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Enregistrement W7035986980

Attaining climate justice through the adaptation of urban form to climate change: flood risks in Toronto

2023· dissertation· en· W7035986980 sur OpenAlexaboutno aff

Notice bibliographique

RevueUWSpace (University of Waterloo) · 2023
Typedissertation
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiquePlanarian Biology and Electrostimulation
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésClimate justiceRedressFlood mythClimate changeAdaptive capacityEconomic JusticeEnvironmental justiceDistributive justiceEmpirical research
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Empirical evidence points out that entrenched cost-benefit rationales behind urban form adaptations to climate change unequally exacerbate vulnerabilities and hazard exposures, engendering risk inequalities and triggering climate injustice. Specifically, adaptive interventions for managing climate change-induced floods, whether through green and blue infrastructure (GBI), land use planning, or urban design, prioritize the protection of high-value urban assets while excluding vulnerable groups. To redress climate injustice, some have called for the consideration of the three pillars of justice: distributive justice, i.e., the just spatial distribution of adaptation responses; procedural justice, i.e., the equality of decision-making processes; and recognitional justice, i.e., the legitimization of marginalized groups. To assess the extent of these pillars’ integration in the scholarship (theoretically and empirically), this dissertation conducted a systematic review of 136 peer-reviewed papers on urban climate justice vis-à-vis adaptation. The findings reveal a lack of theoretical and empirical connections between the three-pillared justice framework and climate adaptive interventions in urban form.
\nThe dissertation’s theoretical framework overcomes these omissions by using different theories/concepts in the literature as nexuses connecting climate justice pillars with urban form. It capitalizes on interconnections distributive justice has with differential vulnerabilities, flood exposures, and the adaptive capacity of urban form to identify areas that unequally experience flood risks and need to be prioritized in adaptation. It, furthermore, combined the three-pillared justice framework with epistemic justice and local experiential knowledge concept to explore how flood-adaptive GBI planning can address the root causes of vulnerabilities, hence facilitating justice-oriented transformative adaptation. Accordingly, the research developed a multi-criteria model including indicators and variables for measuring the spatial distribution of social vulnerabilities, exposure, and the adaptive capacity of urban form, whereby it proposes pathways for justice-oriented transformative adaptation of high-risk priority areas through GBI planning.
\nThe dissertation focuses on Toronto in Ontario, Canada, to test the theoretical framework, which can be applied in any city. The study in Toronto asks: “who” is unequally at-risk of flooding events, “where” are they located, “why” they are unequally vulnerable, and “how” we can engage the high-risk community in adaptive GBI planning to promote justice-oriented transformative adaptation. The methodology started with operationalizing the spatial multi-criteria model through weighted overlay analysis using ArcGIS and an online survey of 120 Toronto-based flooding experts, which yielded the identification of four priority neighborhoods at a disproportionate risk of floods. Focusing on one of the high-risk priority neighborhoods, Thorncliffe Park, I conducted 20 semi-structured interviews with flooding experts and local leaders and an online survey of residents to investigate whether the local experiential knowledge of residents has been recognized in adaptive GBI planning decisions. I furthermore performed an online participatory-mapping activity in this neighborhood during which participants marked, on the neighborhood map, locations that require GBI for socio-cultural benefits. I overlaid the resulting participatory maps with land uses’ run-off coefficients to propose sites for allocating GBI for both socio-cultural benefits and run-off management.
\nThe findings show the effectiveness of the theoretical framework in identifying priority neighborhoods and developing place-based adaptation solutions inside and outside Canada. All four high-risk neighborhoods are inner-city tower communities with old infrastructure and dense low-income, racialized, and migrant populations, typical tower blocks built after the second World War in several cities across North America and Europe. The findings in Thorncliffe Park, as the priority neighborhood, unveil the exclusion of residents from flood-adaptive GBI planning despite their vulnerabilities and exposure. This exclusion, as results indicate, is rooted in technocratic processes based on technical knowledge and cost-benefit rationales. The findings show four epistemic barriers that need to be addressed to facilitate climate justice in adaptation interventions within Thorncliffe Park: lack of social networks, citizenship rights, climate awareness opportunities, and communicational tools. The results also show that the industrial uses around the railway and residential-commercial sites around Overlea Boulevard in this neighborhood are in dire need of GBI for managing run-offs and socio-cultural benefits. I propose adopting inclusive processes to allocate small-scale adaptive GBI in these locations. Building on the findings, the dissertation proposes future theoretical and empirical studies to complement this study by proposing how to design GBI and other urban form adaptive interventions by changing the layout patterns, orientation, and geometry of streets, buildings, and blocks in the high-risk disenfranchised communities to advance climate justice. At the center of this proposition are developing new theories to expand the climate justice triad and devising new forms of inclusive and collaborative design.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,364
Score d'incertitude au seuil0,957

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,029
Tête enseignante GPT0,272
Écart entre enseignants0,243 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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