Non-biomass renewables - C3IAM: Difference between revisions

Revision as of 10:24, 10 August 2021

Model Documentation - C3IAM
Model scope and methods - C3IAM Model concept, solver and details - C3IAM Temporal dimension - C3IAM Spatial dimension - C3IAM Policy - C3IAM Socio-economic drivers - C3IAM Population - C3IAM Economic activity - C3IAM Macro-economy - C3IAM Production system and representation of economic sectors - C3IAM Capital and labour markets - C3IAM Monetary instruments - C3IAM Trade - C3IAM Technological change - C3IAM Energy - C3IAM Energy resource endowments - C3IAM Fossil energy resources - C3IAM Uranium and other fissile resources - C3IAM Bioenergy - C3IAM Non-biomass renewables - C3IAM Energy conversion - C3IAM Electricity - C3IAM Heat - C3IAM Gaseous fuels - C3IAM Liquid fuels - C3IAM Solid fuels - C3IAM Grid, pipelines and other infrastructure - C3IAM Energy end-use - C3IAM Transport - C3IAM Residential and commercial sectors - C3IAM Industrial sector - C3IAM Other end-use - C3IAM Energy demand - C3IAM Technological change in energy - C3IAM Land-use - C3IAM Agriculture - C3IAM Forestry - C3IAM Land-use change - C3IAM Bioenergy land-use - C3IAM Other land-use - C3IAM Agricultural demand - C3IAM Technological change in land-use - C3IAM Emissions - C3IAM GHGs - C3IAM Pollutants and non-GHG forcing agents - C3IAM Carbon dioxide removal (CDR) options - C3IAM Climate - C3IAM Modelling of climate indicators - C3IAM Climate damages, temperature changes - C3IAM Non-climate sustainability dimension - C3IAM Air pollution and health - C3IAM Water - C3IAM Other materials - C3IAM Other sustainability dimensions - C3IAM Appendices - C3IAM Mathematical model description - C3IAM Data - C3IAM References - C3IAM
Corresponding documentation
AIM-Hub BLUES C3IAM COFFEE-TEA DNE21+ GCAM GRACE IFs MESSAGE-GLOBIOM POLES PROMETHEUS REMIND-MAgPIE TIAM-UCL
Previous versions
Archive of C3IAM version 2.0
Model information
Model link	http://inems1.bit.edu.cn/C3IAM https://ideas.repec.org/a/spr/nathaz/v92y2018i2d10.1007 s11069-018-3297-9.html https://www.nature.com/articles/s41467-020-15453-z
Institution	Center for Energy and Environmental Policy Research, Beijing Institute of Technology (CEEP-BIT), China, http://ceep.bit.edu.cn/english/.
Solution concept	General equilibrium (closed economy)
Solution method	Optimization
Anticipation

C³IAM uses an additional production input called the ‘fixed factor’ to describes the representative capacity building constraint of energy technologies. The fixed factor is used to represent the specialized resources that are required for capacity building such as knowledgeable engineering, specialized manufacturing and services. The price of the fixed factor will therefore affect the rate at which this technology enters the market. If the demand for the technology is high, the fixed factor price representing the limited resources will also be high, thereby limiting the initial rate of expansion of production from new energy technologies and taking into consideration the adjustment costs ^[1].

The fixed factor also represents innovation in the form of learning-by-doing, which shows that the constraint is less binding as production and experience is gained. The representative agent is endowed with a very small amount of the particular fixed factor resource for each technology in the base year^[2], and $FF_{t}$ for the base year is 0.00001 in the model. The amount of fixed factor then is increased as a function of cumulative production of that technology, representing cost reduction as we learn and gain experience. The equation for endowment is based on the forms in McFarland et al. (2004)^[3], Jacoby et al. (2005) ^[2] and Ereira et al. (2010) ^[1]:

$FF_{t+1}=FF_{t}+({\alpha }{Y_{t}}^{\gamma }+\lambda {Y_{t}}^{\zeta })$

Where $Y_{t}$ is the electricity output for a given technology in period t. $\alpha {Y_{t}}^{\gamma }$ is approximately linear with $\gamma =0.8~0.9$ and $\alpha =0.01$ . This term governs the growth of the fixed factor at low levels of output ${Y_{t}}^{\gamma }$ as $\alpha >>\lambda$ . The term $\lambda {Y_{t}}^{\zeta }$ accelerates fixed factor growth at high levels of output as $\lambda =0.00001$ and $\zeta =2.0~2.2$ ^[3].

References

↑ ^1.0 ^1.1 Eleanor Charlotte Ereira, 2010. Assessing early investments in low carbon technologies under uncertainty: the case of Carbon Capture and Storage. Massachusetts Institute of Technology.
↑ ^2.0 ^2.1 Henry D Jacoby, John M Reilly, James R McFarland, Sergey Paltsev, 2006. Technology and technical change in the MIT EPPA model. Energy Economics 28, 610-631.
↑ ^3.0 ^3.1 J.R. McFarland, J.M. Reilly, H.J. Herzog, 2004. Representing energy technologies in top-down economic models using bottom-up information. Energy Economics. 26, 685–707.

[:0-1] 1.0 ^1.1 Eleanor Charlotte Ereira, 2010. Assessing early investments in low carbon technologies under uncertainty: the case of Carbon Capture and Storage. Massachusetts Institute of Technology.

[:1-2] 2.0 ^2.1 Henry D Jacoby, John M Reilly, James R McFarland, Sergey Paltsev, 2006. Technology and technical change in the MIT EPPA model. Energy Economics 28, 610-631.

[:2-3] 3.0 ^3.1 J.R. McFarland, J.M. Reilly, H.J. Herzog, 2004. Representing energy technologies in top-down economic models using bottom-up information. Energy Economics. 26, 685–707.

[1]

[2]

[3]

@@ Line 6: / Line 6: @@
 C<sup>3</sup>IAM uses an additional production input called the ‘fixed factor’ to describes the representative capacity building constraint of energy technologies. The fixed factor is used to represent the specialized resources that are required for capacity building such as knowledgeable engineering, specialized manufacturing and services. The price of the fixed factor will therefore affect the rate at which this technology enters the market. If the demand for the technology is high, the fixed factor price representing the limited resources will also be high, thereby limiting the initial rate of expansion of production from new energy technologies and taking into consideration the adjustment costs <ref name=":0">Eleanor Charlotte Ereira, 2010. Assessing early investments in low carbon technologies under uncertainty: the case of Carbon Capture and Storage. ''Massachusetts Institute of Technology''.</ref>.
-The fixed factor also represents innovation in the form of learning-by-doing, which shows that the constraint is less binding as production and experience is gained. The representative agent is endowed with a very small amount of the particular fixed factor resource for each technology in the base year<ref name=":1">Henry D Jacoby, John M Reilly, James R McFarland, Sergey Paltsev, 2006. Technology and technical change in the MIT EPPA model. ''Energy Economics'' 28, 610-631.</ref>, and<math>FF_t</math>for the base year is 0.00001 in the model. The amount of fixed factor then is increased as a function of cumulative production of that technology, representing cost reduction as we learn and gain experience. The equation for endowment follows the form of Jacoby et al. (2005) <ref name=":1" /> and Ereira et al. (2010) <ref name=":0" />:
+The fixed factor also represents innovation in the form of learning-by-doing, which shows that the constraint is less binding as production and experience is gained. The representative agent is endowed with a very small amount of the particular fixed factor resource for each technology in the base year<ref name=":1">Henry D Jacoby, John M Reilly, James R McFarland, Sergey Paltsev, 2006. Technology and technical change in the MIT EPPA model. ''Energy Economics'' 28, 610-631.</ref>, and<math>FF_t</math>for the base year is 0.00001 in the model. The amount of fixed factor then is increased as a function of cumulative production of that technology, representing cost reduction as we learn and gain experience. The equation for endowment is based on the forms in McFarland et al. (2004)<ref name=":2">J.R. McFarland, J.M. Reilly, H.J. Herzog, 2004. Representing energy technologies in top-down economic models using bottom-up information. ''Energy Economics''. 26, 685–707.</ref>, Jacoby et al. (2005) <ref name=":1" /> and Ereira et al. (2010) <ref name=":0" />:
 <br><br>
-<math>FF_{t+1}=FF_t+c(aY_t+b{Y_t}^2)</math>
+<math>FF_{t+1}=FF_t+({\alpha}{Y_t}^\gamma+\lambda{Y_t}^\zeta)</math>
 <br><br>
-Where   <math>Y_t</math>is the electricity output for a given technology in period t. a and b are coefficients that determine the rate of penetration for the technology, empirically found to reflect an energy technology adoption rate of quadratic form that results in the same qualitative ‘S-shape’ curve as penetration rates found in the literature. The share is set at 0.01 for all the advanced energy technologies in the model. c is a product of the fixed factor share, the mark-up of the technology, and the reciprocal of the electricity price by region.
+Where   <math>Y_t</math>is the electricity output for a given technology in period t. <math>\alpha{Y_t}^\gamma</math>is approximately linear with  <math>\gamma=0.8~0.9</math>and <math>\alpha=0.01</math>. This term governs the growth of the fixed factor at low levels of output <math>{Y_t}^\gamma</math>as<math>\alpha>>\lambda</math>. The term <math>\lambda{Y_t}^\zeta</math>accelerates fixed factor growth at high levels of output as <math>\lambda=0.00001</math>and<math>\zeta=2.0~2.2</math><ref name=":2" />.
 <br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>
 === References ===
 <references />

Non-biomass renewables - C3IAM: Difference between revisions

Revision as of 10:24, 10 August 2021

References

Navigation menu

Search