Macro-economy - REMIND-MAgPIE - Revision history

Mathijs Harmsen: Text replacement - "RICE" to "RICE50+"

2023-06-12T13:46:00Z

Text replacement - "RICE" to "RICE50+"

← Older revision		Revision as of 15:46, 12 June 2023
Line 23:		Line 23:
	</figure>		</figure>

	An iterative algorithm adjusts the weights so as to equalize the intertemporal balance of payments of each region over the entire time horizon. This convergence criterion ensures that the Pareto-optimal solution of the model corresponds with the market equilibrium in the absence of non-internalized externalities. The algorithm is an inter-temporal extension of the original Negishi approach <ref>Negishi T (1972) General equilibrium theory and international trade. North-Holland Publishing Company Amsterdam, London</ref>; see also <ref>Manne AS, Rutherford TF (1994) International Trade in Oil, Gas and Carbon Emission Rights: An Intertemporal General Equilibrium Model. The Energy Journal Volume15:57–76</ref> for a discussion of the extension. Other models such as MERGE <ref>Manne A, Mendelsohn R, Richels R (1995) MERGE: A model for evaluating regional and global effects of GHG reduction policies. Energy Policy 23:17–34. doi: 10.1016/0301-4215(95)90763-W</ref> and ~~RICE~~ <ref>Nordhaus WD, Yang Z (1996) A Regional Dynamic General-Equilibrium Model of Alternative Climate-Change Strategies. The American Economic Review 86:741–765</ref> use this algorithm in a similar way.		An iterative algorithm adjusts the weights so as to equalize the intertemporal balance of payments of each region over the entire time horizon. This convergence criterion ensures that the Pareto-optimal solution of the model corresponds with the market equilibrium in the absence of non-internalized externalities. The algorithm is an inter-temporal extension of the original Negishi approach <ref>Negishi T (1972) General equilibrium theory and international trade. North-Holland Publishing Company Amsterdam, London</ref>; see also <ref>Manne AS, Rutherford TF (1994) International Trade in Oil, Gas and Carbon Emission Rights: An Intertemporal General Equilibrium Model. The Energy Journal Volume15:57–76</ref> for a discussion of the extension. Other models such as MERGE <ref>Manne A, Mendelsohn R, Richels R (1995) MERGE: A model for evaluating regional and global effects of GHG reduction policies. Energy Policy 23:17–34. doi: 10.1016/0301-4215(95)90763-W</ref> and RICE50+ <ref>Nordhaus WD, Yang Z (1996) A Regional Dynamic General-Equilibrium Model of Alternative Climate-Change Strategies. The American Economic Review 86:741–765</ref> use this algorithm in a similar way.

	The Nash solution concept, by contrast, arrives at the Pareto solution not by Joint Maximization, but by maximizing the regional welfare subject to regional constraints and international prices that are taken as exogenous data for each region. The intertemporal balance of payments of each region has to equal zero and is one particular constraint imposed on each region. The equilibrium solution is found by iteratively adjusting the international prices until global demand and supply are balanced on each market. The choice of the solution concept is also important for the representation of trade, as discussed in Section the section on Trade.		The Nash solution concept, by contrast, arrives at the Pareto solution not by Joint Maximization, but by maximizing the regional welfare subject to regional constraints and international prices that are taken as exogenous data for each region. The intertemporal balance of payments of each region has to equal zero and is one particular constraint imposed on each region. The equilibrium solution is found by iteratively adjusting the international prices until global demand and supply are balanced on each market. The choice of the solution concept is also important for the representation of trade, as discussed in Section the section on Trade.

Mathijs Harmsen at 10:50, 11 May 2023

2023-05-11T10:50:17Z

← Older revision		Revision as of 12:50, 11 May 2023
Line 4:		Line 4:
	\|DocumentationCategory=Macro-economy		\|DocumentationCategory=Macro-economy
	}}		}}
			'''Note:''' This pages describes the REMIND 1.7 model. It will be updated shortly to describe the most recent version of REMIND-MAgPIE.
			<br>
			<br>
	==Objective function==		==Objective function==

Mathijs Harmsen: Reverted edits by Lavinia Baumstark (talk) to last revision by Rineke Oostenrijk

2023-02-22T17:06:23Z

Reverted edits by Lavinia Baumstark (talk) to last revision by Rineke Oostenrijk

← Older revision		Revision as of 19:06, 22 February 2023
Line 4:		Line 4:
	\|DocumentationCategory=Macro-economy		\|DocumentationCategory=Macro-economy
	}}		}}
			==Objective function==

			REMIND-MAgPIE models each region r as a representative household with a utility function Ur that depends upon per-capita consumption

			<figure id="fig:REMIND-MAgPIE_3.2.1 1.">
			[[File:REMIND-MAgPIE equation 3.2.1 1.JPG]]
			</figure>

			where C(r,t) is the consumption of region r at time t, and P(r,t) is the population in region r at time t. The calculation of utility is subject to discounting; 3% is assumed for the pure rate of time preference rho. The logarithmic relationship between per-capita consumption and regional utility implies an elasticity of marginal consumption of 1. Thus, in line with the Keynes-Ramsey rule, REMIND-MAgPIE yields an endogenous interest rate in real terms of 5–6% for an economic growth rate of 2–3%. This is in line with the interest rates typically observed on capital markets.

			REMIND-MAgPIE can compute maximum regional utility (welfare) by two different solution concepts – the Negishi approach and the Nash approach <ref>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</ref>. In the Negishi approach, which computes a cooperative solution, the objective of the Joint Maximization Problem is the weighted sum of regional utilities, maximized subject to all other constraints:

			<figure id="fig:REMIND-MAgPIE_3.2.1 2.">
			[[File:REMIND-MAgPIE equation 3.2.1 2.JPG]]
			</figure>

			An iterative algorithm adjusts the weights so as to equalize the intertemporal balance of payments of each region over the entire time horizon. This convergence criterion ensures that the Pareto-optimal solution of the model corresponds with the market equilibrium in the absence of non-internalized externalities. The algorithm is an inter-temporal extension of the original Negishi approach <ref>Negishi T (1972) General equilibrium theory and international trade. North-Holland Publishing Company Amsterdam, London</ref>; see also <ref>Manne AS, Rutherford TF (1994) International Trade in Oil, Gas and Carbon Emission Rights: An Intertemporal General Equilibrium Model. The Energy Journal Volume15:57–76</ref> for a discussion of the extension. Other models such as MERGE <ref>Manne A, Mendelsohn R, Richels R (1995) MERGE: A model for evaluating regional and global effects of GHG reduction policies. Energy Policy 23:17–34. doi: 10.1016/0301-4215(95)90763-W</ref> and RICE <ref>Nordhaus WD, Yang Z (1996) A Regional Dynamic General-Equilibrium Model of Alternative Climate-Change Strategies. The American Economic Review 86:741–765</ref> use this algorithm in a similar way.

			The Nash solution concept, by contrast, arrives at the Pareto solution not by Joint Maximization, but by maximizing the regional welfare subject to regional constraints and international prices that are taken as exogenous data for each region. The intertemporal balance of payments of each region has to equal zero and is one particular constraint imposed on each region. The equilibrium solution is found by iteratively adjusting the international prices until global demand and supply are balanced on each market. The choice of the solution concept is also important for the representation of trade, as discussed in Section the section on Trade.

			In contrast to the Negishi approach, which solves for a co-operative Pareto solution, the Nash approach solves for a non-cooperative Pareto solution. The cooperative solution internalizes interregional spillovers between regions by optimizing the global welfare by using Joint Maximization. The non-cooperative solution considers spillovers as well, but they are not internalized. The relevant externalities are the technology learning effects in the energy sector.











			<references />

Lavinia Baumstark: adjust to REMIND-MAgPIE

2022-01-25T10:13:58Z

adjust to REMIND-MAgPIE

← Older revision		Revision as of 12:13, 25 January 2022
Line 4:		Line 4:
	\|DocumentationCategory=Macro-economy		\|DocumentationCategory=Macro-economy
	}}		}}
	~~==Objective function==~~

	~~REMIND-MAgPIE models each region r as a representative household with a utility function Ur that depends upon per-capita consumption~~

	~~<figure id="fig:REMIND-MAgPIE_3.2.1 1.">~~
	~~[[File:REMIND-MAgPIE equation 3.2.1 1.JPG]]~~
	~~</figure>~~

	where C(r,t) is the consumption of region r at time t, and P(r,t) is the population in region r at time t. The calculation of utility is subject to discounting; 3% is assumed for the pure rate of time preference rho. The logarithmic relationship between per-capita consumption and regional utility implies an elasticity of marginal consumption of 1. Thus, in line with the Keynes-Ramsey rule, REMIND-MAgPIE yields an endogenous interest rate in real terms of 5–6% for an economic growth rate of 2–3%. This is in line with the interest rates typically observed on capital markets.

	REMIND-MAgPIE can compute maximum regional utility (welfare) by two different solution concepts – the Negishi approach and the Nash approach <ref>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</ref>. In the Negishi approach, which computes a cooperative solution, the objective of the Joint Maximization Problem is the weighted sum of regional utilities, maximized subject to all other constraints:

	~~<figure id="fig:REMIND-MAgPIE_3.2.1 2.">~~
	~~[[File:REMIND-MAgPIE equation 3.2.1 2.JPG]]~~
	~~</figure>~~

	An iterative algorithm adjusts the weights so as to equalize the intertemporal balance of payments of each region over the entire time horizon. This convergence criterion ensures that the Pareto-optimal solution of the model corresponds with the market equilibrium in the absence of non-internalized externalities. The algorithm is an inter-temporal extension of the original Negishi approach <ref>Negishi T (1972) General equilibrium theory and international trade. North-Holland Publishing Company Amsterdam, London</ref>; see also <ref>Manne AS, Rutherford TF (1994) International Trade in Oil, Gas and Carbon Emission Rights: An Intertemporal General Equilibrium Model. The Energy Journal Volume15:57–76</ref> for a discussion of the extension. Other models such as MERGE <ref>Manne A, Mendelsohn R, Richels R (1995) MERGE: A model for evaluating regional and global effects of GHG reduction policies. Energy Policy 23:17–34. doi: 10.1016/0301-4215(95)90763-W</ref> and RICE <ref>Nordhaus WD, Yang Z (1996) A Regional Dynamic General-Equilibrium Model of Alternative Climate-Change Strategies. The American Economic Review 86:741–765</ref> use this algorithm in a similar way.

	The Nash solution concept, by contrast, arrives at the Pareto solution not by Joint Maximization, but by maximizing the regional welfare subject to regional constraints and international prices that are taken as exogenous data for each region. The intertemporal balance of payments of each region has to equal zero and is one particular constraint imposed on each region. The equilibrium solution is found by iteratively adjusting the international prices until global demand and supply are balanced on each market. The choice of the solution concept is also important for the representation of trade, as discussed in Section the section on Trade.

	In contrast to the Negishi approach, which solves for a co-operative Pareto solution, the Nash approach solves for a non-cooperative Pareto solution. The cooperative solution internalizes interregional spillovers between regions by optimizing the global welfare by using Joint Maximization. The non-cooperative solution considers spillovers as well, but they are not internalized. The relevant externalities are the technology learning effects in the energy sector.











	~~<references />~~

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

2021-11-21T13:34:22Z

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

← Older revision		Revision as of 15:34, 21 November 2021
Line 1:		Line 1:
	{{ModelDocumentationTemplate		{{ModelDocumentationTemplate
	\|IsEmpty=No		\|IsEmpty=No
	\|IsDocumentationOf=REMIND		\|IsDocumentationOf=REMIND-MAgPIE
	\|DocumentationCategory=Macro-economy		\|DocumentationCategory=Macro-economy
	}}		}}
	==Objective function==		==Objective function==

	REMIND models each region r as a representative household with a utility function Ur that depends upon per-capita consumption		REMIND-MAgPIE models each region r as a representative household with a utility function Ur that depends upon per-capita consumption

	<figure id="fig:~~REMIND_3~~.2.1 1.">		<figure id="fig:REMIND-MAgPIE_3.2.1 1.">
	[[File:REMIND equation 3.2.1 1.JPG]]		[[File:REMIND-MAgPIE equation 3.2.1 1.JPG]]
	</figure>		</figure>

	where C(r,t) is the consumption of region r at time t, and P(r,t) is the population in region r at time t. The calculation of utility is subject to discounting; 3% is assumed for the pure rate of time preference rho. The logarithmic relationship between per-capita consumption and regional utility implies an elasticity of marginal consumption of 1. Thus, in line with the Keynes-Ramsey rule, REMIND yields an endogenous interest rate in real terms of 5–6% for an economic growth rate of 2–3%. This is in line with the interest rates typically observed on capital markets.		where C(r,t) is the consumption of region r at time t, and P(r,t) is the population in region r at time t. The calculation of utility is subject to discounting; 3% is assumed for the pure rate of time preference rho. The logarithmic relationship between per-capita consumption and regional utility implies an elasticity of marginal consumption of 1. Thus, in line with the Keynes-Ramsey rule, REMIND-MAgPIE yields an endogenous interest rate in real terms of 5–6% for an economic growth rate of 2–3%. This is in line with the interest rates typically observed on capital markets.

	REMIND can compute maximum regional utility (welfare) by two different solution concepts – the Negishi approach and the Nash approach <ref>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</ref>. In the Negishi approach, which computes a cooperative solution, the objective of the Joint Maximization Problem is the weighted sum of regional utilities, maximized subject to all other constraints:		REMIND-MAgPIE can compute maximum regional utility (welfare) by two different solution concepts – the Negishi approach and the Nash approach <ref>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</ref>. In the Negishi approach, which computes a cooperative solution, the objective of the Joint Maximization Problem is the weighted sum of regional utilities, maximized subject to all other constraints:

	<figure id="fig:~~REMIND_3~~.2.1 2.">		<figure id="fig:REMIND-MAgPIE_3.2.1 2.">
	[[File:REMIND equation 3.2.1 2.JPG]]		[[File:REMIND-MAgPIE equation 3.2.1 2.JPG]]
	</figure>		</figure>

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

2021-11-18T12:14:03Z

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

← Older revision

Revision as of 14:14, 18 November 2021

Laura Delsa at 10:50, 9 June 2017

2017-06-09T10:50:51Z

← Older revision		Revision as of 12:50, 9 June 2017
Line 14:		Line 14:
	where C(r,t) is the consumption of region r at time t, and P(r,t) is the population in region r at time t. The calculation of utility is subject to discounting; 3% is assumed for the pure rate of time preference rho. The logarithmic relationship between per-capita consumption and regional utility implies an elasticity of marginal consumption of 1. Thus, in line with the Keynes-Ramsey rule, REMIND yields an endogenous interest rate in real terms of 5–6% for an economic growth rate of 2–3%. This is in line with the interest rates typically observed on capital markets.		where C(r,t) is the consumption of region r at time t, and P(r,t) is the population in region r at time t. The calculation of utility is subject to discounting; 3% is assumed for the pure rate of time preference rho. The logarithmic relationship between per-capita consumption and regional utility implies an elasticity of marginal consumption of 1. Thus, in line with the Keynes-Ramsey rule, REMIND yields an endogenous interest rate in real terms of 5–6% for an economic growth rate of 2–3%. This is in line with the interest rates typically observed on capital markets.

	REMIND can compute maximum regional utility (welfare) by two different solution concepts – the Negishi approach and the Nash approach <ref>Leimbach M, Schultes A, Baumstark L, et al (2016) Solution algorithms of large‐scale Integrated Assessment models on climate change. Annals of Operations Research, ~~in press~~</ref>. In the Negishi approach, which computes a cooperative solution, the objective of the Joint Maximization Problem is the weighted sum of regional utilities, maximized subject to all other constraints:		REMIND can compute maximum regional utility (welfare) by two different solution concepts – the Negishi approach and the Nash approach <ref>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</ref>. In the Negishi approach, which computes a cooperative solution, the objective of the Joint Maximization Problem is the weighted sum of regional utilities, maximized subject to all other constraints:

	<figure id="fig:REMIND_3.2.1 2.">		<figure id="fig:REMIND_3.2.1 2.">

Laura Delsa at 10:37, 3 February 2017

2017-02-03T10:37:33Z

← Older revision		Revision as of 12:37, 3 February 2017
Line 36:		Line 36:


	<~~rferences~~ />		<references />

Laura Delsa at 10:37, 3 February 2017

2017-02-03T10:37:14Z

← Older revision		Revision as of 12:37, 3 February 2017
Line 25:		Line 25:

	In contrast to the Negishi approach, which solves for a co-operative Pareto solution, the Nash approach solves for a non-cooperative Pareto solution. The cooperative solution internalizes interregional spillovers between regions by optimizing the global welfare by using Joint Maximization. The non-cooperative solution considers spillovers as well, but they are not internalized. The relevant externalities are the technology learning effects in the energy sector.		In contrast to the Negishi approach, which solves for a co-operative Pareto solution, the Nash approach solves for a non-cooperative Pareto solution. The cooperative solution internalizes interregional spillovers between regions by optimizing the global welfare by using Joint Maximization. The non-cooperative solution considers spillovers as well, but they are not internalized. The relevant externalities are the technology learning effects in the energy sector.











			<rferences />

Laura Delsa at 10:36, 3 February 2017

2017-02-03T10:36:37Z

← Older revision		Revision as of 12:36, 3 February 2017
Line 20:		Line 20:
	</figure>		</figure>

	An iterative algorithm adjusts the weights so as to equalize the intertemporal balance of payments of each region over the entire time horizon. This convergence criterion ensures that the Pareto-optimal solution of the model corresponds with the market equilibrium in the absence of non-internalized externalities. The algorithm is an inter-temporal extension of the original Negishi approach <ref>Negishi T (1972) General equilibrium theory and international trade. North-Holland Publishing Company Amsterdam, London</ref>; see also <ref>Manne AS, Rutherford TF (1994) International Trade in Oil, Gas and Carbon Emission Rights: An Intertemporal General Equilibrium Model. The Energy Journal Volume15:57–76</ref> for a discussion of the extension. Other models such as MERGE <ref>Manne A, Mendelsohn R, Richels R (1995) MERGE: A model for evaluating regional and global effects of GHG reduction policies. Energy Policy 23:17–34. doi: 10.1016/0301-4215(95)90763-W</ref> and RICE <ref>Nordhaus ~~and~~ Yang 1996</ref> use this algorithm in a similar way.		An iterative algorithm adjusts the weights so as to equalize the intertemporal balance of payments of each region over the entire time horizon. This convergence criterion ensures that the Pareto-optimal solution of the model corresponds with the market equilibrium in the absence of non-internalized externalities. The algorithm is an inter-temporal extension of the original Negishi approach <ref>Negishi T (1972) General equilibrium theory and international trade. North-Holland Publishing Company Amsterdam, London</ref>; see also <ref>Manne AS, Rutherford TF (1994) International Trade in Oil, Gas and Carbon Emission Rights: An Intertemporal General Equilibrium Model. The Energy Journal Volume15:57–76</ref> for a discussion of the extension. Other models such as MERGE <ref>Manne A, Mendelsohn R, Richels R (1995) MERGE: A model for evaluating regional and global effects of GHG reduction policies. Energy Policy 23:17–34. doi: 10.1016/0301-4215(95)90763-W</ref> and RICE <ref>Nordhaus WD, Yang Z (1996) A Regional Dynamic General-Equilibrium Model of Alternative Climate-Change Strategies. The American Economic Review 86:741–765</ref> use this algorithm in a similar way.

	The Nash solution concept, by contrast, arrives at the Pareto solution not by Joint Maximization, but by maximizing the regional welfare subject to regional constraints and international prices that are taken as exogenous data for each region. The intertemporal balance of payments of each region has to equal zero and is one particular constraint imposed on each region. The equilibrium solution is found by iteratively adjusting the international prices until global demand and supply are balanced on each market. The choice of the solution concept is also important for the representation of trade, as discussed in Section the section on Trade.		The Nash solution concept, by contrast, arrives at the Pareto solution not by Joint Maximization, but by maximizing the regional welfare subject to regional constraints and international prices that are taken as exogenous data for each region. The intertemporal balance of payments of each region has to equal zero and is one particular constraint imposed on each region. The equilibrium solution is found by iteratively adjusting the international prices until global demand and supply are balanced on each market. The choice of the solution concept is also important for the representation of trade, as discussed in Section the section on Trade.

	In contrast to the Negishi approach, which solves for a co-operative Pareto solution, the Nash approach solves for a non-cooperative Pareto solution. The cooperative solution internalizes interregional spillovers between regions by optimizing the global welfare by using Joint Maximization. The non-cooperative solution considers spillovers as well, but they are not internalized. The relevant externalities are the technology learning effects in the energy sector.		In contrast to the Negishi approach, which solves for a co-operative Pareto solution, the Nash approach solves for a non-cooperative Pareto solution. The cooperative solution internalizes interregional spillovers between regions by optimizing the global welfare by using Joint Maximization. The non-cooperative solution considers spillovers as well, but they are not internalized. The relevant externalities are the technology learning effects in the energy sector.