Model Documentation - REMIND-MAgPIE

2017-06-22T22:34:11Z

Gunnar Luderer:

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This document describes the Integrated Assessment Model REMIND, which stands for “Regional Model of Investments and Development”, in its version 1.7. It updates the documentation of the previous model version 1.6. The model was originally introduced by Leimbach et al. (2010b). More information—including a documentation of the system of equations—is available on the REMIND website. <ref>See http://www.pik-potsdam.de/research/research-domains/sustainable-solutions/REMIND-code-1 for further documentation on REMIND. The model is programmed in GAMS.</ref>

REMIND is a '''global energy-economy-climate model spanning the years 2005-2100'''. <xr id="fig:Remind_1"/> illustrates its general structure. The macro-economic core of REMIND is a Ramsey-type optimal growth model in which inter-temporal welfare is maximized. REMIND divides the world into '''11 regions''': five individual countries (China, India, Japan, United States of America, and Russia) and six aggregated regions formed by the remaining countries (European Union, Latin America, sub-Saharan Africa without South Africa, Middle East / North Africa / Central Asia, other Asia, Rest of the World). The model computes the market equilibrium either as a Pareto optimal solution in which global welfare is maximized (cooperative solution assuming all externalities are internalized), or as a non-cooperative Nash solution in which welfare is optimized on the regional level without internalization of interregional externalities. The model explicitly represents '''trade''' in final goods, primary energy carriers, and in the case of climate policy, emissions allowances. '''Macro-economic production''' factors are capital, labor, and final energy. REMIND uses economic '''output''' for investments in the macro-economic capital stock as well as consumption, trade, and energy system expenditures.

The macro-economic core and the energy system module are '''hard-linked''' via the final energy demand and costs incurred by the energy system. 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 the final energy demand. The energy system module accounts for 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.

REMIND uses reduced-form emulators derived from the detailed land-use and agricultural model MAgPIE to represent land-use and agricultural emissions as well as bioenergy supply and other land-based mitigation options. REMIND can also be run in fully coupled mode with the MAgPIE model (Lotze-Campen et al. 2008).

The model accounts for the full range of anthropogenic greenhouse gas (GHG) emissions, most of which are represented by source. The MAGICC 6 (Meinshausen et al. 2011b) climate model is used to translate emissions into changes in atmospheric composition, radiative forcing and climate change.

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'''Figure 1'''. General structure of the REMIND model.

In terms of its macro-economic formulation, REMIND resembles other well established integrated assessment models such as RICE (Nordhaus and Yang 1996) and MERGE (Manne et al. 1995). However, REMIND is broader in scope and features a substantially higher level of detail in the representation of energy-system technologies, trade, and global capital markets. In contrast to RICE, REMIND does not monetize climate damages, and therefore is not applied to determine a (hypothetical) economically optimal level of climate change mitigation ("cost-benefit mode"), but rather efficient strategies to attain an exogenously prescribed climate target ("cost-effectiveness mode").

<xr id="tab:REMINDtable_1"/> provides an overview of REMIND's key features. Sections 2-5 describe individual modules, along with the relevant parameters and assumptions. Section 6 lists the model's strength and limits.

'''Table 1'''. Key features of REMIND, and reference to the relevant sections in this documentation.

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Energy demand - REMIND-MAgPIE

2017-02-09T15:22:11Z

Gunnar Luderer:

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Baseline final energy in REMIND is calibrated to projections from theEDGE2 model (Energy Demand Generator, version 2). EDGE2 integrates econometric projections based on historical trends with scenario assumptions about long-term developments. The econometric projections play an important role in the short term while scenario assumptions rather influence the long-term behavior. The EDGE2 model covers six energy carriers— biomass, coal, electricity, liquids, gas, district heat —and six sectors —residential, commercial, industry, non-energy use, agriculture and fisheries, others.

The econometric regressions draw on the historical relationship between the per capita energy carrier demand in each sector and the GDP or sectoral value added per capita. The specification of the econometric model differs from one energy carrier to the other depending upon the observed relationship in historical data between the explained and the explanatory variables, or upon the regional heterogeneity. Each sectoral energy carrier is treated individually, which allows for a better control of the econometric fit, but has the disadvantage of ignoring the interdependencies between them. However, these interdependencies are partly reflected in the historical data.

The scenario assumptions follow the SSP framework and narratives (O’Neill, Kriegler et al.). In the SSP2 middle-of-the road scenario, EDGE 2 assumes a continuation of historical per-capita energy demand trends, and a regional partial convergence towards a global trend line over time. This global trend line relates globally averaged per capita demand for an energy carrier with per capita GDP. The convergence assumption differs across energy carriers and sectors. Typically, demand for electricity will assume greater convergence than demand for gas, liquids or district heat, which reflects the diverse regional heating requirements. The resulting demands were then user-adjusted to ensure that aggregated demand for energy carriers used to provide heat lies within a band of expected per-capita heat demand at a given per capita income.

To derive SSP1 and SSP5 demand trajectories, three types of modifications were performed relative to SSP2 to reflect the respective scenario narratives: (1) a change in the energy intensity in the end-use sectors transportation, industry, residential and commercial buildings, (2) a change in the energy carrier intensities (most importantly, electric vs. non-electric), and (3) a change in the regional convergence of trajectories.

The projections show agreement with several energy stylized facts <ref>van Ruijven et al. 2008</ref>. In line with the energy-ladder concept <ref>Karekezi et al. 2012</ref>, the share of solids decreases widely. Most notably, they exhibit a phase-out of traditional biomass in developing countries. By contrast, the share of grid-based energy carriers, in particular electricity, is projected to increase across all regions over the century. Following GDP per capita and population projections, developing regions’ demands grow fast, while developed regions experience a slower increase. In line with other studies, we find that currently least-developed countries will account for the bulk of global energy demand in the long-term.

Once these projections are calculated, they are aggregated to the sectoral and energy carrier levels present in REMIND. Then, the macro-economic production function of REMIND is calibrated to meet these energy demand pathways in the baseline scenario .

In policy cases, REMIND can reduce energy intensity energy service input per unit of economic output through two mechanisms. First, the CES production function allows for price-dependent substitutions between aggregated energy and capital (substitution elasticity of 0.5). The introduction of additional constraints on the supply side (e.g., carbon taxes, resource, or emission constraints) results in higher energy prices and thus lower final energy consumption compared to the reference trajectories. As a consequence, the share of macro-economic capital input in the production function increases. In absence of distortions, a reduction in final energy results in a lower GDP and, subsequently, lower consumption and welfare values. Second, the model can endogenously improve end-use efficiency by investing in more efficient technologies for the conversion of final energies into energy services. For example, three vehicle technologies with different efficiencies are implemented in the light duty vehicle (LDV) mode of the transport sector, including internal combustion engine vehicles, battery-electric vehicles, and fuel cell vehicles.

<references/>

IAMC-Documentation - User contributions [en]

Model Documentation - REMIND-MAgPIE

Energy demand - REMIND-MAgPIE