PROSPECTS FOR CARBON CAPTURE AND SEQUESTRATION TECHNOLOGIES ASSUMING THEIR TECHNOLOGICAL LEARNING

摘要

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 emissions (SO_2) 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. 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 percent higher in the case of learning resulting in cumulative sequestration of CO_2 ranging from 150 to 250 billion (10 ) tons carbon during the 21st century. The results illustrate that assumptions on technological change are a critical determinant of future characteristics of the energy system, hence indicating the importance of long-term technology policies in reducing greenhouse gas emissions and climate change.