Technological change in energy - IMAGE - Revision history

Rineke Oostenrijk: Text replacement - "|QQQ " to ""

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Rineke Oostenrijk: Text replacement - "|HasSeq=5 " to ""

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Rineke Oostenrijk: Text replacement - "|HasLevel=2 " to ""

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Rineke Oostenrijk at 12:43, 1 April 2020

2020-04-01T12:43:12Z

← Older revision		Revision as of 14:43, 1 April 2020
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	An important aspect of TIMER is the endogenous formulation of technology development, on the basis of learning by doing, which is considered to be a meaningful representation of technology change in global energy models [[CiteRef::IMG_Azar_1999]][[CiteRef::IMG_Grubler_1999]][[CiteRef::IMG_IEA_2000]]. The general formulation of ''learning by doing'' in a model context is that a cost measure y tends to decline as a power function of an accumulated learning measure, where n is the learning rate, Q the cumulative capacity or output, and C is a constant:		An important aspect of TIMER is the endogenous formulation of technology development, on the basis of learning by doing, which is considered to be a meaningful representation of technology change in global energy models [[CiteRef::IMG_Azar_1999]][[CiteRef::IMG_Grubler_1999]][[CiteRef::IMG_IEA_2000]]. The general formulation of ''learning by doing'' in a model context is that a cost measure y tends to decline as a power function of an accumulated learning measure, where n is the learning rate, Q the cumulative capacity or output, and C is a constant:

	<math>[Y = C * Q^{-n}]</math>		<math>Y = C * Q^{-n}</math>

	Often n is expressed by the progress ratio p, which indicates how fast the costs metric Y decreases with doubling of Q (p=2-n). Progress ratios reported in empirical studies are mostly between 0.65 and 0.95, with a median value of 0.82 [[CiteRef::IMG_Argote_1990]].		Often n is expressed by the progress ratio p, which indicates how fast the costs metric Y decreases with doubling of Q (p=2-n). Progress ratios reported in empirical studies are mostly between 0.65 and 0.95, with a median value of 0.82 [[CiteRef::IMG_Argote_1990]].
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	The indicated market share (IMS) of a technology is determined using a multinomial logit model that assigns market shares to the different technologies (i) on the basis of their relative prices in a set of competing technologies (j).		The indicated market share (IMS) of a technology is determined using a multinomial logit model that assigns market shares to the different technologies (i) on the basis of their relative prices in a set of competing technologies (j).

	<math>[MS_{i}=\frac{e^{\lambda x_{i}}}{\sum_je^{\lambda c_{j}}}]</math>		<math>MS_{i}=\frac{e^{\lambda x_{i}}}{\sum_je^{\lambda c_{j}}}</math>

	MS is the market share of different technologies and c is their costs. In this equation, is the so-called logit parameter, determining the sensitivity of markets to price differences.		MS is the market share of different technologies and c is their costs. In this equation, is the so-called logit parameter, determining the sensitivity of markets to price differences.

	The equation takes account of direct costs and also energy and carbon taxes and premium values. The last two reflect non-price factors determining market shares, such as preferences, environmental policies, infrastructure (or the lack of infrastructure) and strategic considerations. The premium values are determined in the model calibration process in order to correctly simulate historical market shares on the basis of simulated price information. The same parameters are used in scenarios to simulate the assumption on societal preferences for clean and/or convenient fuels.		The equation takes account of direct costs and also energy and carbon taxes and premium values. The last two reflect non-price factors determining market shares, such as preferences, environmental policies, infrastructure (or the lack of infrastructure) and strategic considerations. The premium values are determined in the model calibration process in order to correctly simulate historical market shares on the basis of simulated price information. The same parameters are used in scenarios to simulate the assumption on societal preferences for clean and/or convenient fuels.

Rineke Oostenrijk at 12:41, 1 April 2020

2020-04-01T12:41:27Z

← Older revision		Revision as of 14:41, 1 April 2020
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	An important aspect of TIMER is the endogenous formulation of technology development, on the basis of learning by doing, which is considered to be a meaningful representation of technology change in global energy models [[CiteRef::IMG_Azar_1999]][[CiteRef::IMG_Grubler_1999]][[CiteRef::IMG_IEA_2000]]. The general formulation of ''learning by doing'' in a model context is that a cost measure y tends to decline as a power function of an accumulated learning measure, where n is the learning rate, Q the cumulative capacity or output, and C is a constant:		An important aspect of TIMER is the endogenous formulation of technology development, on the basis of learning by doing, which is considered to be a meaningful representation of technology change in global energy models [[CiteRef::IMG_Azar_1999]][[CiteRef::IMG_Grubler_1999]][[CiteRef::IMG_IEA_2000]]. The general formulation of ''learning by doing'' in a model context is that a cost measure y tends to decline as a power function of an accumulated learning measure, where n is the learning rate, Q the cumulative capacity or output, and C is a constant:

	<math>\[Y = C * Q^{-n}\]</math>		<math>[Y = C * Q^{-n}]</math>

	Often n is expressed by the progress ratio p, which indicates how fast the costs metric Y decreases with doubling of Q (p=2-n). Progress ratios reported in empirical studies are mostly between 0.65 and 0.95, with a median value of 0.82 [[CiteRef::IMG_Argote_1990]].		Often n is expressed by the progress ratio p, which indicates how fast the costs metric Y decreases with doubling of Q (p=2-n). Progress ratios reported in empirical studies are mostly between 0.65 and 0.95, with a median value of 0.82 [[CiteRef::IMG_Argote_1990]].
Line 27:		Line 27:
	The indicated market share (IMS) of a technology is determined using a multinomial logit model that assigns market shares to the different technologies (i) on the basis of their relative prices in a set of competing technologies (j).		The indicated market share (IMS) of a technology is determined using a multinomial logit model that assigns market shares to the different technologies (i) on the basis of their relative prices in a set of competing technologies (j).

	<math>\[MS_{i}=\frac{e^{\lambda x_{i}}}{\sum_je^{\lambda c_{j}}}\]</math>		<math>[MS_{i}=\frac{e^{\lambda x_{i}}}{\sum_je^{\lambda c_{j}}}]</math>

	MS is the market share of different technologies and c is their costs. In this equation, is the so-called logit parameter, determining the sensitivity of markets to price differences.		MS is the market share of different technologies and c is their costs. In this equation, is the so-called logit parameter, determining the sensitivity of markets to price differences.

	The equation takes account of direct costs and also energy and carbon taxes and premium values. The last two reflect non-price factors determining market shares, such as preferences, environmental policies, infrastructure (or the lack of infrastructure) and strategic considerations. The premium values are determined in the model calibration process in order to correctly simulate historical market shares on the basis of simulated price information. The same parameters are used in scenarios to simulate the assumption on societal preferences for clean and/or convenient fuels.		The equation takes account of direct costs and also energy and carbon taxes and premium values. The last two reflect non-price factors determining market shares, such as preferences, environmental policies, infrastructure (or the lack of infrastructure) and strategic considerations. The premium values are determined in the model calibration process in order to correctly simulate historical market shares on the basis of simulated price information. The same parameters are used in scenarios to simulate the assumption on societal preferences for clean and/or convenient fuels.

Oreane Edelenbosch at 15:40, 12 January 2017

2017-01-12T15:40:07Z

← Older revision		Revision as of 17:40, 12 January 2017
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	The equation takes account of direct costs and also energy and carbon taxes and premium values. The last two reflect non-price factors determining market shares, such as preferences, environmental policies, infrastructure (or the lack of infrastructure) and strategic considerations. The premium values are determined in the model calibration process in order to correctly simulate historical market shares on the basis of simulated price information. The same parameters are used in scenarios to simulate the assumption on societal preferences for clean and/or convenient fuels.		The equation takes account of direct costs and also energy and carbon taxes and premium values. The last two reflect non-price factors determining market shares, such as preferences, environmental policies, infrastructure (or the lack of infrastructure) and strategic considerations. The premium values are determined in the model calibration process in order to correctly simulate historical market shares on the basis of simulated price information. The same parameters are used in scenarios to simulate the assumption on societal preferences for clean and/or convenient fuels.

	~~==Technology change in agriculture==~~

	The management factor (MF) describes the actual yield per crop group and per socio-economic region as a proportion of the maximum potential yield. This maximum potential yield is estimated taking into account inhomogeneous soil and climate data across grid cells. The MF for the period up to 2005 is estimated as part of the IMAGE calibration procedure, using FAO statistics on actual crop yields and crop areas [[CiteRef::IMG_FAO_2013]]. The start year for the MF is subsequently taken as point of departure for future projections.

	Guidance for future development of yield changes is provided by expert projection such as the assumptions in FAO projections up to 2030 and 2050 [[CiteRef::IMG_Bruinsma_2003]][[CiteRef::IMG_Alexandratos_2012]].The FAO trends are used as exogenous technical development in the MAGNET model, and subsequently adjusted to reflect the relative shortage of suitable land, as part of the model calculation. The combinations of production volumes and land areas from MAGNET are adopted as future MF projections into the future in IMAGE.

	Future technological change is dependent on the storyline and needs to be consistent with other scenario drivers. For instance, strong economic growth is typically facilitated by rapid technology development and deployment, rising wages and a labour shift from primary production (agriculture) to secondary (industry) and tertiary (services) sectors. These developments foster more advanced management and technology in agriculture. In order to reflect different trends in exogenous yield increase, FAO trends are combined with projections of economic growth to develop scenario-specific trends of yield changes in multiple-baseline studies, like for the SSPs. Because the MF is such a decisive factor in future net agricultural land area, careful consideration of uncertainties is warranted.

Harmen Sytze de Boer at 09:34, 9 August 2016

2016-08-09T09:34:22Z

← Older revision		Revision as of 11:34, 9 August 2016
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	An important aspect of TIMER is the endogenous formulation of technology development, on the basis of learning by doing, which is considered to be a meaningful representation of technology change in global energy models [[CiteRef::IMG_Azar_1999]][[CiteRef::IMG_Grubler_1999]][[CiteRef::IMG_IEA_2000]]. The general formulation of ''learning by doing'' in a model context is that a cost measure y tends to decline as a power function of an accumulated learning measure, where n is the learning rate, Q the cumulative capacity or output, and C is a constant:		An important aspect of TIMER is the endogenous formulation of technology development, on the basis of learning by doing, which is considered to be a meaningful representation of technology change in global energy models [[CiteRef::IMG_Azar_1999]][[CiteRef::IMG_Grubler_1999]][[CiteRef::IMG_IEA_2000]]. The general formulation of ''learning by doing'' in a model context is that a cost measure y tends to decline as a power function of an accumulated learning measure, where n is the learning rate, Q the cumulative capacity or output, and C is a constant:

	<math> Y = C * Q^n\,</math>		<math>\[Y = C * Q^{-n}\]</math>

	Often n is expressed by the progress ratio p, which indicates how fast the costs metric Y decreases with doubling of Q (p=2-n). Progress ratios reported in empirical studies are mostly between 0.65 and 0.95, with a median value of 0.82 [[CiteRef::IMG_Argote_1990]].		Often n is expressed by the progress ratio p, which indicates how fast the costs metric Y decreases with doubling of Q (p=2-n). Progress ratios reported in empirical studies are mostly between 0.65 and 0.95, with a median value of 0.82 [[CiteRef::IMG_Argote_1990]].
Line 27:		Line 27:
	The indicated market share (IMS) of a technology is determined using a multinomial logit model that assigns market shares to the different technologies (i) on the basis of their relative prices in a set of competing technologies (j).		The indicated market share (IMS) of a technology is determined using a multinomial logit model that assigns market shares to the different technologies (i) on the basis of their relative prices in a set of competing technologies (j).

	<math> ~~MS<sub>~~i~~<\sub>~~ = ~~exp(&lambda x<sub>i<\sub>)~~ \frac \~~sum_~~{~~n=0~~} ~~exp(&~~lambda ~~c<sub>~~j<~~\sub~~>)		<math>\[MS_{i}=\frac{e^{\lambda x_{i}}}{\sum_je^{\lambda c_{j}}}\]</math>

	MS is the market share of different technologies and c is their costs. In this equation, is the so-called logit parameter, determining the sensitivity of markets to price differences.		MS is the market share of different technologies and c is their costs. In this equation, is the so-called logit parameter, determining the sensitivity of markets to price differences.

Harmen Sytze de Boer at 08:58, 9 August 2016

2016-08-09T08:58:43Z

← Older revision		Revision as of 10:58, 9 August 2016
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	<math> MS<sub>i<\sub> = exp(&lambda x<sub>i<\sub>) \frac \sum_{n=0} exp(&lambda c<sub>j<\sub>)		<math> MS<sub>i<\sub> = exp(&lambda x<sub>i<\sub>) \frac \sum_{n=0} exp(&lambda c<sub>j<\sub>)

	MS is the market share of different technologies and c is their costs. In this equation, ? is the so-called logit parameter, determining the sensitivity of markets to price differences.		MS is the market share of different technologies and c is their costs. In this equation, is the so-called logit parameter, determining the sensitivity of markets to price differences.

	The equation takes account of direct costs and also energy and carbon taxes and premium values. The last two reflect non-price factors determining market shares, such as preferences, environmental policies, infrastructure (or the lack of infrastructure) and strategic considerations. The premium values are determined in the model calibration process in order to correctly simulate historical market shares on the basis of simulated price information. The same parameters are used in scenarios to simulate the assumption on societal preferences for clean and/or convenient fuels.		The equation takes account of direct costs and also energy and carbon taxes and premium values. The last two reflect non-price factors determining market shares, such as preferences, environmental policies, infrastructure (or the lack of infrastructure) and strategic considerations. The premium values are determined in the model calibration process in order to correctly simulate historical market shares on the basis of simulated price information. The same parameters are used in scenarios to simulate the assumption on societal preferences for clean and/or convenient fuels.

Harmen Sytze de Boer at 07:58, 9 August 2016

2016-08-09T07:58:42Z

← Older revision		Revision as of 09:58, 9 August 2016
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	==Technology change in agriculture==		==Technology change in agriculture==

	The management factor (MF) describes the actual yield per crop group and per socio-economic region as a proportion of the maximum potential yield. This maximum potential yield is estimated taking into account inhomogeneous soil and climate data across grid cells. The MF for the period up to 2005 is estimated as part of the IMAGE calibration procedure, using FAO statistics on actual crop yields and crop areas [[CiteRef::~~IMG_FAO_2013a~~]]. The start year for the MF is subsequently taken as point of departure for future projections.		The management factor (MF) describes the actual yield per crop group and per socio-economic region as a proportion of the maximum potential yield. This maximum potential yield is estimated taking into account inhomogeneous soil and climate data across grid cells. The MF for the period up to 2005 is estimated as part of the IMAGE calibration procedure, using FAO statistics on actual crop yields and crop areas [[CiteRef::IMG_FAO_2013]]. The start year for the MF is subsequently taken as point of departure for future projections.

	Guidance for future development of yield changes is provided by expert projection such as the assumptions in FAO projections up to 2030 and 2050 [[CiteRef::IMG_Bruinsma_2003]][[CiteRef::IMG_Alexandratos_2012]].The FAO trends are used as exogenous technical development in the MAGNET model, and subsequently adjusted to reflect the relative shortage of suitable land, as part of the model calculation. The combinations of production volumes and land areas from MAGNET are adopted as future MF projections into the future in IMAGE.		Guidance for future development of yield changes is provided by expert projection such as the assumptions in FAO projections up to 2030 and 2050 [[CiteRef::IMG_Bruinsma_2003]][[CiteRef::IMG_Alexandratos_2012]].The FAO trends are used as exogenous technical development in the MAGNET model, and subsequently adjusted to reflect the relative shortage of suitable land, as part of the model calculation. The combinations of production volumes and land areas from MAGNET are adopted as future MF projections into the future in IMAGE.

	Future technological change is dependent on the storyline and needs to be consistent with other scenario drivers. For instance, strong economic growth is typically facilitated by rapid technology development and deployment, rising wages and a labour shift from primary production (agriculture) to secondary (industry) and tertiary (services) sectors. These developments foster more advanced management and technology in agriculture. In order to reflect different trends in exogenous yield increase, FAO trends are combined with projections of economic growth to develop scenario-specific trends of yield changes in multiple-baseline studies, like for the SSPs. Because the MF is such a decisive factor in future net agricultural land area, careful consideration of uncertainties is warranted.		Future technological change is dependent on the storyline and needs to be consistent with other scenario drivers. For instance, strong economic growth is typically facilitated by rapid technology development and deployment, rising wages and a labour shift from primary production (agriculture) to secondary (industry) and tertiary (services) sectors. These developments foster more advanced management and technology in agriculture. In order to reflect different trends in exogenous yield increase, FAO trends are combined with projections of economic growth to develop scenario-specific trends of yield changes in multiple-baseline studies, like for the SSPs. Because the MF is such a decisive factor in future net agricultural land area, careful consideration of uncertainties is warranted.