Tracing and building up environmental justice considerations in the urban ecosystem service literature: A systematic review
Bibliographic record
Abstract
The concept of ecosystem services (ES) has mainstreamed as an interdisciplinary framework in the urban sustainability and resilience agenda. While the uptake of ES in urban areas is deeply entangled with multiple values, trade-offs, institutions, management and planning approaches, there is still a lack of a comprehensive and systematic framework to address environmental justice (EJ) in urban ES assessments. This article presents a systematic literature review to examine what factors are critical for the effective inclusion of an EJ lens in urban ES appraisals. More specifically, we assessed how distributional, procedural and recognitional EJ dimensions have been addressed, and in relation to which types of urban ES. Our results reveal that EJ considerations are currently focused on the (un)equal distribution of ES and the associated green and blue infrastructure with regard to socioeconomic groups, with special attention to income and race/ethnicity as the main mechanisms of social stratification. There is also a predominant focus on regulating and cultural ES, analyzing their role on resilience and adaptive capacity on one hand, and recreational values, social cohesion and place-making on the other. In this review, we also evaluate the interconnected dimensions of justice and their constraints, and lay out pathways for new research into intersectional and restorative approaches to justice in ES assessments. Finally, we interrogate what the role of urban ES-based planning might be in making more inclusive and just cities and explore its implications for policy and practice.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 |
| 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".