Source: https://tntcat.iiasa.ac.at/AR5DB/dsd?Action=htmlpage&page=about
Timestamp: 2019-04-19 16:25:53+00:00

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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.
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.
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.
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.
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.
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.
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.
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).
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.
(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.
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.
EIT = Economies in Transition. This region is sometimes also referred to as Reforming Ecomonies of Eastern Europe and the Former Soviet Union (REF).
ASIA = Non-OECD ASIA. The region includes most Asian countries with the exception of the Middle East, Japan and Former Soviet Union states.
MAF = This region includes the countries of the Middle East and Africa.
LAM = This region includes the countries of Latin America and the Caribbean.
Aboumahboub T., G. Luderer, E. Kriegler, M. Leimbach, N. Bauer, M. Pehl, and L. Baumstark (2014). On the regional distribution of climate mitigation costs: the impact of delayed cooperative action. Climate Change Economics, In Press.
Akashi O., T. Hanaoka, T. Masui, and M. Kainuma (2014). Halving global GHG emissions by 2050 without depending on nuclear and CCS. Climatic Change In Press.
Akimoto K., F. Sano, T. Homma, K. Wada, M. Nagashima, and J. Oda (2012). Comparison of marginal abatement cost curves for 2020 and 2030: Longer perspectives for effective global GHG emission reductions. Sustainability Science 7, 157-168.
Arroyo-Currás T., N. Bauer, E. Kriegler, V.J. Schwanitz, G. Luderer, T. Aboumahboub, A. Giannousakis, and J. Hilaire (2013). Carbon leakage in a fragmented climate regime: The dynamic response of global energy markets. Technological Forecasting and Social Change, In Press.
Blanford G., E. Kriegler, and M. Tavoni (2014). Harmonization vs. Fragmentation: Overview of Climate Policy Scenarios in EMF27. Climatic Change, 123 (3-4) 383-396.
Blanford G., J. Merrick, R. Richels, and S. Rose (2013). Trade-offs between mitigation costs and temperature change. Climatic Change, 123 (3-4( 527-541.
Blanford G.J., R.G. Richels, and T.F. Rutherford (2009). Feasible climate targets: The roles of economic growth, coalition development and expectations. International, U.S. and E.U. Climate Change Control Scenarios: Results from EMF 22 31, Supplement 2, S82-S93.
Bosetti V., C. Carraro, and M. Tavoni (2009). Climate change mitigation strategies in fast-growing countries: The benefits of early action. International, U.S. and E.U. Climate Change Control Scenarios: Results from EMF 22 31, Supplement 2, S144-S151.
Calvin K., L. Clarke, V. Krey, and G. Blanford (2012). The role of Asia in Mitigating Climate Change: Results from the Asia Modeling Excercise. Energy Economics, 34, Supplement 3, S251-S260.
Calvin K., J. Edmonds, B. Bond-Lamberty, L. Clarke, S.H. Kim, P. Kyle, S.J. Smith, A. Thomson, and M. Wise (2009). 2.6: Limiting climate change to 450 ppm CO2 equivalent in the 21st century. Energy Economics 31, S107-S120.
Calvin K., P. Patel, A. Fawcett, L. Clarke, K. Fisher-Vanden, J. Edmonds, S.H. Kim, R. Sands, and M. Wise (2009). The distribution and magnitude of emissions mitigation costs in climate stabilization under less than perfect international cooperation: SGM results. Energy Economics 31, S187-S197.
Calvin K., M. Wise, P. Kyle, P. Patel, L. Clarke, and J. Edmonds (2013). Trade-offs of different land and bioenergy policies on the path to achieving climate targets. Climatic Change, 123 (3-4) 691-703.
Calvin K., M. Wise, P. Luckow, P. Kyle, L. Clarke, and J. Edmonds (2014). Implications of uncertain future fossil energy resources on bioenergy use and terrestrial carbon emissions. Climatic Change Forthcoming.
Chen W., H. Yin, and H. Zhang (2014). Towards low carbon development in China: a comparison of national and global models. Climatic Change, In Press.
De Cian E., V. Bosetti, and M. Tavoni (2012). Technology innovation and diffusion in "less than ideal" climate policies: An assessment with the WITCH model. Climatic Change 114, 121-143.
De Cian E., S. Carrara, and M. Tavoni (2013). Innovation benefits from nuclear phase-out: can they compensate the costs? Climatic Change, 123 (3-4) 637-650.
De Cian E., F. Sferra, and M. Tavoni (2013). The influence of economic growth, population, and fossil fuel scarcity on energy investments. Climatic Change, In Press.
Clarke L., J. Edmonds, V. Krey, R. Richels, S. Rose, and M. Tavoni (2009). International climate policy architectures: Overview of the EMF 22 International Scenarios. International, U.S. and E.U. Climate Change Control Scenarios: Results from EMF 22 31, Supplement 2, S64-S81.
Deng Y.Y., K. Blok, and K. van der Leun (2012). Transition to a fully sustainable global energy system. Energy Strategy Reviews 1, 109-121.
Dowling P., and P. Russ (2012). The benefit from reduced energy import bills and the importance of energy prices in GHG reduction scenarios. The Asia Modeling Exercise: Exploring the Role of Asia in Mitigating Climate Change 34, Supplement 3, S429-S435.
Edenhofer O., B. Knopf, M. Leimbach, and N. Bauer (2010). ADAM's Modeling Comparison Project-Intentions and Prospects. The Energy Journal 31, 7-10.
Fisher-Vanden K., K. Schu, I. Sue Wing, and K. Calvin (2012). Decomposing the impact of alternative technology sets on future carbon emissions growth. The Asia Modeling Exercise: Exploring the Role of Asia in Mitigating Climate Change 34, Supplement 3, S359-S365.
Griffin B., P. Buisson, P. Criqui, and S. Mima (2014). White Knights: will wind and solar come to the rescue of a looming capacity gap from nuclear phase-out or slow CCS start-up? Climatic Change, 123 (3-4) 623-635.
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.
Kober T., B. van der Zwaan, and H. Rösler (2014). Emission Certificate Trade and Costs under Regional Burden-Sharing Regimes for a 2°C Climate Change Control Target. Climate Change Economics In Press.
Koljonen T., and A. Lehtilä (2012). The impact of residential, commercial, and transport energy demand uncertainties in Asia on climate change mitigation. The Asia Modeling Exercise: Exploring the Role of Asia in Mitigating Climate Change 34, Supplement 3, S410-S420.
Krey V., G. Luderer, L. Clarke, and E. Kriegler (2014). Getting from here to there - energy technology transformation pathways in the EMF-27 scenarios. Climatic Change, 123 (3-4) 369-382.
Krey V., and K. Riahi (2009). Implications of delayed participation and technology failure for the feasibility, costs, and likelihood of staying below temperature targets-Greenhouse gas mitigation scenarios for the 21st century. International, U.S. and E.U. Climate Change Control Scenarios: Results from EMF 22 31, Supplement 2, S94-S106.
Kriegler E., K. Riahi, N. Bauer, V.J. Schanitz, N. Petermann, V. Bosetti, A. Marcucci, S. Otto, L. Paroussos, and et al. (2014). Making or breaking climate targets: The AMPERE study on staged accession scenarios for climate policy. Technological Forecasting and Social Change, In Press.
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.
Marangoni G., and M. Tavoni (2014).. Climate Change Economics 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.
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.
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