The ENE-MCA tool was introduced to the scientific community in the journals Nature Climate Change (McCollum et al., 2011) and Climatic Change (McCollum et al., 2013).

Users Manual

A brief users manual for the ENE-MCA tool can be found here.

Methodology in Brief

Scenario Taxonomy

A thorough analysis of synergies and trade-offs among energy sustainability objectives necessitates a broad scenario space, stretching the potential development of the energy system in several dimensions. Therefore, in this analysis more than six hundred scenarios have been developed, each of which meets the different objectives (climate mitigation, air pollution and health, and energy security) in a unique way, with consequent impacts on costs. For instance, some scenarios advance new climate policies but do not consider any new energy security and air pollution legislation, while other scenarios prioritize only security while ignoring the other objectives. The reason for this experimental set-up is because the focus of this analysis is on the uncertainties surrounding future policy priorities rather than on traditional, exogenous uncertainties (e.g., technological and socio-economic). Note that achievement of the access objective is taken as given in this analysis, as all scenarios (including even the baseline scenario) have been developed to meet a global target that ensures almost universal access to electricity and clean cooking fuels by 2030. This simplification was made because achieving energy access, compared to other objectives, has relatively low impacts on energy use and GHG emissions.

The scenario ensemble that is created here springs from one of the three illustrative scenario pathways developed for the Global Energy Assessment (GEA), specifically the GEA-Mix (Riahi et al., 2012). Assumptions about the future drivers of global change, namely population and gross domestic product (GDP) within each aggregated world region, as well as the future availability of technologies, are the same as in the standard GEA-Mix pathway. The goal is to cover the entirety of the feasible scenario space under a common storyline for future population, economic development and resulting energy demand growth, using statistically corroborated “middle-of-the-road” assumptions from the scenario literature (Nakicenovic et al., 2006). More specifically, global population increases from almost 7 billion at present to roughly 9 billion by around 2050, before declining toward the end of the century. Such a trajectory represents a median development path based on demographic projections by the United Nations (United Nations, 2009). The GDP development paths for each of the regions build on the updated IPCC B2 scenario projection by Riahi et al. (2007). Globally aggregated GDP roughly triples by 2050 and increases more than seven-fold by 2100. Developing and emerging economies are projected to grow faster than currently industrialized countries during this time, with the total economic output of the former surpassing that of the latter by about 2040. On average, global per capita income in the scenarios grows at an annual rate of 2% over the next half-century.

All of the scenarios in the ensemble have been developed within the MESSAGE Integrated Assessment Modeling Framework. The MAGICC reduced-complexity global climate model is subsequently used to estimate the climate system impacts of the scenarios. Starting from a baseline scenario of energy system development, several hundred additional scenarios are generated by running MESSAGE and imposing varying combinations of policy constraints at varying levels of stringency across several different energy-related dimensions (i.e., climate, security, pollution/health). In particular, for each scenario two types of constraints are imposed: one on the shape of the global annual GHG emissions trajectory over the course of the 21st century, and another on the maximum amount of energy that can be imported into any given region in a particular year, starting in 2030. On top of this, four different sets of increasingly stringent air quality legislation packages are implemented, in order to stretch the scenario space in the air pollution and health dimension.

Figure 1 provides a simple graphical representation of how the multitude of scenarios in the ensemble is constructed. Notably, there are thirty-nine different greenhouse gas emissions trajectories represented, ranging from extremely high baseline futures (>1000 ppm CO2-eq in 2100) to low climate stabilization scenarios (<450 ppm CO2-eq in 2100) and many points in between. Similarly, there are four different combinations each for energy security and air pollution policy packages. Hence, in total this leads to 624 unique scenario combinations across the three dimensions.

For a short write-up elaborating upon this discussion, please see McCollum et al. (2012).

Scenario Taxonomy
Figure 1: Schematic showing the taxonomy of scenarios included in the ensemble

MESSAGE Integrated Assessment Modeling Framework

The MESSAGE (Model for Energy Supply Strategy Alternatives and their General Environmental Impact) integrated assessment model (IAM) is a global systems engineering optimization model used for medium- to long-term energy system planning, energy policy analysis, and scenario development (Messner and Strubegger, 1995). Developed at the International Institute for Applied Systems Analysis (IIASA) for more than two decades, MESSAGE is an evolving framework that, like other global IAMs in its class (e.g., AIM, EPPA, IMAGE, IPAC, WITCH, and GCAM), has gained wide recognition over time through its repeated utilization in developing global energy and emissions scenarios, for example its use in previous IPCC reports (e.g., see Nakicenovic and Swart (2000)).

The MESSAGE model divides the world up into eleven (11) regions in an attempt to represent the global energy system in a simplified way, yet with many of its complex interdependencies, from resource extraction, imports and exports, conversion, transport, and distribution, to the provision of energy end-use services such as lighting, space conditioning, industrial production processes, and transportation. Trade flows (imports and exports) between regions are monitored, capital investments and retirements are made, fuels are consumed, and emissions are generated. The model is able to choose between both conventional and non-conventional technologies and fuels (e.g., advanced fossil, nuclear fission, biomass, and renewables). In the version of the model used in this study, a portfolio of technologies is considered, whose components are either in the early demonstration or commercialization phase (e.g., coal, natural gas, oil, nuclear, biomass, solar, wind, hydro, geothermal, carbon capture and storage, hydrogen, biofuels, and electrified transport, to name just a subset). However, exceedingly futuristic technological options, such as nuclear fusion and geo-engineering, are not included in the current version of the MESSAGE model.

In addition to the energy system, the model includes also the other main greenhouse-gas emitting sectors, agriculture, and forestry. MESSAGE tracks a full basket of greenhouse gases and other radiatively active gases – CO2, CH4, N2O, NOx, volatile organic compounds (VOCs), CO, SO2, PM, BC, OC, NH3, CF4, C2F6, HFC125, HFC134a, HFC143a, HFC227ea, HFC245ca, and SF6 – from both the energy and non-energy sectors (e.g., deforestation, livestock, municipal solid waste, manure management, rice cultivation, wastewater, and crop residue burning). In other words, all Kyoto gases plus several others are accounted for.

A global (real) discount rate of 5% is used within the model.

For a short write-up elaborating upon this discussion, please see McCollum et al. (2011) - Documentation.

MAGICC Global Climate Model

MAGICC (Model for the Assessment of Greenhouse-gas Induced Climate Change, version 5.3) is used in this study to estimate the climate system impacts of the varying greenhouse gas emission trajectories of the scenarios in the ensemble. MAGICC is a reduced complexity coupled global climate-carbon cycle model, in the form of a user-friendly software package that runs on a personal computer (Wigley, 2008). MAGICC calculates internally consistent projections for atmospheric concentrations, radiative forcing, global annual-mean surface air temperature, ice melt, and sea level rise, given emissions trajectories of a range of gases (CO2, CH4, N2O, CO, NOx, VOCs, SO2, and various halocarbons, including HCFCs, HFCs, PFCs, and SF6).

The primary developer of MAGICC is Dr. Tom Wigley at the National Center for Atmospheric Research in the United States. The modeling package has been used in all IPCC Assessment reports, dating back to 1990, and its strength lies in its ability to replicate the more complex global climate models, which often require supercomputers. For our analysis, we use a version of the software that is consistent with the IPCC Fourth Assessment Report, Working Group 1.

For a short write-up elaborating upon this discussion, please see McCollum et al. (2011) - Documentation.

Multi-Criteria Solution Methods

The multi-criteria analysis (MCA) tool works by giving users the ability to compare a given set of discrete alternatives, each characterized by more than one criterion. In this analysis, these discrete alternatives are individual energy and climate model scenarios, which have been generated by running a combination of MESSAGE and MAGICC. These different scenario runs represent unique future states of the world, in which the multiple energy sustainability objectives are satisfied to varying degrees. Various criteria are used to measure the fulfillment of the individual objectives. These can, in theory, include any number of indicators from the output of the scenarios; Though, in an effort not to overwhelm users of the policy tool, we limit the criteria to a small set of representative indicators, summarized here:

  • Climate: probability of limiting global maximum temperature increase to 2 ºC above the pre-industrial level over the 21st century

  • Energy Security: globally-aggregated compound primary energy diversity indicator in 2030

  • Health: globally-aggregated disability-adjusted life years (DALYs) in 2030

  • Costs: total cumulative global energy system costs from 2010 to 2030

Once users have specified their priority ranking structure for the objectives using the slider bars of the tool, the software employs an algorithm that maps these qualitative preference values into quantitative weights. It then finds the alternative (i.e., scenario) that best fits these preferences. The user can at this point decide whether or not the trade-offs between the corresponding criteria values meet his/her expectations. If not, the user enters the next iteration by modifying the preferences, typically by increasing the importance of the criterion value that she wants to improve, while at the same time decreasing the importance of another criterion that she agrees to compromise. This iterative process continues until the user becomes familiar with various combinations of attainable goals (i.e., values for criteria) and finds an efficient alternative that best fits her preferences for trade-offs between the goals.

Optimality in the multi-criteria sense is based on the concept of "Pareto efficiency." A Pareto-optimal (also called efficient, or non-dominated) solution is the alternative for which there is no other alternative that has a better value of one criterion and at least equally good values of all other criteria. In other words, for an efficient alternative one cannot improve the value of at least one criterion without worsening the value of at least one other criterion (Makowski, 2009). In this sense, multi-criteria methods differ substantially from traditional optimization-based methods, in which a pre-defined single-criterion is used as an objective function for the underlying optimization problem. With such methods the other criteria are handled as constraints. An alternative approach is to aggregate all criteria into an objective function (often called a utility function). Both of these traditional approaches have a number of methodological and practical disadvantages (see for example Wierzbicki et al. (2000), Granat and Makowski (2009), Makowski (2009)).

For a short write-up elaborating upon this discussion, please see McCollum et al. (2012).

Further Reading

  • McCollum, D., V. Krey, K. Riahi (2011) - Documentation. "The IIASA Energy - Multi Criteria Analysis (ENE-MCA) Policy Tool: Additional Documentation"

  • McCollum, D., V. Krey, K. Riahi, P. Kolp, M. Makowski, B. Schreck (2012). "The IIASA Energy - Multi Criteria Analysis (ENE-MCA) Policy Tool: User Manual"

  • Granat, J. and M. Makowski (2009). Multicriteria methodology for the NEEDS project, Interim Report IR-09-10. Laxenburg, Austria, New Energy Externalities Development for Sustainability (NEEDS), International Institute for Applied Systems Analysis (IIASA): 58.

  • Makowski, M. (2009). "Management of Attainable Tradeoffs between Conflicting Goals." Journal of Computers 4(10): 10.

  • Messner, S. and M. Strubegger (1995). User's guide for MESSAGE III, Working Paper WP-95-069. Laxenburg, Austria, International Institute for Applied Systems Analysis (IIASA): 164.

  • Nakicenovic, N., P. Kolp, K. Riahi, M. Kainuma, T. Hanaoka (2006). "Assessment of emissions scenarios revisited." Environmental economics and policy studies 7(3): 137-173.

  • Nakicenovic, N. and R. Swart (2000). IPCC Special Report on Emissions Scenarios. Cambridge, Cambridge University Press.

  • Riahi, K., A. Grübler, N. Nakicenovic (2007). "Scenarios of long-term socio-economic and environmental development under climate stabilization." Technological Forecasting and Social Change 74(7): 887-935.

  • Riahi et al. (2012). 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.

  • United Nations (2009). World Population Prospects:The 2008 Revision. New York City, Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat.

  • Wierzbicki, A., M. Makowski, J. Wessels (2000). Model-Based Decision Support Methodology with Environmental Applications, Kluwer Academic Publishers.

  • Wigley, T. M. L. (2008). MAGICC/SCENGEN 5.3: User Manual (version 2). Boulder, National Center for Atmospheric Research.

  • ENE-MCA Policy Tool, 2011
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