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Vulnerability to climate change hazards and risks: crop and flood insurance

2006· article· en· W2169904855 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural risk and resilience
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsCrop insuranceClimate changeVulnerability (computing)Context (archaeology)Flood mythHazardEnvironmental resource managementNatural hazardRisk managementEnvironmental planningFlood insuranceBusinessGeographyNatural resource economicsEnvironmental scienceEconomicsAgricultureEcologyComputer scienceComputer securityMeteorology

Abstract

fetched live from OpenAlex

This paper reviews the widely used concepts of risk and vulnerability as they relate to climate and weather hazards, re‐conceptualizes these terms in the context of climate change and illustrates this development using crop and flood insurance as examples. Government subsidization of insurance against risks associated with adverse climatic conditions and weather events, such as flood damage and crop loss, may lead to individual decisions that actually increase the susceptibility of people, property and economic activities to those risks. The processes that give rise to this phenomenon are important in understanding the vulnerability of human populations to climate change. In many regions, existing conditions that give rise to flooding or crop failure are likely to be exacerbated by climate change over coming decades. In the climate change field, vulnerability has been conceptualised as a function of exposure to risk and as an ability to adapt to the effects. In this context, crop and flood insurance are possible adaptive measures. This treatment of vulnerability compares with similar concepts in insurance and risk management whereby events that cause loss are known as perils, and physical conditions, such as climate change, that increase the likelihood of a peril occurring, are known as physical hazards. Human behaviour that increases the exposure of individuals to potential perils is known as morale hazard or moral hazard, depending on the intentions of the person. Vulnerability consequently becomes a function of hazard and responses taken to reduce risk. Examples of crop and flood insurance programs from Canada, New Zealand and the U.S. are used to show how subsidized insurance might create a morale hazard in addition to physical hazards such as short‐term weather events and long‐term climate change, resulting in a higher level of vulnerability than would otherwise exist. These findings demonstrate that human behaviour affects the formation of both exposure and adaptive capacity in the context of vulnerability to climate change. Responses taken to increase adaptive capacity may in some cases be offset by individual behaviour that increases exposure.

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.155
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.001
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 head0.012
GPT teacher head0.203
Teacher spread0.191 · 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