Technology in support of nature-based solutions requires understanding everyday experiences
Bibliographic record
Abstract
Nature-based solutions that incorporate "smart" technologies to enhance ecosystem services delivery may change the way people experience urban nature in their everyday lives. We lay out a conceptual basis for considering such changes and their social impacts. Cities are increasingly recognized as complex social-ecological-technological systems in which sustainability and climate resilience require environmental function to be paired with innovative technology. Smart technologies for real-time monitoring and autonomous operation promise innovations in urban landscape management. However, this promise can be fully realized only with adequate consideration of social impacts. Drawing on literature in landscape studies, environmental psychology, behavioral economics, public health, and aesthetics, we initiate a discussion connecting everyday experiences of urban nature with the social impacts of smart nature-based solutions and with local communities' support for their implementation. We describe what makes pleasant everyday experiences of urban nature and their related well-being benefits and social and cultural values, and we elucidate how these experiences depend on perceivable landscape characteristics that are only sometimes directly linked to environmental functions. Then, based on this literature, we speculate about how adopting smart technologies to manage nature-based solutions may noticeably change the landscape in novel ways and have unintended negative impacts on everyday experiences of urban nature. We illustrate this with an example: smart stormwater management of retention ponds. We conclude that the risk of degraded everyday experiences of nature must be considered and addressed in the development of smart nature-based solutions. If pleasant everyday experiences are ensured through appropriate design, smart nature-based solutions may not only realize societal co-benefits, but also gain acceptance and continued support from the public for the whole set of ecosystem services they deliver.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".