Technological learning for carbon capture and sequestration technologies

Abstract

This paper analyzes potentials of carbon capture and sequestration technologies (CCT) in a set of long-term energy-economic-environmental scenarios based on alternative assumptions for technological progress of CCT. In order to get a reasonable guide to future technological progress in managing CO 2 emissions, we review past experience in controlling sulfur dioxide (SO 2 ) emissions from power plants. By doing so, we quantify a ''learning curve'' for CCT, which describes the relationship between the improvement of costs due to accumulation of experience in CCT construction. We incorporate the learning curve into the energy-modeling framework MESSAGE-MACRO and develop greenhouse gas emissions scenarios of economic, demographic, and energy demand development, where alternative policy cases lead to the stabilization of atmospheric CO 2 concentrations at 550 parts per million by volume (ppmv) by the end of the 21st century. We quantify three types of contributors to the carbon emissions mitigation: (1) demand reductions due to the increased price of energy, (2) fuel switching primarily away from coal, and (3) carbon capture and sequestration from fossil fuels. Due to the assumed technological learning, costs of the emissions reduction for CCT drop rapidly and in parallel with the massive introduction of CCT on the global scale. Compared to scenarios based on static cost assumptions for CCT, the contribution of carbon sequestration is about 50% higher in the case of learning, resulting in cumulative sequestration of CO 2 ranging from 150 to 250 billion (10 9 ) tons with carbon during the 21st century. Also, carbon values (tax) across scenarios (to meet the 550 ppmv carbon concentration constraint) are between 2% and 10% lower in the case of learning for CCT by 2100. The results illustrate that assumptions on

Use of experience curves to estimate the future cost of power plants with CO2 capture

“…This formulation of a learning curve, first proposed by Wright (1936) to describe productivity improvements in the manufacture of an aircraft, has been widely adopted in the literature and in advanced energy-economic models to describe cost reductions as a function of cumulative production or deployment of a technology (Boston Consulting Group, 1968;Argote and Epple, 1990;McDonald and Schrattenholzer, 2001;Rubin et al, 2004;Riahi et al, 2004). The quantity 2…”

Modeling technological change and its impact on energy savings in the U.S. iron and steel sector

“…However, in this analysis, learning is limited only for some specific technologies for the simplicity of the calculation. Riahi et al [57] incorporated the learning curve into the energy-modeling framework MESSAGE-MACRO to analyze market potentials of carbon capture and sequestration technologies. They concluded that endogenized technological learning is computationally infeasible for their model, which included over 400 energy technologies and operate on 11 world regions.…”

A Case Study of Carbon Sequestration Potential of Land Use Policies Favoring Re-growth and Long-term Protection of Temperate Forests

“…While policies of carbon sequestration technologies are currently being discussed and tested (for example Herzog, 2001;Riahi, Rubin, Taylor, Schrattenholzer, & Hounshell, 2004), it may be worthwhile to further evaluate land use policies favoring both maintenance and re-growth of temperate forest systems. Not only may forest re-growth policies serve (in the aggregate) as substantial and continual carbon sinks, but they also provide additional direct, indirect, and non-use values that are of interest to society (Plantinga & Wu, 2003).…”