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Record W2352024220 · doi:10.14288/1.0220525

Adaptive infrastructure regulation : designing for climate change

2015· article· en· W2352024220 on OpenAlexaboutno aff
Andrew Higgins

Bibliographic record

VenuecIRcle (University of British Columbia) · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeBusinessEnvironmental resource managementComputer scienceEnvironmental planningEnvironmental scienceGeologyOceanography

Abstract

fetched live from OpenAlex

Climate change represents a vexing challenge for infrastructure design. There is increasingly widespread acknowledgement that design practices need to change in order to ensure that structures built today can withstand changes in average climate conditions, growing climate variability, and more frequent and extreme weather events over the coming decades. Yet substantial uncertainty persists with respect to the specific future conditions that structures should be designed for, leading to regulatory paralysis: despite the need for urgent action, regulation continues to require that infrastructure design be based on the assumption that past climate will be representative of future climate. This thesis argues that, in the face of this bedevilling combination of urgency and uncertainty, government regulation will be required to generate the changes in design practices needed to ensure that structures designed today will be resilient and robust to the climate impacts they are likely to confront over their lifetimes. Using the example of the National Building Code of Canada, this thesis identifies several stress points in existing regulatory frameworks for infrastructure design. In particular, this thesis demonstrates that existing methods for dealing with uncertainty in infrastructure design regulation are likely to be overwhelmed by the deep uncertainties surrounding climate change, and that the poor adaptive capacity of existing frameworks renders them unable to keep pace with the increasingly rapid pace of change. Responding appropriately and proactively to these challenges demands a new regulatory paradigm. This regulatory paradigm should draw guidance from new governance theory in the legal scholarship, as well as a range of ‘adaptive’ approaches developed in other disciplines — adaptive management, adaptive governance, and adaptive policymaking. The core of a new, adaptive regulatory paradigm should be a structured, iterative regulatory process that is capable of responding quickly and appropriately to new knowledge and unfolding realities, and formal and informal, multi-level networks that foster learning, cooperation, collaboration, and innovation. Without such a paradigm shift, the existing regulatory paradigm will fall into crisis, rendering structures designed today vulnerable to failure in the face of tomorrow’s climate, and thereby compromising substantial infrastructure investments and increasing risks to public safety.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.983
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0050.025
Scholarly communication0.0090.008
Open science0.0030.005
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0050.001

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 head0.087
GPT teacher head0.202
Teacher spread0.115 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations1
Published2015
Admission routes1
Has abstractyes

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