Green alleys: a local approach to a sustainable urban environment A Montreal case study
Notice bibliographique
Résumé
"Green space availability in cities has gained more attention from the public, professionals from diverse fields, and policymakers over the last few years. Some cities are developing strategies to increase the green space area for the well-known social, physical, and mental health benefits (Gill et al., 2007; Lafortezza et al., 2009; Bowler et al., 2010). Others even as a market strategy targeting the middle class and higher income groups (Mell, 2009; Haase et al., 2017), reinforcing urban elites' political and economic interests (Lovell and Taylor, 2013). Overall, after the covid 19 emergency, some cities are rethinking green space as an alternative to improve people's wellbeing and as part of the strategy to mitigate some climate crisis outcomes (Gill, Handley et al., 2007; Bowler et al., 2010) that are becoming visible in an accelerated way worldwide and that are risking the stability of urban environments. However, last year, the pandemic evinced another element that exacerbates the complexity of finding solutions to mitigate the climate crisis: the spatial disparity in the distribution of green public spaces that low-income and vulnerable groups experience more significantly (Samuelsson et al., 2020). The lack of equity in the distribution is not only worsening the quality of life of people who struggle directly with effects such as urban heat islands, flooding and intense heat waves. It is also preventing urban environments from functioning effectively. Consequently, this study focuses on the city of Montreal. It analyzes how green infrastructure interventions at the neighbourhood scale, specifically green alleys, represent an opportunity to increase green space in the urban environment by transforming back alleys into spaces that generate environmental, mobility, and social benefits. Moreover, the potential that green alleys can have on a bigger scale when multiplied in the city. Therefore, I examine the availability of green alleys in different boroughs, their spatial distribution, physical characteristics, and their relationship with the built environment. The research also seeks to determine if green alleys influence dynamics that might mitigate the climate emergency as a decrease in car dependency due to a rise in active mobility. In order to have qualitative and quantitative information about the Green Alley Program in Montreal that allows a comprehensive analysis of this initiative that is spreading in the city, the study will include a compilation of information obtained from various Éco-quartiers, boroughs, and organizations that are part of the program. Finally, on-field gathering data through observations and interviews with some actors involved in developing the green alleys will complement the study. This study highlights the potential of the green alley program in Montreal based on a bottom-up process as a key to approaching the climate emergency from local and community-oriented interventions that can increase the green infrastructure in the city and active mobility alternatives. The primary outcomes of the research are, first, the acknowledgment of the diversity of alleys' morphology that enriches the variety of possible transformations with green infrastructure elements. Second, the potential of alleys to increase greenery in different neighbourhoods in the eleven boroughs that have active the program. Therefore, it represents an opportunity to include alleys as a neighbourhood strategy to adapt these public spaces to climate challenges and to improve people's quality of life by spatial transformations that lead to more active lifestyles. Third, considering the city will have 581 green alleys at the end of 2022 and the high number of back alleys that could be transformed, there is a unique opportunity to develop alley networks that can promote active transport modes and connect to public transport systems. Lastly, the Green Alley Program demonstrates the citizen mobilization strength to enhance urban environments with small-scale interventions. This research will contribute to the planning discipline by outlining environmental policies based on small-scale green infrastructure interventions in neighbourhoods as a promising strategy to approach environmental issues exacerbated by climate change on a city scale."@eng
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,016 | 0,004 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».