Editorial: Energy, economy, and climate interactions: challenges and opportunities
Bibliographic record
Abstract
Editorial on the Research Topic Energy, economy, and climate interactions: challenges and opportunitiesClimate change has become one of the most prominent problems facing the world today and climate change prevention and adaptation are effective means of response (Shi et al., 2023).In contemporary world, energy plays an important role in all aspects of economic development.However, excessive energy use also leads to a rapid increase in greenhouse gas emissions, which eventually bring about climate change and have a negative impact on environment, economy, agriculture, and society.Global climate change mainly includes extreme climate, such as global climate warming, which has disrupted social organizations, housing, and food systems.In detail, climate change enhances species extinction risk, causes melting of glaciers, and even destroys the development of the world and human wellbeing.The Sixth Assessment Reports of IPCC assessed the impacts of climate change, looking at ecosystems, biodiversity, and human communities at global and regional levels, and emphasized the development path of climate adaptation.In addition, global energy supply shortage and worldwide economic downturn at present might magnify this poor influence.These phenomena indicate a sense of urgency in the collaborative promotion of addressing energy security, economy growth and climate change issues.In other words, the consequences and costs caused by climate change will be significantly magnified and thus hardly affordable.Elshkaki believes that if the current vicious cycle of environmental degradation, such as climate change, caused by the consumption of traditional energy through carbon emissions is maintained, then in the near future, the natural resources of developing countries may not be sufficient to meet the sustainable economic growth of the new generation (Elshkaki, 2023).Therefore, it is advisable for our world to coordinate the promotion of energy security, economic growth, and climate mitigation, although this process consists of various challenges, opportunities and even failures.More importantly, climate change is the result of a complex process of social transformation, which we all need to understand and respond to the challenges it brings.In order to reduce future climate change and ensure that our economy can grow in a sustainable manner, green and low-carbon transformation of energy is considered as an effective approach, which focuses on greenhouse gas emissions reduction and utilizes
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.039 | 0.032 |
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".