Carbon emissions from land acquisitions in Laos
Bibliographic record
Abstract
Large-scale land acquisitions repeatedly fall short of their acclaimed socioeconomic benefits and are associated with unintended social, economic, and ecological costs. In Laos, the government has started to question its own “Turning Land into Capital” policy, and reviews land acquisitions or concessions with regard to their socioeconomic impacts. Empirical investigations of environmental impacts of land concessions, however, remain underrepresented. We link the nation-wide concession development between 2001 and 2017 with associated land use changes and quantify related land use change-induced emissions. Results show that land acquisitions for agriculture, forestry, and mining affect mainly forests and land previously used for shifting cultivation and permanent agriculture; e.g., rice paddies. Consequently, land conversions caused by concessions resulted in net carbon emissions of 4.9 Mt CO2e yr-1 on average in 2001–2017, which amounted to 34% of total emissions from land conversions. Even tree plantations that are meant to serve as net carbon sinks caused net emissions, but those data are the least robust. The relatively low carbon emission intensity of shifting cultivation compared to the high carbon emission intensity of concessions challenges the dominant narrative of shifting cultivation as a causal factor for forest degradation. Political means of fostering sustainable development include the reduction of land acquisitions because of their emissions intensity, and minimization of emissions and social conflict induced by granted concessions, for example, by allocating land with low carbon densities and obtaining consent of local land users.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".