期刊
ENVIRONMENTAL RESEARCH LETTERS
卷 16, 期 1, 页码 -出版社
IOP PUBLISHING LTD
DOI: 10.1088/1748-9326/abd19e
关键词
carbon capture and storage; decarbonization; political economy; preference elicitation; statistical learning
资金
- Alfred P Sloan Foundation [G-2017-9928]
Most studies on deep decarbonization have found that a diverse portfolio of low-carbon energy technologies, including carbon capture and storage (CCS), will be necessary. However, the majority of projects attempting to commercialize the technology end in failure. Factors such as regulatory complexity and public opposition play significant roles in determining project outcome.
Most studies of deep decarbonization find that a diverse portfolio of low-carbon energy technologies will be required, including carbon capture and storage (CCS) that mitigates emissions from fossil fuel power plants and industrial sources. While many projects essential to commercializing the technology have been proposed, most (>80%) end in failure. Here we analyze the full universe of CCS projects attempted in the U.S. that have sufficient documentation (N=39)-the largest sample ever studied systematically. We quantify 12 project attributes that the literature has identified as possible determinants of project outcome. In addition to costs and technological readiness, which prior research has emphasized, we develop metrics for attributes that are widely thought to be important yet have eluded systematic measurement, such as the credibility of project revenues and policy incentives, and the role of regulatory complexity and public opposition. We build three models-two statistical and one derived through the elicitation of expert judgment-to evaluate the relative influence of these 12 attributes in explaining project outcome. Across models, we find the credibility of revenues and incentives to be among the most important attributes, along with capital cost and technological readiness. We therefore develop and elicit experts' judgment of 14 types of policy incentives that could alter these attributes and improve the prospects for investment in CCS. Knowing which attributes have been most responsible for past successes and failures allows developers to avoid past mistakes and identify clusters of near-term CCS projects that are more likely to succeed.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据