ESG: A Global Cooperation Path for Sustainable Power Generation
Bibliographic record
Abstract
The concept of Environmental, Social and Governance (ESG) involves stakeholders at all levels and plays an important role in the energy industry. The member countries and regions of the Association of the Electricity Supply Industry of East Asia and Western Pacific (AESIEAP) have set targets to achieve carbon neutrality between 2050 and 2065. The government has established explicit targets for sustainable power generation, implemented sustainable power generation policies, introduced fiscal and financial support measures, and required companies to disclose their ESG information. Sustainable power generation policies include green power initiatives such as green power trading mechanisms, green certificate trading systems and carbon pricing mechanisms. Despite progress, challenges remain in fully realizing the goals of sustainable power generation. This article therefore argues for increased cooperation between governments, businesses and international organizations. It offers targeted suggestions for cooperation on international investment, technological innovation, supply chain cooperation and market trading. By strengthening cooperation in these areas, countries can seize opportunities, overcome barriers, and accelerate the global transition to sustainable power generation, thereby contributing to global climate change mitigation and sustainable development.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".