Environmental Inequalities in Ecosystem Services Benefits of Green Infrastructure: A Case Study from China
Bibliographic record
Abstract
Rapid urbanization is widespread globally, particularly impacting developing countries. In the face of climate challenges and shrinking public spaces resulting from urbanization, the significance of green infrastructure (GI) for human well-being and sustainability has increasingly taken center stage. This study employs an array of social-environmental benefits to evaluate GI’s contributions to human well-being, including mitigation of the urban heat island (UHI) effect, recreational functions, enhanced landscape connectivity, and efficient stormwater management. By mapping GI’s advantages, we scrutinized tradeoffs and ‘hot spots’ linked to these benefits within a metropolitan region. Moreover, we correlated GI’s advantages with the well-being of different socio-economic status (SES) groups by global and local regression. The study reveals environmental inequality, with higher SES areas—such as affluent and well-educated neighborhoods—providing superior and multifaceted GI benefits. The income coefficient is significantly positively correlated with the recreation function at the 1% significance level, while the coefficient for education is significant at the 10% level. Moreover, the income coefficient (0.349) surpasses the education coefficient (0.012). Our research also highlights that accessibility to GI’s recreational services may be an essential and overlooked indicator of environmental justice, especially for communities with a high proportion of elderly and low-income individuals.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".