Investigating the coordination between ecological and economic systems in China’s green development process: a place-based interdisciplinary evaluation
Bibliographic record
Abstract
Harmonizing human activities with the natural environment has received much attention as a development goal and is a longstanding pursuit of human society. In this study, we investigated the sustainability of green development from the perspective of the coordination between economic and environmental subsystems at the place-based scale, with 290 cities in China as research objects. An interdisciplinary method combining an improved data envelopment analysis model and Lotka-Volterra model was conducted to examine the two subsystems’ development efficiency and their correlation (i.e., mutualism, commensalism, amensalism, sacrifice, competition, independence). The results indicate that one-third of the cities’ economic and environmental subsystems are in a mutualistic state, with a relatively stable social-ecological system. However, the two subsystems are in a state of competition in one-fourth of the cities, with rather intense vulnerability, where the harmonious coexistence of humans and nature is facing more significant challenges. In addition, vulnerability of the social-ecological system in economically developed cities shows substantial polarization. The two subsystems in most of these cities are in mutualism or amensalism relations, whereas the social-ecological system in economically underdeveloped cities shows even greater vulnerability.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".