Energy demand - REMIND-MAgPIE: Difference between revisions

From IAMC-Documentation
Jump to navigation Jump to search
 
m (Text replacement - "REMIND" to "REMIND-MAgPIE")
 
(8 intermediate revisions by 4 users not shown)
Line 1: Line 1:
{{ModelDocumentationTemplate
{{ModelDocumentationTemplate
|IsDocumentationOf=REMIND
|IsEmpty=No
|IsDocumentationOf=REMIND-MAgPIE
|DocumentationCategory=Energy demand
|DocumentationCategory=Energy demand
}}
}}
Baseline final energy in REMIND-MAgPIE 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.


Economic activity results in demand for final energy determined by the macro-economic production function. REMIND distinguishes between the stationary end-use sector (aggregating industry and residential & commercial) and the transport end-use sector. The distribution of energy carriers to end-use sectors forms the interface between the macro-economic module and the energy system module. Table 2 maps secondary energy supply to end-use sectors. REMIND represents transport and distribution of secondary energy carriers in terms of capacities that require investments and incur costs for operation and maintenance. These costs shift the final energy supply curves and depend on the mode of transportation. The effect for electricity is larger than for liquid fuels.
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.


In REMIND, there are three mechanisms for reductions in energy intensity, i.e. a decline in the use of energy for energy service input per unit of economic output. First, the efficiency parameters of the production function (exogenous) lead to autonomous reductions in energy intensity, which also occur in the absence of climate policy interventions. For 2005, the parameters are calibrated based on IEA energy balance sheets (IEA 2007a; IEA 2007b). We assume energy-related CES-efficiency parameters to change at the same rate as labor efficiency, including an additional adjustment factor. The model calibrates this factor separately for each region and each final energy type, so as to induce a gradual shift from solids and liquids to gases, transportation fuels and electricity, reflecting patterns of modernization observed in the past. We derive the reference trajectories for baseline scenarios without climate policy based on the following guidelines:
The scenario assumptions follow the SSP framework and narratives <ref>O’Neill, Kriegler  et al.</ref>. 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.


* Short- and mid-term final energy demand follows the trend for the years 2000-2010, which is consistent with most of the regional projections shown in the ?current policy scenario? of IEA WEO 2010.
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.
* Per-capita energy use for the end-use of transport, non-electric stationary, and stationary electricity follow a converging trend between regions (EJ/capita over GDP in PPP/cap).


Second, 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 to the production function increases. In absence of distortions, a reduction in final energy results in a lower GDP and, subsequently, lower consumption and welfare vaThird, 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.
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.


'''Table 2 - Overview of energy carriers used in end-use sectors.'''
Once these projections are calculated, they are aggregated to the sectoral and energy carrier levels present in REMIND-MAgPIE. Then, the macro-economic production function of REMIND-MAgPIE is calibrated to meet these energy demand pathways in the baseline scenario .


{| class="wikitable"
In policy cases, REMIND-MAgPIE 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.
|width="14%"|''' Sector'''
|width="14%"|'''Electricity'''
|width="14%"|'''Gases'''
|width="14%"|'''Liquids'''
|width="14%"|'''Hydrogen'''
|width="14%"|'''Solids'''
|width="14%"|'''Heat'''
|-
|'''Stationary'''
| x
| x
| x
| x
| x
| x
|-
|'''Transport'''
| x
| -
| x
| x
| -
| -
|}


== Behavioural change ==
Prices are determined via price response through the CES production function.


There is no explicit modeling of behavioral change. However, baseline energy demands are calibrated in such a way that the energy demand patterns in different regions slowly converge when displayed as per capita energy demand over per capita GDP.
 
<references/>

Latest revision as of 15:34, 21 November 2021

Model Documentation - REMIND-MAgPIE

Corresponding documentation
Previous versions
Model information
Model link
Institution Potsdam Institut für Klimafolgenforschung (PIK), Germany, https://www.pik-potsdam.de.
Solution concept General equilibrium (closed economy)MAgPIE: partial equilibrium model of the agricultural sector;
Solution method OptimizationMAgPIE: cost minimization;
Anticipation

Baseline final energy in REMIND-MAgPIE 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 [1]. 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 [2]. In line with the energy-ladder concept [3], 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-MAgPIE. Then, the macro-economic production function of REMIND-MAgPIE is calibrated to meet these energy demand pathways in the baseline scenario .

In policy cases, REMIND-MAgPIE 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.


  1. ↑ O’Neill, Kriegler et al.
  2. ↑ van Ruijven et al. 2008
  3. ↑ Karekezi et al. 2012
Retrieved from "https://www.iamcdocumentation.eu/index.php?title=Energy_demand_-_REMIND-MAgPIE&oldid=15201"