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Record W7035986980

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

2023· dissertation· en· W7035986980 on OpenAlexaboutno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2023
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlanarian Biology and Electrostimulation
Canadian institutionsnot available
Fundersnot available
KeywordsClimate justiceRedressFlood mythClimate changeAdaptive capacityEconomic JusticeEnvironmental justiceDistributive justiceEmpirical research
DOInot available

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.364
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.272
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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