EMF27 Database (version 0.9):

The EMF27 Scenario Database is the result of a multi-year cross-model comparison study entitled, "Global Technology Strategies for Greenhouse Gas Mitigation." It focuses on the development and cross model comparison of a new generation of comprehensive international climate policy intervention scenarios focusing on technology strategies for achieving climate policy objectives. That is, the study investigates which technologies will be need and when they need to be implemented in order to reach a variety of climate targets. These scenarios allowed participating models to exercise enhanced modeling capabilities that were focused on in previous EMF studies on the international trade implications of climate policies; the representation of technological change; and the incorporation of multi-gas mitigation and land use emissions and mitigation policy alternatives. Building upon previous studies (EMF22) the EMF27 scenarios compile a high-quality and coordinated set of technology-rich global and regional transitional policy scenarios. By moving from idealized scnearios to more realistic scenarios that do not assume perfect where, when, and what flexibility in the implementation of the policies, the EMF27 hopes to inform the upcoming climate policy discussions by investigating how the introduction of advanced low GHG emitting technologies can reduce the cost of both ideal and alternative climate policies. The final results of the EMF27 study are published in a special issue of the journal Climatic Change which provides an overview of the results of EMF27.

Contents of the Database

The EMF27 database, operated by IIASA for the EMF27 consortium, hosts the model results for the 18 participating global energy-economy models from research teams around the world.

Participating Models

  • AIM/End Use (Japan)
  • BET (Japan)
  • DNE21+ (Japan)
  • EC-IAM (Canada)
  • ENV-Linkages (OECD)
  • FARM (U.S./Germany)
  • GCAM (U.S.)
  • GCAM-IIM (U.S./India)
  • GRAPE (Japan)
  • IMACLIM (France)
  • IMAGE (Netherlands)
  • MERGE (U.S.)
  • MESSAGE (IIASA)
  • Phoenix (U.S.)
  • POLES (France)
  • REMIND (Germany)
  • TIAM-World (Canada)
  • WITCH (Italy)

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 EMF27 Database Administrator.

A short tutorial on the use of the database

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 five 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. With exception of the About tab which can be viewed without special permissions, all other tabs require a valid user account (login and password) for the database. After entering the credentials the four additional tabs to navigate the database can be accessed. The first three tabs, i.e. Regions, Sectors and Series, all allow to view the scenarios in the database. Which scenarios can be viewed depends on the individual user permissions (see User account). 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 (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. Generally the regions are organized in the two folders Compare and Model Name. While in the Compare folder you can find regions that are (approximately) defined across many models (World, five macro regions and seven countries commonly compared in modeling comparison exercises, see Region definitions), the Model name folder contains the native regions that you report to the database.

(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. Note that historical data is only available for the regions in the Compare folder (see also previous paragraph) and that currently only some emission and energy variables are covered. Further, displaying historical data is at the moment restricted to the Sectors tab.

(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 (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 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.

Region definitions

The consolitated results in the database are shown at regional aggregations of the World, five macro regions and seven individual countries/regions that are commonly used in scenario analysis. These regions are defined as follows:

Aggregation on the five region level

OECD90 = Includes the OECD 90 countries.
Australia, Austria, Belgium, Canada, Denmark, Fiji, Finland, France, French Polynesia, Germany, Greece, Guam, Iceland, Ireland, Italy, Japan, Luxembourg, Netherlands, New Caledonia, New Zealand, Norway, Portugal, Samoa, Solomon Islands, Spain, Sweden, Switzerland, Turkey, United Kingdom, United States of America, Vanuatu

REF = Countries from the Reforming Ecomonies of Eastern Europe and the Former Soviet Union.
Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Czech Republic, Estonia, Georgia, Hungary, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Malta, Poland, Republic of Moldova, Romania, Russian Federation, Slovakia, Slovenia, Tajikistan, TFYR Macedonia, Turkmenistan, Ukraine, Uzbekistan, Yugoslavia

ASIA = The region includes most Asian countries with the exception of the Middle East, Japan and Former Soviet Union states.
Afghanistan, Bangladesh, Bhutan, Brunei Darussalam, Cambodia, China, China Hong Kong SAR, China Macao SAR, Democratic People's Republic of Korea, East Timor, India, Indonesia, Lao People's Democratic Republic, Malaysia, Maldives, Mongolia, Myanmar, Nepal, Pakistan, Papua New Guinea, Philippines, Republic of Korea, Singapore, Sri Lanka, Taiwan, Thailand, Viet Nam

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, Cote d'Ivoire, Democratic Republic of the Congo, Djibouti, Egypt, Equatorial Guinea, Eritrea, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Iran (Islamic Republic of), Iraq, Israel, Jordan, Kenya, Kuwait, Lebanon, Lesotho, Liberia, Libyan Arab Jamahiriya, Madagascar, Malawi, Mali, Mauritania, Mauritius, Morocco, Mozambique, Namibia, Niger, Nigeria, Oman, Qatar, Reunion, Rwanda, Saudi Arabia, Senegal, Sierra Leone, Somalia, South Africa, Sudan, Swaziland, Syrian Arab Republic, Togo, Tunisia, Uganda, United Arab Emirates, United Republic of Tanzania, Western Sahara, Yemen, Zambia, Zimbabwe

LAM = This region includes the countries of Latin America and the Caribbean.
Argentina, Bahamas, Barbados, Belize, Bolivia, Brazil, Chile, Colombia, Costa Rica, Cuba, Dominican Republic, Ecuador, El Salvador, Guadeloupe, Guatemala, Guyana, Haiti, Honduras, Jamaica, Martinique, Mexico, Netherlands Antilles, Nicaragua, Panama, Paraguay, Peru, Puerto Rico, Suriname, Trinidad and Tobago, Uruguay, Venezuela

Seven individual countries/regions commonly used in scenario analysis

Brazil = Federative Republic of Brazil
China = People's Repulic of China
India = Republic of India
EU = European Union (27 member countries)
Japan = State of Japan
Russia = Russian Federation
USA = United States of America

References

Climatic Change Special Issue: The EMF27 Study on Global Technology and Climate Policy Strategies

  • Akashi, O., Hanaoka, T., Masui, T. & Kainuma, M. 2014. Halving global GHG emissions by 2050 without depending on nuclear and CCS. Climatic Change, 123, 611-622.
  • Bibas, R. & M?jean, A. 2014. Potential and limitations of bioenergy for low carbon transitions. Climatic Change, 123, 731-761.
  • Blanford, G., Merrick, J., Richels, R. & Rose, S. 2014. Trade-offs between mitigation costs and temperature change. Climatic Change, 123, 527-541.
  • Blanford, G. J., Kriegler, E. & Tavoni, M. 2014. Harmonization vs. fragmentation: Overview of climate policy scenarios in EMF27. Climatic Change, 123, 383-396.
  • Calvin, K., Wise, M., Kyle, P., Patel, P., Clarke, L. & Edmonds, J. 2014. Trade-offs of different land and bioenergy policies on the path to achieving climate targets. Climatic Change, 123, 691-704.
  • Daenzer, K., Wing, I. S. & Fisher-Vanden, K. 2014. Coal's medium-run future under atmospheric greenhouse gas stabilization. Climatic Change, 123, 763-783.
  • De Cian, E., Carrara, S. & Tavoni, M. 2014. Innovation benefits from nuclear phase-out: Can they compensate the costs? Climatic Change, 123, 637-650.
  • Griffin, B., Buisson, P., Criqui, P. & Mima, S. 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, 623-635.
  • Kanudia, A., Labriet, M. & Loulou, R. 2014. Effectiveness and efficiency of climate change mitigation in a technologically uncertain World. Climatic Change, 123, 543-558.
  • Kim, S. H., Wada, K., Kurosawa, A. & Roberts, M. 2014. Nuclear energy response in the EMF27 study. Climatic Change, 123, 443-460.
  • Klein, D., Luderer, G., Kriegler, E., Strefler, J., Bauer, N., Leimbach, M., Popp, A., Dietrich, J. P., Humpen?der, F., Lotze-Campen, H. & Edenhofer, O. 2014. The value of bioenergy in low stabilization scenarios: An assessment using REMIND-MAgPIE. Climatic Change, 123, 705-718.
  • Krey, V., Luderer, G., Clarke, L. & Kriegler, E. 2014. Getting from here to there - energy technology transformation pathways in the EMF27 scenarios. Climatic Change, 123, 369-382.
  • Kriegler, E., Weyant, J. P., Blanford, G. J., Krey, V., Clarke, L., Edmonds, J., Fawcett, A., Luderer, G., Riahi, K., Richels, R., Rose, S. K., Tavoni, M. & Van Vuuren, D. P. 2014. The role of technology for achieving climate policy objectives: Overview of the EMF 27 study on global technology and climate policy strategies. Climatic Change, 123, 353-367.
  • Luderer, G., Krey, V., Calvin, K., Merrick, J., Mima, S., Pietzcker, R., Van Vliet, J. & Wada, K. 2014. The role of renewable energy in climate stabilization: Results from the EMF27 scenarios. Climatic Change, 123, 427-441.
  • Magn?, B., Chateau, J. & Dellink, R. 2014. Global implications of joint fossil fuel subsidy reform and nuclear phase-out: An economic analysis. Climatic Change, 123, 677-690.
  • Mccollum, D., Bauer, N., Calvin, K., Kitous, A. & Riahi, K. 2014. Fossil resource and energy security dynamics in conventional and carbon-constrained worlds. Climatic Change, 123, 413-426.
  • Mccollum, D., Krey, V., Kolp, P., Nagai, Y. & Riahi, K. 2014. Transport electrification: A key element for energy system transformation and climate stabilization. Climatic Change, 123, 651-664.
  • Popp, A., Rose, S. K., Calvin, K., Van Vuuren, D. P., Dietrich, J. P., Wise, M., Stehfest, E., Humpen?der, F., Kyle, P., Van Vliet, J., Bauer, N., Lotze-Campen, H., Klein, D. & Kriegler, E. 2014. Land-use transition for bioenergy and climate stabilization: Model comparison of drivers, impacts and interactions with other land use based mitigation options. Climatic Change, 123, 495-509.
  • Rose, S. K., Kriegler, E., Bibas, R., Calvin, K., Popp, A., Van Vuuren, D. P. & Weyant, J. 2014. Bioenergy in energy transformation and climate management. Climatic Change, 123, 477-493.
  • Sano, F., Akimoto, K. & Wada, K. 2014. Impacts of different diffusion scenarios for mitigation technology options and of model representations regarding renewables intermittency on evaluations of CO2 emissions reductions. Climatic Change, 123, 665-676.
  • Sugiyama, M., Akashi, O., Wada, K., Kanudia, A., Li, J. & Weyant, J. 2014. Energy efficiency potentials for global climate change mitigation. Climatic Change, 123, 397-411.
  • Van Vliet, J., Hof, A. F., Mendoza Beltran, A., Van Den Berg, M., Deetman, S., Den Elzen, M. G. J., Lucas, P. L. & Van Vuuren, D. P. 2014. The impact of technology availability on the timing and costs of emission reductions for achieving long-term climate targets. Climatic Change, 123, 559-569.
  • Weyant, J. & Kriegler, E. 2014. Preface and introduction to EMF 27. Climatic Change, 123, 345-352.
  • Yamamoto, H., Sugiyama, M. & Tsutsui, J. 2014. Role of end-use technologies in long-term GHG reduction scenarios developed with the BET model. Climatic Change, 123, 583-596.

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    EMF27 Database, 2012
    Available at: https://secure.iiasa.ac.at/web-apps/ene/EMF27DB

    Responsible for this page: EMF27 Database Administrator