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MESSAGEix is a versatile, dynamic, model framework for energy-engineering-economy-environment (E4) systems research. MESSAGE (without "…ix") is a specific formulation of a generic linear programming (LP) optimization model for strategic energy planning and integrated assessment of E4 systems, developed by the IIASA Energy, Climate, and Environment (ECE) Program since the 1980s. To incorporate feedback between prices and demand levels for energy and commodities, the LP model can optionally be linked to the economic general equilibrium (GE) MACRO model. The message_ix Python package—also fully usable from R—includes: Implementations of MESSAGE, MACRO, and their linkage, in GAMS, Application programming interfaces (APIs) and tools for model building and scientific programming, Extensive documentation and a complete test suite. The framework is built on IIASA's ix modeling platform (ixmp), which provides data warehouse features for high-powered numerical scenario analysis.
Thank you for using the MESSAGEix framework! Please cite the GitHub repository and the MESSAGEix framework manuscript (https://doi.org/10.1016/j.envsoft.2018.11.012).
modelling, data visualisation, integrated assessment, Python package, scenario analysis, energy systems, macro-energy
modelling, data visualisation, integrated assessment, Python package, scenario analysis, energy systems, macro-energy
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 3 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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