Model concept, solver and details - REMIND

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Model Documentation - REMIND
Corresponding documentation
Model information
Institution Potsdam Institut für Klimafolgenforschung (PIK)
Concept Hybrid

Hybrid model that couples an economic growth model with a detailed energy system model and a simple climate model.

Solution method Inter-temporal optimization that maximizes cumulated discounted global welfare: Ramsey-type growth model with Negishi approach to regional welfare aggregation.
Anticipation Perfect Foresight

REMIND solves for an inter-temporal Pareto optimum in economic and energy investments in each model region, fully accounting for inter-regional trade in goods, energy carriers and emissions allowances. The model allows for the analysis of technology options and policy proposals for climate change mitigation as well as related energy-economic transformation pathways.

The macro-economic core of REMIND in each region is a Ramsey-type optimal growth model, where the inter-temporal welfare of each region is maximized. Macro-economic production factors are capital, labor, and final energy. Economic output is used for investments in the macro-economic capital stock as well as consumption, trade, and energy system expenditures. It is possible to compute the co-operative Pareto-optimal global equilibrium including inter-regional trade as the global social optimum using the Negishi method [1] , or the non-cooperative market solution among regions using the Nash concept [2],[3]. In the absence of non-internalized externalities between regions, these two solutions coincide. The inclusion of inter-regional externalities (in particular technology spillovers) causes a difference between the market and the socially optimal solution.

The macro-economic core and the energy system module are hard-linked via the final energy demand and costs incurred by the energy system see [4] for further details. Economic activity results in demand for final energy such as transport energy, electricity, and non-electric energy for stationary end uses. A production function with constant elasticity of substitution (nested CES production function) determines final energy demand. The energy system module accounts for regional endowments of exhaustible primary energy resources as well as renewable energy potentials. More than 50 technologies are available for the conversion of primary energy into secondary energy carriers as well as for the distribution of secondary energy carriers into final energy.

The model accounts for CO2 emissions from fossil fuel combustion and land use as well as emissions from other greenhouse gases (GHGs). REMIND determines non-CO2 GHG emissions by applying marginal abatement costs curves relative to baseline emission levels that depend on activity variables or by assuming exogenous scenarios. For numerical reasons, we use a reduced-form climate module, which is calibrated to the MAGICC-6 model [5], to translate emissions into changes in atmospheric GHG concentrations, radiative forcing, and global mean temperature. For a more detailed evaluation, the model can be linked to the full MAGICC-6 climate model in an ex-post mode. REMIND is solved as a non-linear programming model. It is programmed in GAMS [6] and uses the CONOPT solver [7] by default.













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  2. Leimbach M, Baumstark L, Luderer G (2015) The role of time preferences in explaining long-term pattern of international trade. Global Economy Journal 15:83–106. doi: 10.1515/gej-2014-0035
  3. Leimbach M, Schultes A, Baumstark L, et al (2016) Solution algorithms of large‐scale Integrated Assessment models on climate change. Annals of Operations Research, doi:10.1007/s10479-016-2340-z.
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  5. Meinshausen M, S. C. B. Raper, T. M. L. Wigley (2011a) Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6–Part 1: Model description and calibration. Atmos Chem Phys 11:1417–1456. doi: 10.5194/acp- 11-1417-2011
  6. Brooke A, Kendrick D, Meeraus M (1992) GAMS - A User’s Guide, Release 2.25. The Scientific Press, San Francisco
  7. Drud AS (1994) CONOPT - A Large-Scale GRG Code. ORSA Journal on Computing 6:207–216.