Energy demand - REMIND-MAgPIE - Revision history

Rineke Oostenrijk: Text replacement - "REMIND" to "REMIND-MAgPIE"

2021-11-21T13:34:15Z

Text replacement - "REMIND" to "REMIND-MAgPIE"

← Older revision		Revision as of 15:34, 21 November 2021
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	{{ModelDocumentationTemplate		{{ModelDocumentationTemplate
	\|IsEmpty=No		\|IsEmpty=No
	\|IsDocumentationOf=REMIND		\|IsDocumentationOf=REMIND-MAgPIE
	\|DocumentationCategory=Energy demand		\|DocumentationCategory=Energy demand
	}}		}}
	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.		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 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.
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	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.		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 .		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 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.		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.



	<references/>		<references/>

Mathijs Harmsen: Mathijs Harmsen moved page Energy demand - REMIND to Energy demand - REMIND-MAgPIE without leaving a redirect: Text replacement - "REMIND" to "REMIND-MAgPIE"

2021-11-18T12:13:56Z

Mathijs Harmsen moved page Energy demand - REMIND to Energy demand - REMIND-MAgPIE without leaving a redirect: Text replacement - "REMIND" to "REMIND-MAgPIE"

← Older revision

Revision as of 14:13, 18 November 2021

Laura Delsa at 16:48, 14 March 2017

2017-03-14T16:48:50Z

← Older revision		Revision as of 18:48, 14 March 2017
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	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 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.		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.

	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.		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.

Gunnar Luderer at 15:22, 9 February 2017

2017-02-09T15:22:11Z

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 |DocumentationCategory=Energy demand
 }}
-Baseline final energy demand projections are provided exogenously to REMIND. REMIND is then calibrated to match these projections in the baseline case. This approach based on EDGE (Energy Demand GEnerator) version 2, a simple econometric model combined with long-term convergence assumptions, helps combining historical trends with stylized facts and provides simple insights to the baseline energy demand trajectories.
+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.
 <references/>

Laura Delsa at 14:09, 3 February 2017

2017-02-03T14:09:06Z

← Older revision		Revision as of 16:09, 3 February 2017
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	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.		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/>

Laura Delsa at 13:46, 3 February 2017

2017-02-03T13:46:21Z

← Older revision		Revision as of 15:46, 3 February 2017
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	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.		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.
	The projections show agreement with several energy stylized facts (van Ruijven et al. 2008). In line with the energy-ladder concept (Karekezi et al. 2012), the share of solids decreases widely, most notably due to the 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.		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 due to the 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.

	SSP1 and SSP5 are derived from SSP2 intensity trajectories. There are three sets of assumptions used to derive the other SSP energy demand pathways. The first one describes how the sectoral energy intensity will evolve, the second one addresses the energy carrier intensities, and the third one the regional convergence of trajectories. The two first sets of assumptions are intended to represent scenario choices about the energy intensity development of a sector on the one hand side, and the choice of energy carriers on the other side. The convergence assumption describes the reduction of the regional spread to the global mean by 2100.		SSP1 and SSP5 are derived from SSP2 intensity trajectories. There are three sets of assumptions used to derive the other SSP energy demand pathways. The first one describes how the sectoral energy intensity will evolve, the second one addresses the energy carrier intensities, and the third one the regional convergence of trajectories. The two first sets of assumptions are intended to represent scenario choices about the energy intensity development of a sector on the one hand side, and the choice of energy carriers on the other side. The convergence assumption describes the reduction of the regional spread to the global mean by 2100.

Laura Delsa at 21:54, 20 November 2016

2016-11-20T21:54:59Z

← Older revision		Revision as of 23:54, 20 November 2016
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	{{ModelDocumentationTemplate		{{ModelDocumentationTemplate
			\|IsEmpty=No
	\|IsDocumentationOf=REMIND		\|IsDocumentationOf=REMIND
	\|DocumentationCategory=Energy demand		\|DocumentationCategory=Energy demand
	}}		}}
			Baseline final energy demand projections are provided exogenously to REMIND. REMIND is then calibrated to match these projections in the baseline case. This approach based on EDGE (Energy Demand GEnerator) version 2, a simple econometric model combined with long-term convergence assumptions, helps combining historical trends with stylized facts and provides simple insights to the baseline energy demand trajectories.

	~~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.		These projections cover several energy carriers and sectors (residential, commercial, industry, non-energy use, agriculture and fisheries, others).

	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:~~		Energy demand projections are calculated with the model EDGE. For the scenario SSP2, EDGE 2 relies on a mixture of econometric projections and convergence assumptions to produce baseline energy demand projections. The econometric projections play an important role in the short term while convergence assumptions rather influence the long-term behavior. Other SSPs are calculated as variations from SSP2 long-term assumptions.
			In the econometric part, projections are derived for different energy carriers biomass, coal, electricity, liquids, gas, district heat in several economic sectors residential, commercial, industry, agriculture and fishery, other, non-energy use. The regressions draw on the historical relationship between the per capita energy carrier demand in each given 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.

	* 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~~.		In the convergence part, a global convergence line is first computed from regional econometric projections, which relates the per capita demand for the energy carrier and the per capita value added level. Each region is then assumed to converge towards this line in the long term without necessarily reaching full convergence within the time horizon of the model. 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.
	* 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 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.
			The projections show agreement with several energy stylized facts (van Ruijven et al. 2008). In line with the energy-ladder concept (Karekezi et al. 2012), the share of solids decreases widely, most notably due to the 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~~.~~'''~~		SSP1 and SSP5 are derived from SSP2 intensity trajectories. There are three sets of assumptions used to derive the other SSP energy demand pathways. The first one describes how the sectoral energy intensity will evolve, the second one addresses the energy carrier intensities, and the third one the regional convergence of trajectories. The two first sets of assumptions are intended to represent scenario choices about the energy intensity development of a sector on the one hand side, and the choice of energy carriers on the other side. The convergence assumption describes the reduction of the regional spread to the global mean by 2100.
			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 these energy demand pathways in the baseline scenario by adjusting the efficiency parameters at each CES level in each time step.

	~~{\| class="wikitable"~~		In policy cases, REMIND can reduce energy intensity energy service input per unit of economic output through two mechanisms.
	~~\|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 ==~~		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.
	~~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~~.		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.

Laura Delsa at 18:34, 19 October 2016

2016-10-19T18:34:17Z

← Older revision		Revision as of 20:34, 19 October 2016
Line 8:		Line 8:
	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:		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:

	* 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.		* 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.
	* 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).		* 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).

Rineke Oostenrijk: 1 revision imported

2016-08-17T09:58:25Z

1 revision imported

← Older revision	Revision as of 11:58, 17 August 2016
(No difference)

Rineke Oostenrijk: /* Behavioural change */

2016-08-16T13:42:22Z

Behavioural change

New page

{{ModelDocumentationTemplate
|IsDocumentationOf=REMIND
|DocumentationCategory=Energy demand
}}

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.

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:

* 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.
* 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.

'''Table 2 - Overview of energy carriers used in end-use sectors.'''

{| class="wikitable"
|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.