Explaining successful and failed investments in U.S. carbon capture and storage using empirical and expert assessments
Bibliographic record
Abstract
Abstract 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".