Towards a relational understanding of youth lifestyles and wellbeing in climate resilient urban development: insights from a seven-city study of young people
Bibliographic record
Abstract
Supporting youth wellbeing in low carbon ways is a crucial challenge in cities. Seventy percent of youth will live in urban areas by 2050 and urban sites account for 67–72% of the global share of carbon emissions. Young people’s consumption behaviour including energy use is increasingly identified as a key driver of urban emissions. This paper expands beyond dominant individualised approaches to examining urban youth wellbeing and consumption to interrogate the relational contexts in which young people live, their wellbeing aspirations, and the conditions that enable or lock-in lifestyle emissions. Applying a relational lens and thematic analysis to focus group data collected from 332 youth aged 12–24 years in seven cities of the global South and North, the paper examines experiences shaping youth wellbeing in the context of urban consumption activities. Findings emphasised the complexities of “linked lives”, foregrounding family, peer and community relationships as critical in shaping youth wellbeing and consumption. Home was highlighted as a significant relational context, where family relationships impact wellbeing and energy use, through connection, comfort, conflict and compromise. Public space was also valued, but findings highlighted issues of identity and inequality that impact access. Findings also underscored the significance of beyond-human relationships. This cross-cultural research highlights underacknowledged complexities in youth wellbeing and consumption activities. Discussion proposes ways local government can adopt relational perspectives to advance climate resilient urban development, including cultivating meaningful relationships with youth and prioritising secure housing, access to green space, and care and integration of nature within urban landscapes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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".