A social-ecological-technological systems framework for urban ecosystem services
Bibliographic record
Abstract
As rates of urbanization and climatic change soar, decision-makers are increasingly challenged to provide innovative solutions that simultaneously address climate change impacts and risks and inclusively ensure quality of life for urban residents. Cities have turned to nature-based solutions to help address these challenges. Nature-based solutions, through the provision of ecosystem services, can yield numerous benefits for people and address multiple challenges simultaneously. Yet, efforts to mainstream nature-based solutions are impaired by the complexity of the interacting social, ecological, and technological dimensions of urban systems. This complexity must be understood and managed to ensure ecosystem-service provisioning is effective, equitable, and resilient. Here, we provide a social-ecological-technological system (SETS) framework that builds on decades of urban ecosystem services research to better understand four core challenges associated with urban nature-based solutions: multi-functionality, systemic valuation, scale mismatch of ecosystem services, and inequity and injustice. The framework illustrates the importance of coordinating natural, technological, and socio-economic systems when designing, planning, and managing urban nature-based solutions to enable optimal social-ecological outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".