Bibliographic record
Abstract
Alberta's industrial and power sectors have many facilities classified as large emitters, with high-concentration carbon dioxide emissions from large point sources. Carbon capture and storage (CCS) reduces high-concentration carbon dioxide emissions from large point sources. CCS is a technically feasible technology that reduces greenhouse gas emissions in existing industries, and is recognized as a key factor in reaching international climate change targets. Alberta hosts two commercial-scale CCS projects funded by the provincial government and private industry. However, even with regulatory approvals for CCS projects, including the province's property right to subsurface pore space for CO2 sequestration, future CCS commercial-scale projects are non-existent. CCS deployment is often obstructed by high project costs and risks in developing an emerging technology to commercial scale. Recent carbon pricing in Alberta may provide an incentive for investment in CCS and deployment. This research includes a Multiple Account Benefit-Cost Analysis of carbon capture and storage projects in Alberta, from the perspective of Albertans. There is a significant cost to private firms and industry to invest in CCS. However, as carbon prices escalate to $50 per tonne by 2022, CCS becomes more economical for the cement industry, and hydrogen processing, ammonia, and chemical production. When impacts in the taxpayer, environment, social, and economic activity accounts are considered, there is an overall benefit to Albertans to reduce carbon emissions with CCS. Regardless, public perception of CCS projects remains a crucial factor. Recent public opposition to a CCS pilot project in Alberta demonstrates the power of negative public opinion to cancel projects, and should not be underestimated. Both industry and the government need to ensure trust and a sense of fairness is established when engaging with communities regarding CCS initiatives. To reduce barriers to CCS development and increase investment in CCS, a policy strategy is needed. The policy strategy needs to address both the market failures that lead to pollution and an underinvestment in research. Therefore, in addition to carbon pricing, environmental taxation such as tax credits specifically for CCS projects can encourage research and development. To also signal government support to the public and investors, existing low-carbon and clean-energy projects that are incentivized by provincial and federal governments should extend to include CCS in both the industrial and power sectors. With recent carbon pricing, this research provides the opportunity to reexamine CCS in Alberta and consider complementary policies for CCS deployment that can benefit Albertans as a whole.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".