The role of socio-economic factors in planning and managing urban ecosystem services
Bibliographic record
Abstract
How green spaces in cities benefit urban residents depends critically on the interaction between biophysical and socio-economic factors. Urban ecosystem services are affected by both ecosystem characteristics and the social and economic attributes of city dwellers. Yet, there remains little synthesis of the interactions between ecosystem services, urban green spaces, and socio-economic factors. Articulating these linkages is key to their incorporation into ecosystem service planning and management in cities and to ensuring equitable outcomes for city inhabitants. We present a conceptual model of these linkages, describe three major interaction pathways, and explore how to operationalize the model. First, socio-economic factors shape the quantity and quality of green spaces and their ability to supply services by influencing management and planning decisions. Second, variation in socio-economic factors across a city alters people’s desires and needs and thus demands for different ecosystem services. Third, socio-economic factors alter the type and amount of benefit for human wellbeing that a service provides. Integrating these concepts into green space policy, planning, and management would be a considerable improvement on ‘standards-based’ urban green space planning. We highlight the implications of this for facilitating tailored planning solutions to improve ecosystem service benefits across the socio-economic spectrum in cities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".