Infrastructural adaptation : barriers and challenges municipalities encounter when responding to climate change
Bibliographic record
Abstract
Climate change has the potential to impact infrastructure in Metro Vancouver municipalities through the changing hydrological regime including increased frequency and severity of storm and rainfall events, as well as through sea level rise. Municipalities are at different stages of responding to climate change, and different challenges and barriers exist when adapting infrastructure to climate change. These challenges and barriers are as follows: Organizational and Regulatory These challenges include issues of jurisdiction in terms of what the municipality controls and what is outside of their control, and ensuring that different groups are able to work together and comprise different timelines, budgets, needs and goals. There could be a stronger level of coordination and leadership to better connect different municipalities in the region. Recommendation: Conduct partnership studies and projects with municipalities that have shared geographies (i.e. coastlines), infrastructure, or interests. This will better connect the region and create a more unified and strengthened response to climate change. Perception Some municipalities have identified perception barriers when responding to climate change. These occur based on the degree of public understanding and willingness to support adaptation initiatives, as well as the amount of political will for mayor and council. These are influenced by the perception of climate change as being in the distant future, as well as competing priorities for immediate action. Recommendation: Public outreach should occur around the benefits of pursuing infrastructural adaptations to climate change. Because few infrastructural changes have been implemented thus far as a direct response to climate change, less public outreach has occurred. However, this will influence public perceptions and the willingness of city council to pursue decisions around climate change adaptation. Economic One continual challenge when implementing infrastructural adaptations is the amount of funding available. This is both with regards to time and resources allocated towards conducting studies and developing strategies, as well as implementing these strategies through infrastructural changes. Recommendation: Conduct partnership studies and projects with multiple municipalities. This will facilitate cross-municipal collaboration as well as reduce the financial and resource pressures placed on one individual municipality. Informational Although data and information about climate change is available, this is not practical for establishing design criteria. This can lead to adjacent municipalities interpreting values differently, and responding in different ways. Additionally, a challenge exists when trying to adapt to climate change while considering seismic potential of the region. Recommendation: Work with municipalities to translate climate change data and studies into more coherent design criteria. Additionally, when establishing design criteria this should consider seismic potential, and how climate change will interact with that.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".