Gaining competitive advantage through a low carbon economy: China vs Europe
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the possible developments of the energy sector in 2050. Special consideration is given to the evolution of the relationship between Europe and China. Design/methodology/approach The paper draws on the analysis of current trends and news to develop a hypothetical scenario for the energy sector in 2050. A new hypothetical scenario is proposed in which China's role is dramatically shifted from world's largest polluter to that of leader of the green revolution. Findings The paper suggests that the countries with the highest standards could gain a competitive advantage by imposing their standards on the other countries. In the specific scenario analyzed in the paper, China is the country that will gain such an advantage by 2050. Research limitations/implications The paper's main limitation is the lack of estimations on the likelihood of such a hypothetical scenario. Practical implications Practical implications of the paper are the recommendations to European Governments and agencies to take their energy policies one step forward towards low carbon solutions. Originality/value The new perspective taken by the paper puts China under a new light and uncovers long‐term strategic implications of recent trends in the energy sector.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".