Advancing the Cement Industry's Climate Change Plan in British Columbia: Addressing Economic and Policy Barriers
Bibliographic record
Abstract
The cement manufacturing industry is both energy intensive and carbon intensive. The industry contributes to approximately 5 percent of global, man-made CO2 emissions. Globally the cement industry has developed a comprehensive strategy for reducing emissions through energy efficiency, fuel substitution, material substitution, and long-term research into both manufacturing processes and cement and concrete applications. While governments across North America grapple with taking action to addressing climate change, British Columbia (BC) is moving in advance of other Canadian jurisdictions in establishing a stringent price signal for greenhouse gas emissions. The newly instated carbon tax has the potential to significantly impact the competitiveness of BC's cement industry. This is of concern as provincial efforts to green the BC economy will require more, not less, cement given the many sustainability properties of cement and concrete products. This paper provides an overview of the BC carbon tax and the competitiveness considerations of the cement industry, including the economic impacts of BC's carbon tax on the cement industry. This paper assesses the policy and economic barriers that must be addressed in order for the cement industry to the advance its own globally developed and proven climate change strategy. Finally, the cement industry's recommendations on moving forward are provided.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".