Does carbon finance make a sustainable difference? Hydropower expansion and livelihood trade‐offs in the Red River valley, Yunnan Province, China
Bibliographic record
Abstract
The Kyoto Protocol's Clean Development Mechanism (CDM) is a carbon credit trading scheme intended to reduce anthropogenic greenhouse gas emissions and promote ‘sustainable development’. Hundreds of CDM‐sponsored hydroelectric dams have been constructed in southwest China's Yunnan Province, where carbon finance contributes substantial financial incentives to hydropower expansion. This article investigates whether riparian Handai farmers settled near the Madushan hydropower plant on the Chinese section of Red River have experienced positive outcomes from this project's participation in the CDM. I assess how Handai individuals' access to core livelihood assets has been modified following dam completion and probe how the CDM reconfigures scalar relations among the various stakeholders involved in hydropower governance in Yunnan. Though the CDM facilitates hydropower expansion, it fails to produce development that is more sustainable than ‘business as usual’ from a local perspective. Rather, the CDM consolidates hydropower governance in the same way as it unfolded in Yunnan before the province became an active participant in this scheme. The CDM also facilitates a national development campaign fostering specific socio‐economic modernization patterns in China's western provinces.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".