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Record W7015981977

Water Secure and Climate Resilient Ontario: Developing a Transdisciplinary Water Risk Management Framework and Decision Support Tool to Guide Multi-Sector Sustainable Water Management Policies and Strategies

2024· dissertation· en· W7015981977 on OpenAlexaboutno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2024
Typedissertation
Languageen
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsnot available
Fundersnot available
KeywordsWater securityRisk managementRisk perceptionWater supplyRisk assessmentWater industryWater resourcesIntegrated water resources management
DOInot available

Abstract

fetched live from OpenAlex

Sustainable management of water resources, which provide critical social, economic, cultural, and ecological functions, is essential for sustainable development, yet risks to water security are growing. The province of Ontario is an interesting case for investigating water risks, risk perception, and water risk management. Nestled between the Great Lakes, a “myth of water abundance” exists amidst a myriad of local water challenges, including the lack of safe drinking water in Indigenous communities, dwindling flows, groundwater overextraction, deteriorating water quality, regulatory complexity, and water-user conflicts. While academic interest in water risk assessment and sustainable water management is growing, the literature reveals limited interdisciplinary investigation of local water risks and how these risks are perceived, evaluated, and managed by influential non-state actors like the corporate and financial sector. Addressing these gaps, this dissertation focused on its phenomenon of interest of water security risks in Ontario. It executed a three-stage interconnected objective and examined water risk assessment, perception, evaluation, and management using a novel normative-analytical theoretical framework. 
\nThe first stage assessed interdisciplinary biophysical and social water risks at the sub-watershed scale in Ontario using secondary data analysis. It found high and moderate risk in at least 50% of studied sub-watersheds for all water risks, challenging the myth of water abundance. The second stage examined water risk perception and evaluation in the corporate and financial sector, using explanatory mixed methods (survey followed by interviews). It confirmed that risk-centric, individual-centric (cognitive, affective, socio-cultural demographic, trust-based), and spatial factors generate risk perception and impact water risk evaluation. Thus, revealing the nuanced model of expert risk perception. The third stage investigated water risk management strategies using a survey and interviews of corporate and financial practitioners. Moreover, using transdisciplinary approaches, it developed a contextually-attuned water risk decision support tool to guide multi-sector sustainable water management policies and strategies in Ontario. The results emphasize a combination of regulatory, voluntary, and multi-stakeholder participatory approaches, tailored based on the sector, location, and context, and risk severity, is necessary. Moreover, the criteria of flexibility, efficiency, strategic incentives, economic, and regulatory signals are essential. 
\nThis dissertation contributes to the knowledge in the fields of sustainability management, socio-hydrology, risk analysis, and water resources management. It is the first-of-a-kind comprehensive scholarship to address the wicked sustainability issue of water security using social-ecological perspectives and Risk Theory, a new theoretical arena, intersecting multiple disciplinary paradigms to empirically validate the normative-analytical theoretical framework for water. The interdisciplinary water risk assessment revealed a higher total water risk, highlighting the importance of including contextual variables. Revealing the impact of risk perception on water risk evaluation and management in the corporate and financial sector, the dissertation challenges the rational risk perception model of experts and practitioners, hence making a novel empirical contribution to risk analysis. Finally, the dissertation demonstrates the use of interdisciplinary data, transdisciplinary methods, and normative-analytical theoretical frameworks to investigate nuanced systems-based constructs like water risks, water risk perception, and develop decision support tools. Thus, advocating for widespread inclusion of interdisciplinary and transdisciplinary approaches in sustainability management research.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.795
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.271
Teacher spread0.260 · 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.

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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