An Integrated Risk Management Framework for Carbon Capture and Storage in the Canadian Context
Bibliographic record
Abstract
Climate change is a risk issue of global proportions. Human health and environmental impacts are anticipated from hazards associated with changes in temperature and precipitation regimes, and climate extremes. Increased natural hazards include storms and flooding, extreme heat, drought, and wildfires. Reduced food and water quality and quantity, reduced air quality, new geographic range of infectious diseases, and increased exposure to ultra-violet radiation are also predicted. In order to make a measurable contribution to reducing carbon dioxide emissions at point source fossil fuel and industrial process sites that contribute to climate change, estimates suggest that up to 3,000 dedicated large scale carbon capture and geological sequestration (CCS) projects will be necessary by 2050. Integrated projects include carbon dioxide capture; compression into a supercritical stream; transport, most often by pipeline; deep injection at wellheads; and sequestration in suitable saline aquifer geological formations, usually 800 metres or more below the earth’s surface. In implementing CCS as part of an overall climate change mitigation strategy, it is important to note that population health and environmental risks are associated with each of these value chain components of integrated projects. Based on an assessment of existing regulatory and non-regulatory guidance for risk assessment/risk management (RA/RM), an analysis of the application, assessment, and approval process for four large scale Canadian projects, and findings from a structured expert elicitation focused on hazard and risk issues in injection and storage and risk management of low probability high impact events, this research developed an Integrated Risk Management Framework (IRMF) for CCS in the Canadian context. The IRMF is a step-wise systematic process for RA/RM during the life of a project, including engagement with wide ranging government and non-government partners that would contribute to a determination of acceptable risk and risk control options. The execution of the IRMF is an intervention that could reduce local hazards and associated risks in terms of likelihood and consequence, as well as identify and document risk management that could underpin broad acceptance of CCS as a climate change mitigation technology. This would thereby also have an important part in protecting global population health and wellbeing in the long term. Indeed, diverse stakeholders could be unforgiving if hazard assessment and risk management in CCS is considered insufficient, leading to ‘pushback’ that could affect future implementation scenarios. On the other hand, RA/RM done right could favourably impact public perception of CCS, in turn instilling confidence, public acceptance, and ongoing support for the benefit of populations worldwide. This thesis is composed of an introduction to the research problem, including a population health conceptual framework for the IRMF, followed by five manuscripts, and concluding with a discussion about other barriers to CCS project development, and a risk management policy scenario for both the present time and during the 2017-2030 implementation period.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".