Belt and Road Initiative in Central Asia: Anticipating socioecological challenges from large‐scale infrastructure in a global biodiversity hotspot
Bibliographic record
Abstract
Abstract Until recently, China's Belt and Road Initiative (BRI) has overlooked many of the social and environmental dimensions of its projects and actions in favor of more immediate economic and sociopolitical considerations. The main focus of investments under BRI has largely been to improve transport, telecommunication, and energy infrastructures. However, in Central Asia, biodiversity is not only foundational for the livelihoods and socioeconomic wellbeing of communities, it also shapes people's culture and identities. Furthermore, ecosystem services derived from functioning landscapes bring enormous benefit for millions of people downstream through integrated and transboundary water systems. Already under pressure from climate‐induced melting of glaciers, the fate of ecologically important areas is considered in light of the potential harm arising from large‐scale linear infrastructure projects and related investments under China‐led BRI. Following review of some of the anticipated impacts of BRI on mountain environments and societies in the region, we highlight several emerging opportunities and then offer recommendations for development programs—aiming fundamentally to enhance the sustainability of BRI investments. Leveraging new opportunities to strengthen partner countries’ priority Sustainable Development Goals and enhancing their agency in the selection of collaborations and the standards to use in environmental impact and risk assessments are recommended.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".