The Dynamic Interaction Between Scientific Output in Environmental Science and Energy and Carbon Dioxide Emissions in G7 Countries
Bibliographic record
Abstract
Abstract Due to excessive carbon dioxide emissions, the world is facing environmental devastation. Energy and environmental innovations are considered to be critical tools in combating the growing CO2 emissions. Developing these innovations requires extremely high investments in research and development processes, where knowledge is generated as one of the important outputs. This knowledge serves as a basis for innovation development and raising awareness among all relevant stakeholders about excessive environmental degradation. One of the significant sources of knowledge is scientific publications. Therefore, the aim of this research is to examine whether increased CO2 emissions stimulate the scientific community to publish a greater number of papers, as well as whether the knowledge contained in these publications is utilized in reducing CO2 emissions. The sample consists of G7 member countries. The time frame of the research is 1996–2019. The dynamic properties of the vector autoregression (VAR) models were summarized using impulse response function and variance decomposition forecast error. In most G7 countries, it has been determined that an increase in scientific production in environmental science and energy leads to a reduction in CO2 emissions. On the other hand, increased CO2 emissions affect higher scientific productivity in environmental science and energy only in Canada.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".