Visions for development and management of urban green and blue infrastructure: a citizen's perspective
Bibliographic record
Abstract
Ongoing urbanization leads to problems such as densification, loss of biodiversity, and social injustice in cities. For increasing urban populations, green–blue infrastructure (GBI) is an important element in compact cities contributing to human health, well-being, and the provision of important ecosystem services. We analyzed responses from two open-ended questions about visions, ideas, and topics for the development and management of GBI important for citizens of the city of Leipzig, Germany. The questions were part of an online survey accompanying the development of the local GBI planning strategy: Master Plan Green. The strategy is focusing on five guiding themes that are leading local and global debates about sustainable and resilient cities: biodiversity, climate adaptation, environmental justice, health, and sustainable mobility. We categorize citizens' ideas and suggestions, summarize frequent problems and conflicts, and link ideas and visions to the five guiding themes. As the last step, we discuss citizens' suggestions in order to minimize conflicts in GBI and to identify deficits in present local planning. Major problems and conflicts that were addressed by respondents relate to quality, usability, other users, activities, and safety and security of GBI. Numerous suggestions aimed to tackle these problems, for example, by designating separate use areas, adding naturalness, improving maintenance, and enhancing facilities. A range of ideas and suggestions were based on diverging expectations underpinning the challenge of matching heterogeneous demands of GBI users in an equitable fashion. Linking these suggestions to the five guiding themes reveals that most ideas are covered by one or several guiding themes and are considered in local planning strategies. However, findings also demonstrate that increasing the quantity of Leipzig's GBI is a central request from respondents. Sociocultural and economic aspects as well as conflicting demands among citizens should further be central to GBI planning to avoid injustice and achieve sustainability objectives. This analysis gives insights into opinions and visions of citizens regarding the development of the city's GBI network and thus substantiates major strategic and planning themes leading global and local urban strategies toward sustainable cities. Considering specific suggestions and GBI deficits that bother citizens on a local level, offers the opportunity to improve the social and ecological resilience of GBI.
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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".