Bibliographic record
Abstract
The article presents the results of a study of the achieved effect of reducing CO2 emissions when replacing coal and oil with wind and solar energy. The concept of determining the achieved savings developed by the author is described in detail. The author's methodology and step-by-step calculations with comments are presented. The calculations are based on the actual amount of the fee for CO2 emissions, taking into account the damage caused, the number of people saved from premature death due to CO2 emissions, the economic cost of living determined by the World Bank for Canada, health care costs due to concomitant diseases, the social discount rate. This makes it possible for the first time to determine the real socio-economic effect of replacing fossil energy sources with cleaner energy carriers. Generation prices are compared. An argument is presented that refutes the arguments about the increase in costs in the economy, which may occur due to an increase in fees for harmful CO2 emissions into the atmosphere. Taking into account the economic cost of life, health care costs, social discount rates, carbon charges in Canada, it was possible to prevent the premature death of 11,307 people in six years, i.e. more than 5 people per 10 thousand inhabitants of the country. The total savings of the resources under consideration during this period amounted to almost 122 billion USD. In the conditions of the energy crisis, coal mines were partially deconserved to temporarily replace less carbon-intensive natural gas at power plants. I believe that this is a temporary and forced measure, associated with negative environmental consequences, which, of course, will lead to human and economic losses. The results obtained make it possible to establish more accurate benchmarks to justify the construction of wind and solar power plants, and to use hydrocarbon resources more efficiently.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".