Competition risk among production-living-ecological space of Mainland Southeast Asia in 2050
Bibliographic record
Abstract
Balancing ecological conservation and social development is essential for meeting global sustainability targets, exemplified by the Kunming-Montreal Global Biodiversity Framework’s Target 3 to protect at least 30% of critical biodiversity areas by 2030 through protected area expansion. However, social development cannot always yield to ecological targets, making the implementation of this target face significant challenges, particularly in developing countries. Spatial competition emerges as expanding ecological spaces often conflict with areas designated for human production and living—a phenomenon expected to intensify in the context of global change. In this study, we define the concept of "spatial competition" and use Mainland Southeast Asia as a case study to map the competition risks among production, living, and ecological spaces in 2050, and analyze inter-country variations. We found that urban–biodiversity competition risks concentrate in major cities, while cropland–biodiversity risks are prevalent in southeastern coastal and central regions—with 10–40% of urban lands and over 50% of croplands overlapping with biodiversity hotspots. We then analyzed the underlying driving factors of competition risk using a random forest model. The analysis shows that 25 variables (including human and environmental factors) significantly contribute to these risks, with urban–biodiversity competition risk driven mainly by socioeconomic factors (e.g., GDP, nighttime lights) and cropland–biodiversity risk by road density and environmental factors (e.g., slope, precipitation). These findings underscore an intensifying spatial competition, provide a theoretical framework to address the protection-development dilemma, and offer insights for sustainable development strategies in developing regions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".