Impacts of the Sharing Economy on Urban Sustainability: The Perceptions of Municipal Governments and Sharing Organisations
Bibliographic record
Abstract
By changing the institutionalised practices associated with resource distribution, the sharing economy could support sustainable urban transformations. However, its impacts on urban sustainability are unknown and contested, and key actors hold different perceptions about them. Understanding how they frame these impacts could help solve conflicts and outline what can be done to influence the development of the sharing economy in a way that fosters urban sustainability. This study explores the diversity of these frames across actors (sharing economy organisations and municipalities), segments (accommodation, bicycle, and car sharing), and cities (Amsterdam and Toronto). A framework of the impacts on urban sustainability was developed following a systematic literature review. This then guided the analysis of secondary data and 51 interviews with key actors. Results show that accommodation sharing is framed most negatively due to its impact on urban liveability. Bicycle sharing is surrounded by less conflict. Still, in Amsterdam, which has a well-functioning bicycle infrastructure, it is viewed less positively than in Toronto. Car sharing is the most positively framed segment in Amsterdam as its potentials to lower emissions align with municipal sustainability agendas. Practical insights for negotiations between sharing economy organisations and municipalities to advance urban sustainability are proposed.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| 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".