A Study of China-Latin America Cooperation in Green Energy Industry: Status, Obstacles, and the Policy Suggestions
Bibliographic record
Abstract
In the global context of tackling climate change and promoting energy transition, cooperation between China and Latin America in the green energy industry is gaining prominence.This study employs literature analysis, case studies, and data statistics to systematically examine the development status, cooperation dynamics, and key challenges faced by both regions.It proposes tailored strategies to overcome these obstacles.The research reveals China has accumulated strong technological strength in the fields of renewable energy and nuclear power, while Latin America has abundant renewable energy resources.At the same time, there are still many challenges in the cooperation between the two regions, such as policy barriers, technological disparities, and financial constraints.Nonetheless, collaboration in areas like wind power, solar energy, and hydropower holds immense potential for mutual economic and environmental gains.To enhance collaboration, the study recommends strengthening policy alignment, facilitating technology transfer, diversifying financing options, and attaching importance to the evaluation of geopolitical risks.Future efforts should focus on fostering policy dialogue, promoting market integration, and ensuring sustainable development in the green energy sector.This research contributes to academic discourse and offers practical insights for governments, businesses, and international entities seeking to advance cooperation in green energy between China and Latin America.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".