet Union (REF).
Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Czech Republic, Estonia, Georgia, Hungary, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Macedonia, Malta, Moldova (Republic of), Montenegro, Poland, Romania, Russian Federation, Serbia, Serbia and Montenegro, Slovakia, Slovenia, Tajikistan, Turkmenistan, Ukraine, Uzbekistan,

ASIA = Non-OECD ASIA. The region includes most Asian countries with the exception of the Middle East, Japan and Former Soviet Union states.
Afghanistan, American Samoa, Bangladesh, Bhutan, British Indian Ocean Territory, Brunei Darussalam, Cambodia, China, Christmas Island, Cocos (Keeling) Islands, Cook Islands, Fiji, French Polynesia, Heard Island and McDonald Islands, India, Indonesia, Kiribati, Korea (Democratic People's Republic of), Laos (People's Democratic Republic), Malaysia, Maldives, Marshall Islands, Micronesia (Federated States of), Mongolia, Myanmar, Nauru, Nepal, New Caledonia, Niue, Norfolk Island, Northern Mariana Islands, Pakistan, Palau, Papua New Guinea, Philippines, Pitcairn, Samoa, Singapore, Solomon Islands, South Korea, Sri Lanka, Thailand, Timor-Leste, Tokelau, Tonga, Tuvalu, US Minor Outlying Islands, Vanuatu, Viet Nam, Wallis and Futuna

MAF = This region includes the countries of the Middle East and Africa.
Algeria, Angola, Bahrain, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Cape Verde, Central African Republic, Chad, Comoros, Congo, Congo (The Democratic Republic of the), Cote d'Ivoire, Djibouti, Egypt, Equatorial Guinea, Eritrea, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Iran, Iraq, Israel, Jordan, Kenya, Kuwait, Lebanon, Lesotho, Liberia, Libya, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mayotte, Morocco, Mozambique, Namibia, Niger, Nigeria, Oman, Palestinian Territory, Qatar, Reunion, Rwanda, Saint Helena, Sao Tome and Principe, Saudi Arabia, Senegal, Seychelles, Sierra Leone, Somalia, South Africa, South Sudan, Sudan, Swaziland, Syrian Arab Republic, Tanzania, Togo, Tunisia, Uganda, United Arab Emirates, Western Sahara, Yemen, Zambia, Zimbabwe

LAM = This region includes the countries of Latin America and the Caribbean.
Anguilla, Antarctica, Antigua and Barbuda, Argentina, Aruba, Bahamas, Barbados, Belize, Bermuda, Bolivia, Bouvet Island, Brazil, British Virgin Islands, Cayman Islands, Chile, Colombia, Costa Rica, Cuba, Curacao, Dominica, Dominican Republic, Ecuador, El Salvador, Falkland Islands (Malvinas), French Guiana, French Southern Territories, Grenada, Guadeloupe, Guatemala, Guyana, Haiti, Honduras, Jamaica, Martinique, Mexico, Montserrat, Netherlands Antilles, Nicaragua, Panama, Paraguay, Peru, Puerto Rico, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Sint Maarten, South Georgia and the South Sandwich Islands, Suriname, Trinidad and Tobago, Turks and Caicos Islands, Uruguay, US Virgin Islands, Venezuela

References

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Klein D., G. Luderer, E. Kriegler, J. Strefler, N. Bauer, M. Leimbach, A. Popp, J.P. Dietrich, F. Humpenöder, H. Lotze-Campen, and O. Edenhofer (2014). The value of bioenergy in low stabilization scenarios: an assessment using REMIND-MAgPIE. Climatic Change, 123(3-4) 705-718.

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Kriegler E., J. Weyant, G. Blanford, L. Clarke, J. Edmonds, A. Fawcett, V. Krey, G. Luderer, K. Riahi, R. Richels, S. Rose, M. Tavoni, and D. van Vuuren (2014). The Role of Technology for Climate Stabilization: Overview of the EMF 27 Study on Energy System Transition Pathways Under Alternative Climate Policy Regimes. Accepted for publication in Climatic Change In press.

Labriet M., A. Kanudia, and R. Loulou (2012). Climate mitigation under an uncertain technology future: A TIAM-World analysis. The Asia Modeling Exercise: Exploring the Role of Asia in Mitigating Climate Change 34, Supplement 3, S366-S377.

Leimbach M., N. Bauer, L. Baumstark, M. Lüken, and O. Edenhofer (2010). Technological change and international trade - Insights from REMIND-R. Energy Journal 31, 109-136.

Lim J.-S., and Y.-G. Kim (2012). Combining carbon tax and R&D subsidy for climate change mitigation. The Asia Modeling Exercise: Exploring the Role of Asia in Mitigating Climate Change 34, Supplement 3, S496-S502.

Loulou R., M. Labriet, and A. Kanudia (2009). Deterministic and stochastic analysis of alternative climate targets under differentiated cooperation regimes. International, U.S. and E.U. Climate Change Control Scenarios: Results from EMF 22 31, Supplement 2, S131-S143.

Lucas P.L., P.R. Shukla, W. Chen, B.J. van Ruijven, S. Dhar, M.G.J. den Elzen, and D.P. van Vuuren (2013). Implications of the international reduction pledges on long-term energy system changes and costs in China and India. Energy Policy 63, 1032-1041.

Luderer G., C. Bertram, K. Calvin, E. De Cian, and E. Kriegler (2014). Implications of weak near-term climate policies on long-term mitigation pathways. Climatic Change In Press.

Luderer G., V. Bosetti, M. Jakob, M. Leimbach, J. Steckel, H. Waisman, and O. Edenhofer (2012). The economics of decarbonizing the energy system-results and insights from the RECIPE model intercomparison. Climatic Change 114, 9-37.

Luderer G., R.C. Pietzcker, E. Kriegler, M. Haller, and N. Bauer (2012). Asia's role in mitigating climate change: A technology and sector specific analysis with ReMIND-R. The Asia Modeling Exercise: Exploring the Role of Asia in Mitigating Climate Change 34, Supplement 3, S378-S390

Marangoni G., and M. Tavoni (2014).. Climate Change Economics In Press.

Marcucci A., and H. Turton (2013). Induced technological change in moderate and fragmented climate change mitigation regimes. Technological Forecasting and Social Change, In Press

Massetti E., and M. Tavoni (2012). A developing Asia emission trading scheme (Asia ETS). The Asia Modeling Exercise: Exploring the Role of Asia in Mitigating Climate Change 34, Supplement 3, S436-S443.

Matsuo Y., R. Komiyama, Y. Nagatomi, S. Suehiro, Z. Shen, Y. Morita, and K. Ito Energy Supply and Demand Analysis for Asia and the World towards Low-Carbon Society in 2050. J. Jpn. Soc. Energy and Resources 32, 1-8.

McCollum D.L., V. Krey, P. Kolp, Y. Nagai, and K. Riahi (2014). Transport electrification: a key element for energy system transformation and climate stabilization. Climatic Change In Press.

Mi R., H. Ahammad, N. Hitchins, and E. Heyhoe (2012). Development and deployment of clean electricity technologies in Asia: A multi-scenario analysis using GTEM. The Asia Modeling Exercise: Exploring the Role of Asia in Mitigating Climate Change 34, Supplement 3, S399-S409.

Mori S. (2012). An assessment of the potentials of nuclear power and carbon capture and storage in the long-term global warming mitigation options based on Asian Modeling Exercise scenarios. The Asia Modeling Exercise: Exploring the Role of Asia in Mitigating Climate Change 34, Supplement 3, S421-S428.

O'Neill B.C., X. Ren, L. Jiang, and M. Dalton (2012). The effect of urbanization on energy use in India and China in the iPETS model. The Asia Modeling Exercise: Exploring the Role of Asia in Mitigating Climate Change 34, Supplement 3, S339-S345.

Prinn R., S. Paltsev, A. Sokolov, M. Sarofim, J. Reilly, and H. Jacoby (2011). Scenarios with MIT integrated global systems model: Significant global warming regardless of different approaches. Climatic Change, 104 (3-4) 515-537.

Riahi K., F. Dentener, D. Gielen, A. Grubler, J. Jewell, Z. Klimont, V. Krey, D. McCollum, S. Pachauri, S. Rao, B. van Ruijven, D.P. van Vuuren, and C. Wilson (2012). Chapter 17 - Energy Pathways for Sustainable Development. In: Global Energy Assessment - Toward a Sustainable Future.Cambridge University Press, Cambridge, UK and New York, NY, USA and the International Institute for Applied Systems Analysis, Laxenburg, Austria pp.1203-1306, (ISBN: 9781 10700 5198 hardback 9780 52118 2935 paperback).

Riahi K., E. Kriegler, N. Johnson, C. Bertram, M. Den Elzen, J. Eom, M. Schaeffer, J. Edmonds, and et al. (2014). Locked into Copenhagen Pledges - Implications of short-term emission targets for the cost and feasibility of long-term climate goals. Accepted for publication in Technological Forecasting and Social Change.

Riahi K., S. Rao, V. Krey, C. Cho, V. Chirkov, G. Fischer, G. Kindermann, N. Nakicenovic, and P. Rafaj (2011). RCP 8.5 - A scenario of comparatively high greenhouse gas emissions. Climatic Change 109, 33-57.

Van Ruijven B.J., D.P. van Vuuren, J. van Vliet, A. Mendoza Beltran, S. Deetman, and M.G.J. den Elzen (2012). Implications of greenhouse gas emission mitigation scenarios for the main Asian regions. The Asia Modeling Exercise: Exploring the Role of Asia in Mitigating Climate Change 34, Supplement 3, S459-S469.

Sands R., H. Förster, C. Jones, and K. Schumacher (2014). Bio-electricity and land use in the Future Agricultural Resources Model (FARM). Climatic Change In Press.

Sano F., K. Wada, K. Akimoto, and J. Oda (2014). Assessments of GHG emission reduction scenarios of different levels and different short-term pledges through macro- and sectoral decomposition analyses. Technological Forecasting and Social Change, In Press.

Tavoni M., E. Kriegler, T. Aboumahboub, K. Calvin, G. DeMaere, T. Kober, J. Jewell, P. Lucas, G. Luderer, D. McCollum, G. Marangoni, K. Calvin, R. Pietzcker, J. van Vliet, and B. van der Zwaan (2013). The distribution of the major economies' effort in the Durban platform scenarios. Climate Change Economics, 4(4) 1340008.

Van Vliet J., M.G.J. den Elzen, and D.P. van Vuuren (2009). Meeting radiative forcing targets under delayed participation. International, U.S. and E.U. Climate Change Control Scenarios: Results from EMF 22 31, Supplement 2, S152-S162.

Van Vliet J., A. Hof, A. Mendoza Beltran, M. van den Berg, S. Deetman, M.G.J. den Elzen, P. Lucas, and D.P. van Vuuren (2014). The impact of technology availability on the timing and costs of emission reductions for achieving long-term climate targets. Climatic Change In Press.

Van Vliet O., V. Krey, D. McCollum, S. Pachauri, Y. Nagai, S. Rao, and K. Riahi (2012). Synergies in the Asian energy system: Climate change, energy security, energy access and air pollution. The Asia Modeling Exercise: Exploring the Role of Asia in Mitigating Climate Change 34, Supplement 3, S470-S480.

Wada K., F. Sano, K. Akimoto, and T. Homma (2012). Assessment of Copenhagen pledges with long-term implications. Energy Economics 34, S481-S486.

Yamamoto H., M. Sugiyama, and J. Tsutsui (2014). Role of end-use technologies in long-term GHG reduction scenarios developed with the BET model. Climatic Change In Press.

Updates

The table below contains a list of the different versions of the AR5 Scenario Database and which updates have been released since its publication. Users that have registered and downloaded the full data set via the Download tab, will be notified via e-mail in case of future updates.

Version	Date	Change	Implications for WGIII AR5
1.0	05/13/2014	AR5 Scenario Database made public	-
1.0.1	06/11/2014	MESSAGE V.2 RCP_8.5_MIT_4.5W and RCP_8.5_MIT_2.6W: NOx and Sulfur emissions in 2005 corrected	None
1.0.2	06/29/2015	POLES EMF27, all scenarios: Variable "Land Cover\|Cropland\|Energy Crops" removed from database because of inconsistent definition (reported "potential area" for bioenergy from food crops and short rotation coppice instead of "actual area")	None

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for the avoidance of doubt, Section 2(a)(1) grants You the right to extract, reuse, reproduce, and Share all or a substantial portion of the contents of the database;

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IAMC AR5 Scenario Database, 2014
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