Learning curves for environmental technology and their importance for climate policy analysis
Learning curves for environmental technology and their importance for climate policy analysis
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DOI：
10.1016/j.energy.2004.03.092
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发表时间：
2004-07
期刊：
影响因子：
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通讯作者：
E. Rubin;Margaret R. Taylor;S. Yeh;David A. Hounshell
中科院分区：
文献类型：
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作者：
E. Rubin;Margaret R. Taylor;S. Yeh;David A. Hounshell
我们寻求提高综合评估（IA）模型的能力，以纳入随着时间的推移，在CO2捕获和封存（CCS）技术的成本和性能的变化。本文介绍的研究结果，审查过去的经验，在控制其他主要发电厂的排放量，可能作为一个合理的指导未来的CCS系统的技术进步率。特别是，我们专注于美国和世界各地的二氧化硫（SO2）和氮氧化物（NOx）控制技术在过去30年的经验，并得出这些技术的经验学习率。将这些比率应用于大规模综合影响模型中的CCS成本表明，实现气候稳定化目标的成本相对于没有学习CCS技术的情景要低得多。
We seek to improve the ability of integrated assessment (IA) models to incorporate changes in CO2capture and sequestration (CCS) technology cost and performance over time. This paper presents results of research that examines past experience in controlling other major power plant emissions that might serve as a reasonable guide to future rates of technological progress in CCS systems. In particular, we focus on US and worldwide experience with sulfur dioxide (SO2) and nitrogen oxide (NOx) control technologies over the past 30 years, and derive empirical learning rates for these technologies. Applying these rates to CCS costs in a large-scale IA model shows that the cost of achieving a climate stabilization target are significantly lower relative to scenarios with no learning for CCS technologies.