Technological Learning for Carbon Capture and Sequestration Technologies

By Keywan Riahi, Edward S. Rubin, Margaret R. Taylor, Leo Schrattenholzer, David A. Hounshell. 2004

195 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Mechanical Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/ab06ab006a281cd543be3005556cd6e913a03011

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Reyer Gerlagh
2. Bob van der Zwaan
3. Sonia Yeh
4. Edward S. Rubin
5. Margaret R. Taylor
6. G. P. Hammond
7. S. W. Williams
8. Serge S. Ondo Akwe

In Mechanical Engineering

1. Bob van der Zwaan
2. Reyer Gerlagh
3. S. W. Williams
4. G. P. Hammond
5. Serge S. Ondo Akwe
6. Sonia Yeh
7. Sabine Fuss
8. André P. C. Faaij

In Renewable Energy, Sustainability and the Environment

1. Reyer Gerlagh
2. Bob van der Zwaan
3. Margaret R. Taylor
4. Inês Lima Azevedo
5. P. Jaramillo
6. D. P. Vuuren
7. Sabine Fuss
8. David A. Hounshell

Citations in the corpus

• A review of uncertainties in technology experience curves (2012, background): “Experience curves for CO2 capture technologies already have been incorporated into some large-scale energy-economic models (Riahi et al., 2004; Riahi et al., 2005).”
• Fast growing research on negative emissions (2017, background): “Integrated scenarios have initiated and mainstreamed the discussion of negative emissions and their role in climate change mitigation (Herzog 2001, Riahi et al 2004, Fuss et al 2014, Riahi et al 2015), but they do not feature heavily in the topic structure nor in terms of publications included in…”
• Uncertainty in Carbon Capture and Storage (CCS) deployment projections: a cross-model comparison exercise (2014, background): “Still, from individual model studies of the EMF19 interesting conclusions regarding the importance, and uncertainty or sensitivity to techno-economic assumptions of CCS can be drawn (e.g. (Akimoto et al. 2004; Kurosawa 2004; Riahi et al. 2004; SmekensRamirez Morales 2004)).”
• Learning curves for environmental technology and their importance for climate policy analysis (2004, background): “In a preliminary integrated assessment modeling study carried out by researchers at IIASA [ 18 ], an average learning rate of 12% (basedon the results foundabove) was usedto mod el the expected rate of capital cost decline for CCS systems deployed to reduce greenhouse gas emissions.”
• Techno-economic appraisal of fossil-fuelled power generation systems with carbon dioxide capture and (2011, background): “The cost of production of a technology tends to reduce as production volumes increase; a phenomenon reflected by so-called technology ‘learning curves’ or ‘experience curves’ [26-28].”
• Economics of geological CO2 storage and leakage (2009, background): “We observed that an increasing number of existing (especially but not solely bottom-up) models are today able to simulate the deployment of CCS technologies (see e.g. Riahi et al. 2004, and Smekens and van der Zwaan 2006).”
• A review of learning rates for electricity supply technologies (2015, methodology): “An alternative formulation proposed by Romer (1986) modeled technological change endogenously as a function of selected variables—a formulation now adopted in much of the technological change literature.”
• Options and Instruments for a Deep Cut in CO2 Emissions: Carbon Dioxide Capture or Renewables, Taxes or Subsidies? (2006, background): “Whereas an increasing number of existing bottom-up models are today able to simulate CCS technologies (for example, Riahi et al., 2004, and Smekens and van der Zwaan, 2006, to mention only two), only few top-down models are available that incorporate CCS.”