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Record W1481112820

IMFG Graduate Student Papers: (1) Development Charges across Canada: An Underutilized Growth Management Tool? (2) Preparing for the Costs of Extreme Weather in Canadian Cities: Issues, Tools, Ideas

2012· preprint· en· W1481112820 on OpenAlexaboutno aff
Mia Baumeister, Cayley Burgess

Bibliographic record

VenueRePEc: Research Papers in Economics · 2012
Typepreprint
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsGrowth managementRedevelopmentLegislationFlexibility (engineering)BusinessSmart growthSustainable developmentLand useEnvironmental planningPolitical scienceEconomicsEngineeringGeographyManagement
DOInot available

Abstract

fetched live from OpenAlex

(1) Increasingly, compact and sustainable development has become a priority for Canadian municipalities. In order to realize these growth objectives, it is possible to look not only to conventional land use and growth management policies, but also to fiscal instruments to achieve planning goals. Existing literature suggests that development charges, which are financial tools used by municipalities in several Canadian provinces to pay for the growth-related capital costs associated with new development or redevelopment, can influence how land resources are consumed and developments are designed. Drawing on information from the literature and interviews with key informants, this research analyzed how development charges are used in British Columbia, Alberta, and Ontario, as well as the Halifax Regional Municipality, to understand how jurisdictions employ development charges and what role these charges currently play in achieving growth objectives. The research found that few municipalities use their development charges proactively to meet planning goals. Moreover, the research revealed a divide among practitioners, with some maintaining that development charges were a revenueraising tool and a poor mechanism by which to achieve planning objectives. Others recognized that development charges could be—and were being—used as a tool to encourage compact growth, but identified several barriers to more effective and widespread use as a planning tool. Suggested recommendations for policy changes include more flexibility within legislation to collect for transit and other services, ongoing support from provincial officials to assist municipalities in designing development charge programs with policy goals in mind, and further exploration of how fiscal tools can best be used as planning tools. (2) This paper reviews the risks to Canadian municipal finance from extreme weather and analyzes the financial tools that cities can use to prepare for extreme weather events: insurance, weather reserves, weather derivatives, and budget provision. Despite the threat of climate change, Canadian cities are not substantially increasing their use of these tools. However, improvements could be made to accounting procedures and disaster assistance regulations, and amalgamating smaller cities could improve their ability to manage risk, all of which will ameliorate the financial impacts of extreme weather. The paper proposes reasons why Canadian cities have failed to fully adapt their infrastructure to extreme weather: lack of information, low fiscal capacity, externalities, moral hazard in disaster assistance arrangements, and poor program design. It concludes by discussing how these arrangements may be overhauled to better prepare Canadian municipalities for extreme weather, including new provincial legislation and the creation of a federal infrastructure fund modelled on the United States’ Pre-Disaster Mitigation program.

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)0.003
metaresearch head score (Gemma)0.009
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.936
Threshold uncertainty score0.466

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.005
Science and technology studies0.0130.004
Scholarly communication0.0120.003
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0720.008

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 head0.107
GPT teacher head0.376
Teacher spread0.268 · 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
Published2012
Admission routes1
Has abstractyes

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