Knowledge domain of greenhouse gas emissions from hydropower reservoirs: Hotspots, frontiers and future perspectives
Bibliographic record
Abstract
Research on greenhouse gas (GHG; CO 2 , CH 4 , N 2 O) emissions from hydropower reservoirs has attracted widespread attention due to the potential effect on global climate change. However, few attempts have been made to conduct the research progress in this field from a global perspective. In this study, knowledge mapping research was conducted by applying scientometric analysis to explore research hotspots, frontiers and emerging trends of this field from 1993 to 2021, and five research priorities were recommended for the further study. The results showed that the research on GHG emissions from hydropower reservoirs was interdisciplinary, and there was an exponential increase in yearly publication outputs. Additionally, China, Brazil, Canada, America, and France were the leading contributors with high publication outputs, and the Chinese Academy of Science was the most productive and influential institution. Furthermore, the research hotspots in the field mainly focused on CO 2 , CH 4 , and N 2 O emissions and their spatiotemporal characteristics due to great contributions to greenhouse effect and heterogeneities of the GHG emissions from hydropower reservoirs. Research frontiers mainly concentrated on the Three Gorges Reservoir, bubble-mediated gas exchange, GHG emissions across different interfaces and gas transfer velocity. Meanwhile, the first three research frontiers were regarded as emerging trends in recent years. Although great progress has been made in the field, there were still some research challenges. Future research priorities were recommended to strengthen: 1) Application of remote sensing in the research on GHG emissions from hydropower reservoirs, 2) improvement of life cycle assessment research, 3) standardization research on the measurement methods, 4) anthropogenic impacts on carbon dynamics, and 5) international cooperation and database construction. Finally, several mitigation measures were suggested to provide useful insights into the management and control of GHG emissions. In contrast to previous reviews, this paper provides an insight for the visual study of the research on GHG emissions from hydropower reservoirs, helping researchers understand the current research status and future perspectives from a global perspective.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".